A Clinical Gait and Balance Analysis B Force Sensing Shoe Hardware Design C Infrared Sensing Floor Hardware

"And whatever you do, whether in word or deed, do it all in the name of the Lord Jesus, giving thanks to God the Father through him" (Colossians 3 :17)."For in him you have been enriched in every way, with all kinds of speech and with all knowledge" (1 Corinthians 1 :4-5). In the light of these verses from the Bible, I would like to raise a warm "thank you" to the Lord Jesus Christ for enlightening my way throughout this Ph.D thesis and for guiding my steps to reach my goal.

A Ph.D thesis is more than just the hard work accomplished by one person during a period of three years ; it is the synergy and the collaboration of a team work and experience sharing. This work wouldn't have been accomplished without the hard contribution of many people that I am grateful to have made acquaintance and worked with. I am taking this opportunity to express my gratitude to everyone who supported me throughout the course of this thesis. I am thankful for their guidance and invaluable constructive criticism. First and foremost, I would like to acknowledge with much appreciation my advisor and thesis director Prof. Amir HAJJAM EL HASSANI. You have been a tremendous support for me. A warm thank you for your confidence and for letting me accomplish a career in research. I would like to thank Prof. Emmanuel ANDRES for co-directing my thesis and honoring the jury by his presence. My thanks to my co-director and friend Dr. Rony DARAZI for his insightful comments. It was an honor to have worked with you.

I would like to thank the jury members, Prof. Bernard THIRION and Prof. Franc ¸ois CHAR-PILLET, who were kind enough to take notes of this research and to send me their objective comments. My gratitude goes also for Dr. Franck GECHTER, Prof. Benoît MACQ and Prof. Jacques D ÛCH ÊNE for their presence as examiners and for letting my defense be an enjoyable moment. Thank you for your brilliant comments and questions.

A special thank you for Mr. Elie NAKHLE, director of St. Antonios Elderly Care Home-Hadat and his team, for helping me in recruiting elderly people for my experiments. A big thank you to the students of the Faculty of Engineering at the Antonine University who did not hesitate in answering my requests for participating in the different phases of the experiments. I would also like to thank all of my friends and colleagues who supported me during this work of research, and incented me to strive towards my goal.

Finally, a warm than you to my family. Words cannot express how grateful I am to my mother for all of the sacrifices that she has made on my behalf. Your prayers for me were what sustained me this far.

GENERAL INTRODUCTION

1.1 CONTEXT OF THE WORK "Today, one does not age the same way" an article by Pierre-Joseph Ulysse and Fr éd éric Lesemann that indicates that aging is a natural, progressive, heterogeneous process that can affect all living species. Gerontology is the branch of science that studies all aspects of the aging process (morphological, pathophysiological, psychological, social and even environmental). At these levels, the concept of age usually measured by the number of years lived, known as "chronological age" is replaced by the concept of the "biological age" which is measured by the functional abilities of a person at a given age. This concept is closely correlated to the aging process that also depends on several factors such as the environment, food, education, maintenance of physiological and psychological faculties, etc. As a consequence, an elderly person is by definition anyone over a certain age that has the physical characteristics of aging [Couturier, 2005]. The World Health Organization (WHO) retains the age criterion of an elderly person of 65 or more [START_REF] Ageing | WHO global report on falls prevention in older age[END_REF]. The health status of an elderly person is usually the result of the effects of aging as well as additive effects of attained diseases. The usual or common aging which is characterized by functional deficiencies or an age-related physiological impairment of certain functions, could lead the elderly to a state of frailty or fragility. This can reduce his capacity of reaction to the environment or to certain pathologies and thus his autonomy and social integration in his Activities of Daily Living (ADL). This insidious aspect of frailty is often referred by "unstable incapacity" [Hazzard, 1990]. Frailty has major effects on most of the systems of a living organism such as the nervous system, the metabolic system, the sensory systems (vision, hearing, etc.), the cardiovascular system, the respiratory system and most particularly the musculoskeletal system in terms of locomotion and mobility [Morley, 2004]. The result is a general physical and functional weakening which will eventually increase the risk of incidents such as falls.

Falls are considered among the most hazardous incident that can strike a frail old person. They are critical if they happen but it some cases they are fatal. They have many consequences on different levels (person, environment, government). Today, the loss of independence caused by these accidents are being taken care by medical institutions.

However, one of the major issues that developed countries will be facing in the coming years, is the rise of the ageing population and the increase in the number of dependant elders. As a matter of fact, 20% of the world population will be over 65 by 2050[START_REF] Economic | World population ageing[END_REF]. In France, the National Institute for Statistics and Economic Studies (INSEE) forecasts a rise of 80% of people over the 60 in 2060. This demographic expansion lies foremost on societies and governments which are faced to deal with the inevitable ageing of their populations and its fallouts. Moreover, this growth of elderly population is faced with a decline in the number of natural care-givers and the low number of medical institutes that are incapable to accommodate this large number of people. But human interventions are not the only means that are able to manage this health problem, technical interventions are currently being developed to complement the human help by providing assistance to the elderly in the situation of incapacity. This duality of support to elderly people is accounted for "gerontechnology". As a result, gerontechnology is introduced as a new discipline dedicated to the use of new technologies in the ageing domain.

The framework of this discipline is to address the needs of elderly persons in terms of heath, security, mobility, etc. and in all aspects of their daily living taking into consideration their desire, ambitions, family conditions and habitude. The outcome of this discipline shall be measured in terms of gain of independence and improved quality of life. Thus, in the context of preventing frailty related accidents, such as falls, human and technological means could be deployed together to ensure the security of the elderly.

OBJECTIVES AND PROBLEM STATEMENT

On this basis, the main goal of this thesis is to answer the needs of the elderly in terms of security, mobility and independence during his/her Activities of Daily Living (ADL). In other words, this thesis seeks to contribute in maintaining the elderly in his environment.Since falls can happen at home or in an elderly care facility as well as outdoor, the object is to be able to propose a system capable of detecting and/or predicting a fall incident in these premises. In fact, most elderly people tend to stay at home or around their premises [Weber, 2006]. Fall-related technology can be contextualized according to two research tracks : fall detection and fall prevention. In this work of thesis, we are interested in the development of a non-intrusive system combining information from different types of sensors that are capable of elaborating the actimetric profile of fall-sensitive people for early detection of fall incidents. This amounts to extract and analyze spatial-temporal and kinetic gait parameters in order to determine the risk of falling and alert the person or his surrounding. In fact, as people age, they become more fragile and subject to many pathologies. Thus, they exhibit difficulties in maintaining their gait and balance. Their state of fragility may alter their gait and subsequently increase their vulnerability to fall incidents. The rapid management of a person who has fallen by means of both human and technical interventions limits the aggravation of the loss of autonomy and minimize the fall consequences (physical and psychological). Designing a generic system capable of answering the needs of the elderly in terms of mobility, security and independence is very challenging. For this, we have fixed some constraints that we will answer in our proposed system solution. The system needs to be feasible in terms of existing technologies. It must cover indoor and outdoor premises. The system should be as much as possible less intrusive to the person, easy to install and operate. The idea is to free the patient from the complexity of wearable devices while preserving his privacy and his security. The system must be very easy to use requiring minimum training for the user. The system should be able to extract the spatial-temporal and kinetic gait parameters such as the step length and time, the stride length and time, the angle of feet, the Center of Pressure (CoP), etc. These will compose the actimetric profile of the person under supervision. The system should be generic, in the sense that it could allow the implementation of algorithms to detect or to predict a fall or to analyze the behavior in real time with minimum cost in terms of installation and resources.

The opt for designing this system is originated from the fact that existing gait and balance analysis solutions are only found in hospitals inside a clinical setting or in a gait lab. They have usually a high cost, and they require high technicality to operate. Beside the fact that these systems provide objective measurement of gait and balance parameters, the assessment of these parameters at home is different when dealing with real-life scenarios. In order to export gait and balance analysis from hospitals into homes, we propose our Walk More Fall Less (WMFL v1.0) platform that will combine ambient sensors and footwear sensors. Such combination of sensors will enable us to ensure an early detection of falls complying thereby to the previously set requirements.

THESIS OUTLINE AND CONTRIBUTIONS

The overall organization of this these is outlined by describing each chapter as follows :

Part I of this thesis is composed of Chapters 2 and 3. In this part, we focus on the understanding of the problem of elderly falls and how this problem is tackled by the scientific community through our proposed generic classification of fall-related systems.

In Chapter 2, we provide a brief understanding on the problem of elderly falls, their magnitude, their causes and their consequences. We also highlight the importance of screening the risk factors and the interaction among them in the elaboration of an individual profile and the determination of the risk of falling. For this reason, we propose a classification of the risk factors according to their measurability. In the same context, we rise the need for merging the efforts between health-care professionals and scientists to find solutions that leverage the impact of this problem on governments and health-care facilities.

In Chapter 3, we focus on how the problem of falls is approached in the scientific literature. For this, we identify that existing solutions in this field fall in two main research groups : 1) Fall Detection (FD) and 2) Fall Prevention (FP). We also find that the literature reviews lack a common ground reference in terms of existing solutions. To answer this issue, we propose our generic classification of fall-related systems based on their sensors deployment. The proposed classification takes into consideration existing solutions in both FD and FP groups. We also try to unify as much as possible the taxonomy in both research groups. An extensive research scheme from FD to FP systems with their corresponding data processing techniques was also conducted based on this common ground classification. According to this classification, we were able to determine the main contribution of this thesis. In fact, one of the objectives of this thesis is the design and development of our Walk More Fall Less (WMFL v1.0) platform which belongs to the FP group and combines a Footwear (FW) system with an Ambient (AMB) system.

Part II of this thesis is composed of Chapters 4,5 and 6. In this part, we survey existing solutions on footwear systems as well as their applications. Then, we describe the design and development of our proposed Force Sensing Shoe. Finally, we propose our early fall detection approach.

In Chapter 4, we perform a background survey on existing footwear systems and their applications. The purpose of this chapter is to understand the design requirements of a footwear system relatively to the target application. A self-contained briefing of the surveyed papers was conducted, taking into consideration the system design, the extracted parameters, and the analysis methods.

Chapter 5 describes the design and development of our Force Sensing Shoe for gait analysis. We answer the need to continuously monitor the gait of an elderly and alert him in case of a risky situation. In this chapter, we focused on the hardware development of the instrumented insole with piezo-resistive force sensors and the wearable electronic board. We were able to optimize the number of force sensors used and extract the Center of Pressure (CoP) trajectory under the foot plantar surface during walking. The Force Sensing Shoe was tested to measure the accuracy of the weight of the person.

In Chapter 6, we propose our early fall detection approach. It answers the need for a real time and fast processing analysis to alert the person of a potential fall. In this chapter, we consider that a fall might be induced through several consecutive sway motions. For this, we monitor and analyze the amount of body sway in the Medial-Lateral (M-L) plane by computing the Center of Pressure (CoP) displacement at each foot plantar surface during the stance phase of the gait cycle. The alerts are generated by applying the spatiotemporal sliding window correlation in order to benefit form the history of recent stances. We validate our approach via a two-phase experimental protocol with 5 subjects. Finally, the risk of falling was also determined.

Part III of this thesis is composed of Chapters 7 and 8. In this part, we survey existing solutions on Ambient systems then we describe the design and development of our proposed Infrared Sensing Floor.

In Chapter 7, we perform a background survey on existing solutions regarding ambient systems used for gait analysis. These systems are more present in clinical settings and use different sensing technologies to extract gait parameters (e.g. Spatio-temporal, kinematic and kinetic parameters). Ambient systems found in elderly care facilities were also surveyed. A self-contained briefing on various state of the art papers was conducted, taking into consideration the system design including the sensing technologies and the extracted parameters.

In Chapter 8, we describe the design and development of our proposed Infrared Sensing Floor. In this chapter, we address the issues of ambient systems usually used for fall detection, location tracking and activity monitoring. For this, we propose our Infrared Sensing Floor that is capable of extracting the spatial-temporal gait parameters by simply detecting the footprint on the floor. The focus of this chapter is on the hardware development of the infrared-sensing walkway that use optical sensors to determine the footprint. We validate our system by extracting the data from these matrices were the footprint Active Contact Area (ACA) is determined. Once the footprint is identified on the floor, the rest of the spatial-temporal parameters were easily extracted.

Part IV of this thesis is composed of Chapters 9 and 10. In this part, we introduce our WMFL v1.0 Software Application and our proposed WMFL v1.0 platform.

In Chapter 9, we describe the design and development of the WMFL v1.0 Data Collection and Processing Software Application. In this chapter, we present the software functional architecture and the Graphical User Interface (GUI). The three modules of the Software Application with their functional block diagrams are described in details.

In Chapter 10, the culmination of the work described in Part II and III is discussed. In this chapter, we combine our Force Sensing Shoe and our Infrared Sensing Floor to form our WMFL v1.0 Platform. The architecture of the platform is described and answers the need of a non-intrusive gait analysis system in homes. By combining these two systems, we are able to provide almost all gait and balance parameters similarly to clinical systems. We validated the reliability of our proposed platform using the test-retest repeatability measures and we identified the gait parameters that differentiate between the young and the elderly actimetric profiles.

Finally Chapter 11 concludes the thesis. In this chapter, we draw the major conclusions obtained in various chapters with a flash back on the contributions. We also elaborate the perspectives that will be addressed in the continuity of this work.

-Overview on Existing

Footwear Systems

2

UNDERSTANDING ELDERLY FALLS

INTRODUCTION

The problem of falls is becoming more and more prominent as the worldwide elderly population is increasing. Understanding fall incidents, their causes and their consequences is essential in the elaboration of a fall management scheme. From the medical perspective, understanding falls will help health-care practitioners recognize risky situations and adopt the correct strategy in order to prevent these incidents from reoccurring. From another point of view, learning the multifactorial aspect of falls would also serve medical technologists such as scientists, researchers and engineers, develop systems and devices to better detect a fall or even to predict a future fall. The main goal remains in providing the elderly with more independence and an improved quality of life.

In this context, the purpose of this chapter is to provide a basic understanding on elderly falls, why they happen and what are their consequences on the person himself as well as on his environment.

DEFINITION AND MAGNITUDE

Falls are defined by Kellog International working group as "inadvertently coming to rest on the ground, floor or other lower level, excluding intentional change in position to rest in furniture, wall or other objects". This definition has been adopted by many research studies for many years. It remains general and the room is open for scientists and researchers to refine the terms with respect to their study aspect. For instance, a health-care practitioner would relate the cause of falling to the loss of balance or unsteady gait. Therefore, the operational definition of a fall may explicit inclusion and exclusion criterion [START_REF] Ageing | WHO global report on falls prevention in older age[END_REF].

According to the World Health Organization (WHO), the magnitude of falls is increasing worldwide. In fact, approximately 28-35% of people aged of 65 and over fall each year, increasing to 32-42% for those over 70 years of age [START_REF] Ageing | WHO global report on falls prevention in older age[END_REF]. These numbers are likely to grow with age, frailty level, pathological history and living conditions 9 (e.g. falls happen more often in nursing homes or in long term institutions). The frequency of falls is also affected by the growing number of elderly. It is estimated that by the year of 2050 one or more in each group of five people will be aged 65 years or above [START_REF] El-Bendary | Fall detection and prevention for the elderly : A review of trends and challenges[END_REF]. This demographic change will considerably affect worldwide societies and will subsequently lead to a high rate of fall incidents and fall related injuries.

For example, falls lead to 20-30% of mild to severe injuries and are the cause of 10-15% of all emergency visits [START_REF] Ageing | WHO global report on falls prevention in older age[END_REF]. Thus, this increasing weight of demographic change levies new challenges on socio-economic levels if appropriate measures are not taken to tolerate the impact of this inevitable ageing and its associated fallouts.

CAUSES OF A FALL

The causes of falls are often the result of complex interactions among many factors. [START_REF] Skelton | Exercise for falls management : Rationale for an exercise programme aimed at reducing postural instability[END_REF] have identified more than 400 factors that contribute to a fall. [START_REF] Campbell | Risk factors for falls in a community-based prospective study of people 70 years and older[END_REF], said that elderly falls result from the effect of additive factors. Thus, screening the factors may help reduce the risk of falling. In fact, the risk of falling and the number of factors are linearly correlated [START_REF] Tinetti | Risk factors for falls among elderly persons living in the community[END_REF].

The scientific literature often classifies the risk factors in three dimensions. There are factors related to the behavior, other factors are connected to the health status of the elderly and finally factors linked to the environment (extrinsic factors). The first two dimensions can be merged together. They score the factors associated to the person (intrinsic factors). Falls happen because of the interaction among these dimensions. The more the link between these factors is identified, the more medical professionals are able to reduce the risk of falling. Figure 2.1 shows the three-dimensional classification of the risk factors and the interaction among them.

For the rest of the section, we will consider the two-dimensional classification of the risk factors. With this objective in mind, we define the factors related to the person and those related to the environment.

FACTORS RELATED TO THE PERSON

Ageing is not successful all the time. It is accompanied with diseases and troubles that vary in acuities among different people. Age-associated troubles and pathologies can reduce the elderly's capacity of reaction with his surroundings. Factors such as those related to troubles in gait and balance may be linked to difficulties in locomotion (e.g. musculoskeletal frailty), lack of sensory inputs (e.g. vision, vestibular information) and deficiencies in the central nervous systems (e.g. attention, coordination, speed of reaction). Other factors are more correlated to chronic or acute pathologies that may affect the aforementioned faculties. Musculo-Skeletal Diseases (MSD) such as osteoarthritis, neuro-degenerative diseases such as Parkinson or Alzheimer or other forms or sensorial troubles (e.g. dementia, cataract, depression) are all contributors in the increase of the risk of falling. Hypotension, dehydration and urinary infection are acute pathologies that also raise the risk of elderly falls [START_REF] Perry | Survey and evaluation of real-time fall detection approaches[END_REF].

Whether ageing finishes successfully or with pathologies, the majority of falls happen during Activities of Daily Living (ADL) (e.g. chair rise, stair walk, getting up from bed, etc.). The behavior in every day's activities has also impacted the risk of falling. Controversially, there is no cutting evidence on the impact of daily activities on the risk of falling. In fact, factors related to daily activities are uncontrollable and complex to quantify. Incorrect behaviors must be identified and treated accordingly. For example, elders with low vision must have their eyeglasses checked regularly. Moreover, elders with hypertension must sit for few seconds after getting up from bed in the morning. Risky behaviors that may alter gait must also be avoided (e.g. abusive alcohol consumption, inappropriate shoes, etc.). Medication intake is also a prominent factor associated with falls. The risk increases with the number of drug intakes (more than four medicines) as well as with the type of medicine involved such as antiarrhythmic drugs. Various drug intakes may provoke unwanted health complications and increase the risk of falling. The psychological impact plays also a big role in increasing the risk of falling. Although the significance of falling may vary between elders, the fear is always present and may inhibit daily activities and increase the dependency. According to [START_REF] Zijlstra | Prevalence and correlates of fear of falling, and associated avoidance of activity in the general population of communityliving older people[END_REF], 54% of older adults above the age of 70 express a fear from falling.

FACTORS RELATED TO THE ENVIRONMENT

Personal factors are not only the reason why elderly fall. There are other reasons associated to the environment (extrinsic factors) that contribute to increase the risk and the potential of a fall incident. These factors are originated from the person's surrounding.

Studies have shown that between one third or two third of elderly falls happen indoor [START_REF] Speechley | Falls and injuries in frail and vigorous community elderly persons[END_REF], often at home or in an elderly care facility. The link between falls and environmental risk factors are not yet clearly defined due to the variability of the situation. Nonetheless, certain indoor home characteristics (e.g. insufficient lighting, loose carpets, unsteady ground, wet or slippery floors, presence of obstacles, poorly constructed steps, etc.) were identified by researchers, may influence the frequency and the gravity of a fall incident. Although falls are more likely to happen in local settings, outdoor falls can also occur.

As a result of the above, the risk of falling gathers both intrinsic and extrinsic factors. It is also noticeable that these factors are interconnected together and vary between different people. Due to the complexity of the problem in directly identifying the risk factors and their interactions, it is important to have an individual profile when approaching an elderly person with a high risk of falling or who underwent a fall incident. Conferring to this profile, care givers and socio-medical professionals must elaborate a prevention plan to decrease the risk of a potential fall.

DETERMINING THE RISK OF FALLING

The establishing of an individual profile is not an easy task. However, institutions such as the American Geriatrics Society (AGS) and the National Institute for Clinical Excellence (NICE) provide a simple clinical pathway algorithm that summarizes the assessment of falls and program interventions. The algorithm provides screening for adults over the age of 65 for recent falls and/or poor gait balance. The study realized under the direction of H él ène Bourdessol and St éphanie Pin [Bourdessol, 2005] on fall prevention at homes, provides a decision tree based on the timed-up & go test and the history of falls. The result of the test and/or a recurrent fall would sort patients as high, moderate or no risk. Further assessment is recommended for people with moderate and high risk.

In order to estimate the risk of falling, data regarding the risk factors must be well documented and quantified. Measurable screening of risk factors must be conducted using adequate testing tools or questionnaires. In their study article, [START_REF] El-Bendary | Fall detection and prevention for the elderly : A review of trends and challenges[END_REF] divide the risk factors causing falls according to their origin and controllability. The review on challenges for new technologies for fall prevention at home written by [Kenny et al., 2010a] categorizes the falling risks according to their objective mechanism or whether they can be modifiable or not. According to these two studies, risk factors can be modifiable and controllable. However, in order to estimate the risk of falling, we propose a more beneficial classification of fall factors based on their measurability. As a result, the involvement of these factors would lead to estimate the global risk of falling and help socio-medical professionals elaborate the appropriate prevention plan. The common fall factors are classified whether they are measurable or not in

NEED FOR HELP

For all the above reasons, the need for help arises. As a matter of fact, it is estimated that the number of injuries caused by falls incidents is projected to a 100% higher in 2030 [START_REF] Igual | Challenges, issues and trends in fall detection systems[END_REF]. The burden of this health problem lies foremost on the shoulder of societies and governments. It is also the role of the scientific communities to find solutions that are facing societies. From a scientific point of view, the problem of falling is approached by two research tracks. The first track is Fall Detection (FD) that has been aggressively investigated by researchers, scientists and industrialists through the past two decades. The main focus is to reduce the response and rescue time to reach the victim after a fall. In fact, lying on the floor for more than one hour is associated with many diseases (e.g. dehydration, pneumonia, hypothermia) and eventual death within 6 months [START_REF] Lord | Falls in older people : risk factors and strategies for prevention[END_REF]. The second track is Fall Prevention (FP). The latter gathers the efforts made to detect the possibility of a fall. Research in this track is still in its early phases.

The focus is mainly on predictive analysis of gait and balance parameters.

CONCLUSION

Ageing is inevitable, whether it ends with no, mild or severe pathologies, it is always decent to age with good quality of life. Ageing is accompanied with many risks among which the risk of falling is becoming prominent and its magnitude is increasing with the increasing number of elderly population. Understanding why people fall is definitely the starting point to build a good management strategy to prevent these incidents from happening. In this chapter, we focused our understanding on falls, their intrinsic and extrinsic causes and their consequences. We also learned that determining the risk of falling must be performed by measurable screening of the risk factors in the purpose to establish the individual profile of the patient. Facing the extent of this problem, health-care practitioners, researchers and scientists have joint the efforts to tackle the problem of elderly falling with regarding to two research tracks : Fall Detection and Fall Prevention. In the following chapter, we will review the major development in these two tracks.

FALL DETECTION AND FALL PREDICTION SYSTEMS

INTRODUCTION

Falls are a major health problem for the frail community dwelling old people. For more than two decades, falls have been extensively investigated by medical institutions to mitigate their impact (e.g. lack of independence, fear of falling, etc.) and minimize their consequences (e.g. cost of hospitalization, etc.). However, the problem of elderly falling does not only concern health-care professionals but has also drawn the interest of the scientific community. In fact, falls have been the object of many research studies and the purpose of many commercial products from academia and industry. These studies have tackled the problem using fall detection approaches exhausting a variety of sensing methods.

Lately, researcher has shifted their efforts to fall prevention where falls might be spotted before they even happen. Despite their restriction to clinical studies, early-fall prediction systems have started to emerge. At the same time, current reviews in this field lack a common ground classification. In this context, the main contribution of this chapter is to propose a generic classification of fall-related systems based on their sensor deployment. An extensive research scheme from fall detection to fall prevention systems have been also conducted based on this common ground classification. Data processing techniques in both Fall Detection and Fall Prevention tracks are also highlighted. The objective of this work is to deliver medical technologists in the field of public health a good position regarding fall-related systems.

FALL DETECTION SYSTEMS

One would give a definition to a fall detection system (also known as a fall alert system) according to its purpose. For example, [START_REF] Igual | Challenges, issues and trends in fall detection systems[END_REF] define a fall detection system as an assistive device whose main objective is to alert if a fall event has occurred.

[ [START_REF] El-Bendary | Fall detection and prevention for the elderly : A review of trends and challenges[END_REF] define fall detection according to their functional components (sensing, processing and communicating). We believe that a global definition is required. Thus, we propose our definition of a fall detection system as :

"An assistive device that is capable of sensing, processing and communicating alarm data in the event of a fall under real-life conditions effectively".

Our intention in this definition is to emphasize not only the structure and the purpose but also the practical use of a fall detection system. In other words, a fall detector is effectively fit into an application if it is put into test in real-life scenarios. Besides the aspects that a fall detection system has in mitigating the consequences of falls such as reducing the response time, it must prove reliability and confidence to be adopted. These are accounted for the number of false positives and false negatives generated by the device. Referring to the problem of falling in chapter 2, the consequences can never be negligible. If a person falls and the device does not detect it, he/she remains prone on the ground. Essentially, the main goal of a fall detection system is to discriminate between fall events and Activities of Daily Living (ADL).

FALL PREVENTION SYSTEMS

Fall detection systems have helped reduce the consequences of falls (long lie, fear of falling) ; however, they didn't stop them from happening. Thus, the problem of falling can be rather tackled using prevention systems (also known as fall prediction systems). In this track, the problem of falling become much more complex. It amounts to identify and assess the risk factors contributing to a fall incident. Conventionally, this is usually done by care-givers through questionnaires, diaries or phone calls ; but the data collected is sometimes incomplete and not always reliable [START_REF] Delahoz | Survey on fall detection and fall prevention using wearable and external sensors[END_REF].

As was previously mentioned in chapter 2, fall risk factors are either related to the person or to the environment. The latter can be easily evaluated through simple observation, whereas the formers are complex and require more examination. Person-related fall risk factors assessment consists of extracting and analyzing human kinetic and kinematic parameters of gait (e.g. stride and step length, time, Center of Pressure (CoP), etc.) and balance (e.g. static and dynamic sway, Ground Reaction Forces (GRF), etc.). According to [Kenny et al., 2010a], neuro-cardiovascular assessment is also required.

Gait and balance parameters are assessed using either semi-subjective or objective assessment tools described below :

-Semi-subjective assessment tools : They are usually conducted in clinical settings under the supervision of a specialist. Clinicians often use functional performance traditional scales (e.g. Berg Balance, Performance-Oriented balance and Mobility Assessment (POMA), etc.) to evaluate gait and balance. The patient is asked to walk or perform a series of tasks which require static or dynamic anticipatory postural controls. Results are scored in a semi-subjective way. Table A.1 in Appendix A gives a list of traditional scales used for gait and balance assessment for both healthy and disabled elders. Detailed description on methods of assessing balance can be found in [START_REF] Browne | Review of the different methods for assessing standing balance[END_REF].

-Objective assessment tools : Objective assessment tools with advanced sensor technologies provide a large amount of reliable information on patients' gait and balance. The majority of these tools are deployed in a gait laboratory or in hospitals and require specific technical skills to run them. Moreover, the cost of such tools is high to be adopted for home use. Given the fact that elders prefer to stay in their own home comfort [START_REF] Mcgrath | Taking balance measurement out of the laboratory and into the home : discriminatory capability of novel centre of pressure measurement in fallers and nonfallers[END_REF] ; efforts must be put to export gait and balance assessment into homes. Recent efforts in Micro Electro-Mechanical Systems (MEMS) technology have given rise to methods deploying wearable body sensors to evaluate human gait and balance during daily life activities where falls are more likely to happen. In this outlook, the design of a fall prevention system must be able to screen the fall risk factors through objective measurable parameters. Table A.2 of Appendix A, provides a list of available assessment technologies for gait and balance assessment.

CLASSIFICATION REVIEWS ON FD AND FP SYSTEMS

The literature reviews on fall-related systems lack a common ground classification. In the track of fall detection systems, we analyzed four review papers. Each study provided a different classification approach based on the understanding of the problem of falling and the anticipated contribution. [START_REF] Noury | A proposal for the classification and evaluation of fall detectors[END_REF] and [START_REF] Noury | Fall detection-principles and methods[END_REF] classify fall detection studies according to the detection techniques of the shock impact or the post-fall phase. [START_REF] Mubashir | A survey on fall detection : Principles and approaches[END_REF] used a three-category approach based on the physical deployment of the sensors. They classified the systems as wearable based devices, ambient based and camera based. They also provided a brief summary on fall detection studies in each category. [START_REF] Perry | Survey and evaluation of real-time fall detection approaches[END_REF] considered a survey and evaluation of real time accelerometry based fall detection techniques. Their classification was therefore based on methods that measure acceleration, methods that combine acceleration with other sensor data and methods that do not measure acceleration at all. It can be noticed that Pierry et al. were very specific in their classification which was based on accelerometers. This type of classification doesn't provide a common reference for the variety of sensors that can be deployed on wearable systems. [START_REF] Igual | Challenges, issues and trends in fall detection systems[END_REF] provided a general classification. They divided fall detection systems into wearable devices and context-aware systems.

The latter considered mostly of image processing and computer vision techniques using camera based devices and ambient sensors.

At the time when those papers were published, the fusion systems had not significantly appeared and consequently they were considered a category in our proposed classification. For instance, none of the previously mentioned reviews considered solution where wearable and ambient sensors are combined together to detect falls. These are known as fusion or hybrid systems. Recently, a survey on challenges and issues in multisensor fusion approach for fall detection systems provided a three-category classification [START_REF] Koshmak | Challenges and issues in multisensor fusion approach for fall detection : Review paper[END_REF]. The authors based their classification on wearable, ambient and vision sensors much like [START_REF] Mubashir | A survey on fall detection : Principles and approaches[END_REF]. In their article, the fusion concept is only considered within the same category. For instance, they considered fusion of accelerometers and gyroscopes while both of these sensors are wearable. The emphasis on fusion systems highlighted by our proposed classification combine different sensor technologies.

In the track of Fall Prevention, more data need to be collected and analyzed from different sensors. In this context, data fusion from multi-sensor technologies that use wearable based and ambient based systems must be achieved in order to provide a global actimetric profile for fall prevention. In the literature, three articles were investigated. The most relevant classification was found in [Muro-de-la Herran et al., 2014]. The review paper provides an overview of wearable and non-wearable fall prevention systems used for gait analysis ; however, the focus was on clinical applications only. The other two reviews were more specific. For instance, [START_REF] Tao | Gait analysis using wearable sensors[END_REF] provided a review on gait analysis using wearable systems highlighting their applications in sports, rehabilitation and clinical diagnosis. They also conducted a brief research on the types of sensors used in these systems and their working principles. [Abdul Razak et al., 2012] reviewed foot plantar pressure measurement systems highlighting the types of sensors involved and their current applications particularly in sports and rehabilitation.

Large reviews on both fall detection and fall prevention were also found.

[ [START_REF] El-Bendary | Fall detection and prevention for the elderly : A review of trends and challenges[END_REF] provided a review of trends and challenges in fall detection and fall prevention systems. The paper offers exhaustive narrative information regarding both systems without any obvious classification. Another recognized survey by [START_REF] Delahoz | Survey on fall detection and fall prevention using wearable and external sensors[END_REF] is much more explicit. Their article classifies fall detection and fall prevention systems based on two categories : wearable systems and external sensors systems. The paper provides a qualitative evaluation based on different criterions (e.g. type of sensor, falling factor, cost, etc.). In their classification, vision systems are considered as external systems controversially to [START_REF] Mubashir | A survey on fall detection : Principles and approaches[END_REF]. Other related surveys such as [START_REF] Habib | Smartphone-based solutions for fall detection and prevention : challenges and open issues[END_REF], focus on fall detection and fall prevention systems that use the smart phone as a sensing device.

PROPOSED GLOBAL CLASSIFICATION SCHEME

Based on the previous classification issues, we propose our global common classification scheme for better understandability of fall-related systems. The goal of this classification is to provide researchers and scientists a global reference scheme on existing fall-related systems. According to this scheme, emerging new systems can be easily identified and classified. The proposed scheme is a three-category based classification shown in Figure 3.1. The entities drawn in this figure and the connection between them, are based on literature studies. Table 3.1 shows the existing paths related to fall detection and fall prevention solutions as illustrated in Figure 3.1.

In this scheme, fall-related systems are divided in two big groups : Fall Detection (FD) systems and Fall Prevention (FP) systems. The first group uses the fall impact to trigger an alarm, whereas the second group extracts parameters of gait and balance. Due to the large amount of studies and to the lack of a global classification, Fall Detection and Fall Prevention systems can both be divided with respect to their sensor-technology deployment according to three categories :

-Wearable based Systems (WS).

-Non-Wearable based Systems (NWS).

-Fusion or hybrid based Systems (FS).

Controversially to [START_REF] Mubashir | A survey on fall detection : Principles and approaches[END_REF] and to [START_REF] Igual | Challenges, issues and trends in fall detection systems[END_REF], camera systems are a sub-category of the Non-Wearable Systems much like [START_REF] Delahoz | Survey on fall detection and fall prevention using wearable and external sensors[END_REF] who named this category as external sensors. Furthermore, due to the emerging fusion solutions, Fusion based Systems category is also added to this classification. At this level of the scheme, both groups have the same classification, however, when screening existing solutions and studies, FD and FP systems exhibit major differences. In fact, the deployment of sensors in Wearable Systems is typically attached to the body. These can be named as Body Wear (BW). For Fall Prevention systems, sensors can be found on the body or inside the sole of a shoe. These can be named as Foot Wear (FW). Another difference is the type and number of sensors used in Wearable Fall Prevention Systems. In fact, motion data must be collected from body segments and joints to study the human gait. Vital signs monitors are also added to this category for their relevance in providing intrinsic data to estimate the risk factor.

The Non-Wearable Systems category is the same for both groups with a minor difference in Fall Prevention. Sensors in this category are either Ambient (AMB), Vision (VIS) or Radio Frequency (RF). The reason for taking out the Vision and the Radio systems from the Ambient sub-category is the size of the data and the level of processing that can be achieved within these systems. Moreover, the type and size of the sensors used in vision and radio devices are different from those used in ambient sensing. These subcategories can be baptized by the name context-aware systems due to their ability to sense and process data from the environment. Data collected from these systems are more dependent on the main context (FD or FP). For instance, ambient sensors are more variant in Fall Prevention systems to provide behavior analysis and activity monitoring while Radio Frequency systems are much adequate for gait analysis only.

Fall Detection and Fall Prevention systems can benefit from the fusion of wearable and non-wearable sensors to provide more accurate analysis in terms of detection and prevention. Fusion Systems (FS) are set in both categories. In this case, data is collected from different sensors for analysis. This category is poorly investigated in the literature of Fall Prevention systems ; However, this option must be considered when taking fall prevention out of the laboratory to elderly homes. [START_REF] Kalgaonkar | Acoustic doppler sonar for gait recogination[END_REF] 14 FP → FS → (AMB + SP) [START_REF] Huang | Pervasive, secure access to a hierarchical sensor-based healthcare monitoring architecture in wireless heterogeneous networks[END_REF]] 15 FP → FS → (BW + AMB) [START_REF] Suryadevara | Intelligent sensing systems for measuring wellness indices of the daily activities for the elderly[END_REF] 16 FP → FS → (BW + SP) [START_REF] Allen | Telemedicine assessment of fall risk using wireless sensors[END_REF]] 17 FP → FS → (FW + SP) [START_REF] Majumder | Smartprediction : A real-time smartphone-based fall risk prediction and prevention system[END_REF] Due to the emerging smartphone sensing technologies, we have integrated in our proposed classification the Smartphone (SP) category. This category is represented as a floating entity, because smartphone systems can be located at any level of the classification. They can be considered as wearable or non-wearable devices that are capable of sensing, processing and communicating. Existing smartphone systems are either standalone or combined with other wearable or non-wearable sensors for both Fall Detection and Fall Prevention systems. According to [START_REF] Habib | Smartphone-based solutions for fall detection and prevention : challenges and open issues[END_REF], Smartphone based systems are not yet been deployed as stand-alone systems in a non-wearable context.

SENSORS DEPLOYMENT IN FD AND FP SYSTEMS

Based on the proposed global classification in Figure 3.1, the sensor type and number are mainly dependent on the context of the application. These sensors are deployed in wearable, non-wearable and fusion systems.

WEARABLE SYSTEMS SENSORS

In the context of Fall Detection, sudden changes in body motion parameters such as acceleration, orientation or inclination may be interpreted as fall-like incidents in case of elders. To measure these parameters, sensors must be tied to the body. Wearable based Systems widely use inertial sensors such as accelerometers, gyroscope, inclinometers or other types of sensors like barometers and magnetometers, to detect abrupt surges in human gait, assess balance and monitor displacement [START_REF] Culhane | Accelerometers in rehabilitation medicine for older adults[END_REF]. Practically, accelerometers and gyroscopes are MEMS devices that can measure acceleration and orientation (e.g. pitch, roll, yaw or angular momentum) of a body part along a certain axis. These sensors are basically utilized when identifying different types of falls : fall from sleeping, fall from sitting, fall from walking or standing, and fall from standing on support tools such as a ladder or stairs [START_REF] Delahoz | Survey on fall detection and fall prevention using wearable and external sensors[END_REF]. Most often, 2-axial or 3axial accelerometers and gyroscopes are found in almost all studies.

In the context of Fall Prevention, wearable sensors are used to analyze human gait. Compared to the types of sensors used for fall detection, Fall Prevention systems use a wider range of motion sensors. These sensors can be placed on different body parts in order to measure the characteristics of the human gait (e.g. feet, knee, hips, waist, chest, etc.) [START_REF] Tao | Gait analysis using wearable sensors[END_REF]. These systems use in addition to inertial sensors, extenso-meters, force sensors and goniometers. According to our proposed classification in Figure 3.1, wearable systems sensors can be deployed on body and foot wears. The list of sensors used in wearable FD and FP systems are listed in Table 3.2. These listed sensors can be directly linked to human locomotion as they provide input to extract gait characteristics and detect abnormal changes in behavior depicting a potential fall. But, changes in behavior can also originate from intrinsic factors related to the pathophysiological history of the patient (e.g. muscle fragility, diabetes, heart diseases, hypertension, etc.). These factors must also be monitored as they are highly correlated with falls [Kenny et al., 2010a]. In this context, recent advances in MEMS technology have led to the development of new types of non-invasive health-care sensors to monitor health signals. For example, blood glucose, blood pressure and cardiac activity can be measured using techniques such as infrared sensing, optical sensing and oscillometry [START_REF] Rashidi | A survey on ambientassisted living tools for older adults[END_REF]. These sensors are listed in Table 3.3. Moreover, new technologies in sensing fabric also known as e-textile are currently being deployed on garment, in fabric and ultimately in fiber [START_REF] Mazzoldi | Smart textiles for wearable motion capture systems[END_REF]. This technology is more flexible and comfortable compared with other wearable sensors in measuring human gait and posture [START_REF] Scilingo | Strain-sensing fabrics for wearable kinaesthetic-like systems[END_REF]]. In Non-Wearable Fall Detection Systems, sensors are deployed in the environment. The main effectiveness of this arrangement is when obtrusiveness is rejected from the subject as he refuses to wear any device on his/her body. The purpose of such systems is to be able to track human daily activities and to detect abrupt changes in motion that might trigger a fall event. Despite their advantage of being none-intrusive, the detection area of these solutions is limited to the range covered by the sensor [START_REF] Rougier | Robust video surveillance for fall detection based on human shape deformation[END_REF], the reason why their usability is restricted to indoor premises only. [START_REF] Koshmak | Challenges and issues in multisensor fusion approach for fall detection : Review paper[END_REF], this novel approach can potentially offer a significant improvement in reliability and specificity of fall-related systems. A multisensory fusion approach is more likely to answer the needs of independent living for elderly people [START_REF] Demongeot | Multi-sensors acquisition, data fusion, knowledge mining and alarm triggering in health smart homes for elderly people[END_REF]. The opt for a fusion based approach is the flexibility of adjusting the system for a wider context (e.g. wider sensor network, implementation of fall detection and fall prevention strategies, design of a patient-based monitoring process, etc.).

Fusion systems are currently being exploited in both Fall Detection and Fall Prevention tracks. In Fall Detection, these systems can extract data related to posture, inactivity, presence, body shape, head change etc. In Fall Prevention, these systems are able to provide a rich source of data on human gait and balance characteristics to estimate the falling risk. Fusion Systems are solely deployed in gait assessment labs. Efforts to export these technologies into homes are currently being made. These are known as "Abient Assisted Living". Despite the advantages that Fusion Systems may offer to the falling problem, some challenges need to be overcome (e.g. integration into smart homes, real time analysis, computational power, processing scheme, cost, etc.). The development of a new FD or FP system must take into consideration the problem of falling in real-life situations where the reliability and the confidence of the systems are evaluated. Future solutions in these two tracks must answer many constraints such as the coverage, the level of intrusiveness, the cost, the facility of operation, etc. This can be realized by merging different sensing technologies. In fact, using our proposed generic classification scheme in Section 3.5, researchers and medical technologists tackling the problem of elderly falling, can draw new fusion-based fall-related solutions. In this context, we propose our WMFL v1.0 Platform which will make the main object of this thesis.

WMFL is the acronym for "Walk More Fall Less". The choice of the name is inspired from the fact that elderly lose confidence after a fall incident and they develop fear of falling.

The name restores a certain level of confidence and provides security if a person or an elderly care facility opts for the choice of our solution to be installed. In our proposed solution, we were able to combine different types of sensing technologies to provide a wide coverage with less intrusiveness to the elderly. With reference to our proposed generic classification in Section 3.5, WMFL v1.0 is a hardware and software platform that combines Footwear (FW) and Ambient (AMB) sensors in a particular architecture mainly composed of two systems : The footwear system and the ambient system. These two systems are controlled by a Smartphone device to select the mode of deployment (indoor or outdoor). The design and development of these two systems composing our WMFL v1.0 platform are discussed in Chapters 5 and 8 respectively. The architecture of the WMFL v1.0 Platform is introduced in Chapter 10. The current version of the platform requires a Software Application that runs on a computer for data collection and visualization. The software functional architecture is described in Chapter 9. WMFL v1.0 is originally designed to be deployed in elderly care facilities or at homes, but it can also be used in a clinical setting.

CONCLUSION

Research in both Fall Detection and Fall Prediction tracks is still an ongoing process. The joint efforts of health-care professionals and scientists has led to the development of many solutions. But, the multitude and the diversity of these systems lack a common ground classification. In this chapter, we have proposed a four-level common ground classification scheme where fall related-systems (FD and FP) were categorized into Wearable, Non-Wearable and Fusion Systems according to their sensor deployment. The proposed scheme provides a global overlook over the systematic studies related to elderly falling which hasn't been seen in previous reviews of the same type. The scheme has also emphasized the Fusion and Smartphone Systems. The rest of the chapter is a summary on Fall Detection systems with respect to their sensor deployment and their data processing techniques. Fall Prevention systems were also surveyed ; however more details were accorded to existing solutions given the current efforts in this research track. Future directions in Fall Prevention may consider exporting gait and balance assessment tools into homes while providing reliable and low cost solutions for the elderly. This was concretized by the design of our proposed WMFL v1.0 platform which will make the object of the of the subsequent chapters of parts II, III and IV of this thesis.

4

BACKGROUND ON FOOTWEAR SYSTEMS AND METHODS

INTRODUCTION

As the world population is ageing, health-care facilities alone are becoming short to answer the needs of elderly people in terms of independence and quality of life. In the context of elderly falling, the consequences could be severe and sometimes fatal particularly if the person falls and no one is around for immediate rescue.

The shortage in health-care practitioners can be complemented with solutions that are particularly engineered to leverage the burden of these incidents. According to our generic classification introduced in Chapter 3 Section 3.5, these solutions are divided in two groups : 1) Fall Detection (FD) systems and 2) Fall Prevention (FP) systems. The first are able to detect a fall when it happens whereas the second can predict if a fall is about to happen. These solutions rely on sensors that are deployed on the body or in the environment. Opposite to FD systems that rely on the level of impact and the reaction time to detect a fall event, FP systems rather extract gait and balance parameters for analysis to determine if a fall is likely to happen. Among the latter group, footwear systems are extensively researched in the literature. They allow the extraction of gait and balance characteristics for various applications (e.g. sports, gait and balance analysis, rehabilitation). Much like any other wearable system, footwear systems also use methods and algorithms capable of detecting the abnormality of human gait and balance and estimate the risk of falling in order to alert the elderly from a potential fall. With reference to Chapter 3, we divided these methods into two categories : Analytical Methods (ANM) and Machine Learning Methods (MLM). In this chapter, we provide a summary of many researches conducted within the same application domain and motivation.

4.2 RELATED WORK [START_REF] Musselman | Development of an in-shoe sensor insole for motion analysis, physical therapy, and rehabilitation[END_REF] stressed on the problem of elderly falls and their subsequent impact on Activities of Daily Living and thereby on gait. The main goal of their research is to develop new methods for gait and balance assessment to prevent future falls. They proposed an in-shoe system composed of two parts. The first part being the Inertial Measurement Unit (IMU) that utilizes a gyroscope and an accelerometer to analyze the motion of the foot, and the second part used an insole sensing system with 16 Force Sensitive Resistors (FSR) to analyze the force distribution underneath the foot. During the experiments, the subjects were asked to stand with both feet flat on the ground for one minute.

Results has shown that balance has a frequency component and a magnitude component that varied according to the balance stabilities of the subject.

[ [START_REF] Mustufa | Design of a smart insole for ambulatory assessment of gait[END_REF] designed and developed a smart insole that can be used for the assessment of long-term chronic conditions. This insole also offers an evidence base for rehabilitation. The insole design is made of two layers attached to each other. The sensor layer and the electronic components layer. These layers are manufactured using Surface Mounted Technologies (SMT) and encased inside a rubber pad. The sensor layer has 32 capacitive pressure sensors distributed over the plantar surface. The hardware layer comprises all the electronic components for conditioning, processing, communicating and powering of the system. The insole accommodates also an accelerometer, a temperature sensor and a pressure sensor. The system is able to transmit data to a remote computer or Smartphone using a Bluetooth communication module.

[ [START_REF] Xu | Smart insole : A wearable system for gait analysis[END_REF] described a foot wear system that can be used for gait analysis. The system has the objective to identify normal and pathological walking patterns during all types of daily life activities. The Smart Insole as they named it, consists of 48 pressure sensors with a 3-axis accelerometer and gyroscope, a microcontroller and a Bluetooth module for communication. The sensors cover most of the foot plantar surface from the toe to the heel. The system has a sampling rate of 100 Hz and used 8 12-bits ADC channels. Spatio-temporal parameters such as the number of steps, step-time, stancetime, pitch, angular velocity were extracted in the experiments for future analysis.

[ [START_REF] Manupibul | Design and development of smart insole system for plantar pressure measurement in imbalance human body and heavy activities[END_REF] presented a footwear device that measures the plantar pressure distribution in different force ranges. The purpose of the device is to assist doctors in identifying patho-mechanical dysfunction (or body imbalance) and improve sport skill performance. The device is realized at the lowest cost. The hardware part consists of an insole with eight FlexiForce sensors. These sensors are connected to an Arduino unit for data collection and conditioning. The Arduino unit transmits the data to a remote station through an X-Bee transmitter module. The software is a Graphical User Interface developed using C# to visualize the foot plantar pressure distribution. The system is designed to bear extreme activities such as long jump and parachute jump. The system is tested by one athlete and four soldiers to record the pressure distribution during two sets of experiments (long jump and parachute jump).

[ [START_REF] Light | Optimizing pressure sensor array data for a smart-shoe fall monitoring system[END_REF] designed an optimized layout for pressure sensors for a smart shoe.

Their design has the goal to find a robust, reliable, usable and cost effective fall detection and monitoring solution. Their proposed system features an insole with smart-skin pressure sensors that is developed by SmartSkin Technologies Inc. The insole has a layout of 6x21 pressure sensors layout which are continuously read and transmitted using Bluetooth communication. In the experiments, the subjects wore the insole on their left foot.

The categories of fall-like events used in their experiments are falling-left, falling-right, falling-forward, falling-backward, standing, walking, and kneeling down. In their classification of fall and non-fall events, they used different machine learning algorithms such as Naive Bayes, Bayes Nest, Radial Basis Function (RBF) Network, Sequential Minimal Optimization (SMO), Lazy.IBK and j48. They presented the results in terms of the build time, accuracy, precision, f-measure, recall and sensitivity parameters. J48 and Naive Bayes were able to build a model that is reasonably faster, whereas lazy.IBK was able to maintain high accuracy and precision as well as better sensitivity and specificity while modeling fall activity.

[ [START_REF] Berengueres | A smart pressure-sensitive insole that reminds you to walk correctly : An orthoticless treatment for over pronation[END_REF] addressed the problem of foot over pronation and calcaneal inversion prevalence. Correction of this foot deformation is usually done using heel wedges insoles which can lead to a healthy footprint restoration. The authors in this paper tackled this problem with an interactive insole system that is able to detect when a foot overpronates and warn the user to correct their posture using their own muscles by means of vibro-tactile feedback. The force sensor is located at the outer mid-sole position of the foot. They chose to measure the connectedness of the pressure footprint shape and the calcaneal inversion degree of a foot posture. The system warns the user when more than four steps have not reached a threshold pressure. They recorded one data point every 150 seconds that consists of : number of steps, mean and standard deviation of force, number of warnings issued to the patient and the simplified histogram of force. During the experiments, they tested the system in two scenarios : 1) The patient had a severe inversion, his footbridge had collapsed since childhood.

2) The second patient had a mild inversion. The subjects learned how to correct the valgus posture using the device. Berengueres et al. combined data after testing the patients for two weeks. They noticed when they turned off the feedback the over-pronation increased by 30% to 50%, the patient reverted to the old walking style but not completely. Finally, this system helped the patient to reduce unhealthy posture time by 50%.

[ [START_REF] Kawsar | A novel activity detection system using plantar pressure sensors and smartphone[END_REF] stressed on the importance of continuously monitoring various activity patterns of elderly people at home as indicators to predict health risks. They presented an insole system that consisted of eight pressure sensors, a gyroscope and an accelerometer. The insole is connected via Bluetooth to transfer the pressure data to an android phone. The gyroscope and the accelerometer are those of a smartphone. Data from the sensors are sampled at 37Hz and transmitted using Bluetooth communication module to a remote station where they were being processed using MATLAB. They estimated the mean, the median, the standard deviation and the summation of the sampled data for each of the eight sensors. They tested the system during four activities : sitting, standing, walking, and running. Having the summary files of each activity, they applied three approaches for activity recognition using machine learning algorithms. In the learning phase they applied three algorithms to each data set to find a classifier. Four classifications are applied based on the accelerometer, gyroscope and the pressure data from the left and right shoes. The final activity is identified based on the majority voting algorithm. In their second approach, they considered their sensor data as time series shapelets and applied recently developed algorithms to differentiate those shapelets (between sitting and running). Gaussian Mixture Model (GMM) with time-delay embedding was used for detecting different activities.

[ Srivises et al., 2012a] designed a smart shoe for gait analysis. The objective of this smart shoe is to distinguish the five phases of a normal gait cycle in real time (stance, heel-off, swing1 ,swing 2, heel-strike). The system consist of four FSR (3 circular and 1 square) and a gyroscope. The circular force sensors are placed in the fore-foot and the mid-foot, whereas the square sensor and the gyroscope are located at the bottom. Data from the sensors are sampled at 20Hz using an Arduino unit and transmitted to a remote station using the X-Bee module. In their analysis method each gait phase is defined as a distinct state. The transition between the gait states is determined by the input signals measured from the FSRs and the gyroscope and by the state transition theory whose condition of the transition are divided into sub-event and transition-events. The FSR thresholds were set to be the average value of the overall ambulation and the thresholds of the angular velocity were set to be 10% of the peak value. During the experiments, a normal person was assigned to walk on a smooth surface for 40 times. The accuracy of this decision system was 81.88% validated by comparing results to pictures taken from a video camera.

In their second paper [START_REF] Srivises | Design of a smart shoe for reliable gait analysis using state transition theory[END_REF], Sriviseset al. used a fuzzy logic algorithm to deal with the transition between gait phases. Two important components of fuzzy logic are 1) fuzzy membership functions are employed to relate the sensor outputs to the gait phases using hyperbolic tangent function with a counterpart value and 2) fuzzy membership values are used to represent the gait phases. In this work, each FSR was set to have its own thresholds. These were set to be the average value of the overall ambulation.

During the experiments, the person places the foot on the floor differently in order to vary the starting sensor outputs and vary the gait speed. Thus, the data obtained from the experiments were applied to the fuzzy membership function and value. The accuracy of this decision system is 85%.

[ Majumder et al., 2015a] discussed the relevance between gait analysis and elderly falls, with gait analysis being used for a comprehensive study of falling in old age. To answer their need, they equipped the insole of their smart shoe with 4-piezo resistive pressure sensors located at the top and at the bottom of the foot. Data is collected via a Wi-Fi communication between the smart shoe and the phone where the pressure variation is visualized. During the experiments, 3 women and 7 men were recruited. The subjects were asked to walk with free gait, with trendelenburg gait and spastic gait. The system analyzes the number of peaks, the standard deviation, and the step frequency of each sensor. They demonstrated two classes of application using their system : 1) self-gait training assistance, and 2) gait disorder relief for patients with Parkinson. In their second paper, [START_REF] Majumder | A wireless smart-shoe system for gait assistance[END_REF] used the same multisensory system to study abnormal walking patterns to prevent falls. The smart shoe collected foot pressure while the subjects were asked to perform 4 different types of simulated walking patterns : Standing still, walking with no abnormalities, walking with stiff leg and walking with leg length discrepancy. During the experiments, they collected a total of 15 different samples of pressure values from 15 subjects during the three different walking patterns. Each data sample was 18 sec long. They used Reconstructed Phase Space (RPS) and Gaussian Mixture Model (GMM) for gait detection. The result is 89% accuracy for fall prevention.

[ [START_REF] Donkrajang | A wireless networked smart-shoe system for monitoring human locomotion[END_REF] were interested in monitoring static and dynamic posture of healthy volunteers with the implementation of a low cost wireless data shoe. Their shoe consists of 3 force sensors and a 3-axis accelerometer. The force sensors are located at the ball, the lateral arch and the heel. The data from the sensors is sampled at 10 Hz using the Analogue to Digital Converters (ADC) of the microcontroler and transmitted via a ZigBee wireless module. The experimental protocol considered different scenarios such as sitting, standing and walking of 6 healthy subjects (4 males and 2 females) with an average age of 25. Results of the experiments found that constant values are present for static posture (sitting, standing) while dynamic posture (walking) shows cyclic values.

In their second research, [Donkrajang et al., 2012a] were interested in 3 different walking postures namely the normal walk, the tip-toe walks, and the dragging foot walk. They designed for this purpose a second insole. The insole has 6 force sensors and 1 bending sensor which are spread all over the area of the foot. The data is sample at 10Hz using the ADC of an 8-bit microcontroller and transmitted using a Zigbee module. The experimentation process is conducted on 5 healthy male volunteers with shoe size of 7.5 US. Principal Component Analysis (PCA) is used to analyze pressure values from the sensors from each posture.

[De Rossi et al., 2012] and [START_REF] Crea | Development of gait segmentation methods for wearable foot pressure sensors[END_REF] insisted on the importance of developing automated gait segmentation methods to replace human experts in performing gait analysis. In their papers, they proposed two methods for gait segmentation. The first method ensures a gait cycle with no-swing phase. The second is an automated segmentation method based on the analysis of plantar pressure signals that are recorded by two wireless foot insoles with a common machine learning technique. Both methods were based on the Hidden Markov Model (HMM) to detect gait phases with different levels of complexity. Experiments involved 5 subjects walking at two different speeds (normal pace and fast pace). Each subject has to perform 200 steps. The gait of an individual was divided into 6 sections and which are fed to the machine learning method. Results of both methods were the same showing high classification performances being 95% on average.

Recently, [START_REF] Crea | A wireless flexible sensorized insole for gait analysis[END_REF] described the design of another insole system. It is equipped with low-cost pressure sensors to monitor the plantar pressure distribution during walking.

The insole consists of 64 pressure sensitive elements. Data from the sensors are sampled by 4 16-channel Analogue to Digital Converters at 1.2KHz. Data is then processed by a microcontroller and transmitted via a Bluetooth module to a remote computer for further analysis. They used a gait segmentation algorithm that is based on equations relating the Ground Reaction Force (GRF) and the Center of Pressure (CoP) of the individual.

The system is tested on two individuals with similar characteristics in height, weight and shoe size. Recorded data were analyzed in two steps. The first is the reconstruction of a time-changing qualitative map of the pressure distribution of the foot. The second is the combination of online computed gait phases that allowed the extraction of different gait parameters (e.g. stance and swing duration, duration of the double support, step cadence for both feet, etc.) [START_REF] Bae | A mobile gait monitoring system for gait analysis[END_REF] and [START_REF] Bae | Design of a mobile gait monitoring system[END_REF] were interested in mobile gait monitoring for rehabilitation. They proposed a mobile gait monitoring system that diagnoses abnormal gait and helps in its rehabilitation by enabling the subject to correct their gait when needed.

The system consists of smart shoes and a micro signal processor with a display that provides visual feedback to the user when the ground reaction force deviates from a fixed normal line. When patients start to exhibit abnormal gait, they follow the visual feedback as reference to rehabilitate their own gait. The system's insole is equipped with sensors fixed in terms of anatomy, on the hallux, the first metatarsal, the fourth metatarsal and the calcaneus. The system uses a 32-bit ARM Cortex-M3 core for processing and a 2.8" screen display. The system is tested on two subjects, one with Parkinson and the other with spinal stenosis. Results proved and verified that abnormal gait can be detected and improved by the mobile gait monitoring system.

[ [START_REF] Liu | A wearable ground reaction force sensor system and its application to the measurement of extrinsic gait variability[END_REF] described the design of a wearable Ground Reaction Force (GRF) sensor system for gait analysis. The system uses 5 small triaxial force sensors placed on the hallux, the fifth metatarsal, the mid area of the foot and 2 on the heel. Data from the sensors are sampled at 100 Hz using the ADC of a PIC16F877A microcontroller and transmitted to a remote PC for further analysis. The system is tested on 7 volunteers aged between 25 and 32 that were required to walk at three different speeds (slow, preferred speed, fast). The designed GRF system was validated with a reference system using a force plate taking into consideration all the parameters that influence the sensor system in terms of natural gait. In conclusion, the system showed good results for extrinsic gait variability analysis.

[ [START_REF] Shu | Inshoe plantar pressure measurement and analysis system based on fabric pressure sensing array[END_REF] were interested in the making of a system with high-pressure sensitivity, long service life with a low cost of realization and flexibility. They designed an in-shoe plantar measurement system where they placed 6 sensors inside the insole (3 on the heel section and 3 on the forefoot). The system used 10-bit ADC and has a sampling rate of 100 Hz. The processing part consists of a PIC18F452 microcontroller and a Bluetooth module for data transmission. The system provided a real time display and analysis software to determine parameters such as the mean pressure, the peak pressure, the CoP, and the shift speed of the CoP. The experimentation of the designed system showed that discreet pressures are able to reflect the wearer's status and plantar pressure change. It can be seen from the systems summarized in Table 4.1, that the main components of most footwear systems are identical. The system design is commonly divided in two layers : The physical layer and the software layer. The first is usually composed of the main functional blocks namely, the sensing, the processing and the communication. In a footwear device, this layer consists of the design of the insole and the data acquisition electronics. Sensors are commonly placed inside the sole of a shoe on the foot plantar surface. The processing and the communication are usually performed by the data acquisition electronics which perform functions such as sensor conditioning, data processing, data transmission, and power management. The second (the software layer) is where the data is processed or analyzed. Data processing and analysis can be either performed locally on the physical layer or remotely on a remote computer. Data processing and analysis is composed of functions such as data collection, parameter extraction, and decision algorithm. With reference to Chapter 3, data processing and analysis in fall-related systems can be performed using Analytical Methods (ANM) or Machine Learning Methods (MLM). ANM are more power and resource efficient and can be implemented in applications that require real time response. MLM are generally used with large amount of data where deep insight is required. In summary, the choice of the components, their number and their type as well as the parameters extracted and the data analysis algorithm are highly dependant on the application and the objectives of the study.

In the following chapters of part II, we will describe the design concept and the development of our proposed Force Sensing Shoe (Chapter 5) and our proposed early fall detection approach (Chapter 6).

FORCE SENSING SHOE

INTRODUCTION

The first functional entity of our proposed WMFL v1.0 Platform introduced in Chapter 3 Section 3.9 of this thesis, is the Force Sensing Shoe. According to the generic classification described in Chapter 3 Section 3.5, the designed system lies under the Foot Wear (FW) sub-category of Fall Prevention (FP) systems. In this chapter, we describe the concept design and the development of the Force Sensing Shoe system with a specific emphasis on the design of the instrumented insole. Through this chapter, the architecture of the system is explained taking into consideration the instrumented insole with piezoresistive force sensors and the wearable electronic board. The designed Force Sensing Shoe is able to extract the weight and the Center of Pressure (CoP) trajectory. The latter parameter will be exploited in Chapter 6 in our proposed early fall detection approach.

FORCE SENSING SHOE DESIGN CONCEPT

SYSTEM DESIGN REQUIREMENTS

The objective of our proposed footwear system is to be able to provide insight on the patient gait and balance during his daily life activities. The design concept of the system must be as much as possible scaled and optimized for the desired application. Therefore, the system must comply to specific constraints that should be taken into consideration.

In the context of Fall Prediction, the system must be able to collect and analyze gait and balance parameters to alert the patient from a potential fall. This can be seen from the design concept point of view, that the footwear system must ensure functions such as data collection, features extraction, data analysis and alert notification. Notification in real time is also a condition in the design concept of a fall-related system. Moreover, the system must be able to operate as a stand-alone or to be interfaced with other remote systems. In addition, the system must be portable ; it must be easy to wear and less intrusive to the patient. In fact, opposite to Body Wear (BW) systems, a footwear device has the advantage that it will not be easy forgotten to be worn by the patient. The system should also be able to operate in indoor and outdoor premises. In terms of usability, the system should require minimum training by the user.

SYSTEM DESIGN ARCHITECTURE

From the hardware perspective, the proposed footwear system is a Force Sensing Shoe for elderly fall detection or prediction. The design concept is divided into two main parts :

-The shoe with the instrumented insole -The wearable electronic board The design architecture of the proposed footwear system is shown in Figure 5.1. As shown in Figure 5.1, the instrumented insole with force sensors is located inside the shoes. It is hardwired to the wearable electronic board fixed at the waist of the person. The choice of this configuration is for prototyping and testing purposes only as we are currently working on two separate electronic boards located inside the tissue material at the rear side of the shoe. The new configuration will release the person from the cumbersome of the wire connection between the insole and the electronics eliminating by such the intrusiveness on the patient. The electronic wearable board is capable to collect force data from the instrumented insole, extract and analyze gait and balance parameters in the purpose to notify the person from a potential fall, complying thereby to the design requirements of a fall-related system. This electronic board has Bluetooth and ZigBee communication capabilities allowing data communication with a remote system or computer. The reason behind this configuration is the operability and scalability of this system. As a matter of fact, our Force Sensing Shoe is capable to communicate with a Smartphone through the Bluetooth interface and with an Infrared Sensing Floor using the ZigBee interface as part of the WMFL v1.0 Platform described in details in Chapter 10. With this configuration, our Force Sensing Shoe is capable to operate in indoor/outdoor premises via stand-alone and dependant modes. Note that our proposed wearable electronic board has room for an Inertial Measurement Unit (IMU) composed of an accelerometer and a gyroscope.

The following section describes the hardware design and development of the Force Sensing Shoe mainly the instrumented insole with piezo-resistive force sensors and the wearable electronic board.

FORCE SENSING SHOE DEVELOPMENT

SHOES AND INSOLE DESIGN

The first part of the system architecture is the shoe with the instrumented insole. In the subsequent paragraphs of this subsection, we will describe the shoe requirements and the design of the insole equipped with the force sensors.

SHOE REQUIREMENTS

The choice of the shoe itself is particulary essential and very precise. From one hand, the sole of the shoe must be flexible and soft for the foot to fit and rest in. In fact, The elderly must feel at ease while performing his daily activities. From the other hand and according to a podiatrist, the bottom of the shoes must be flat and thin for better positioning of the sensors and later for the extraction of the gait parameters such as the plantar pressure distribution and the Center of Pressure trajectory. The size of the shoe is also an important requirement, since it is directly related to the population that will be considered later in the experiments. The shoe size was chosen between 43-44 (European sizing chart) with the possibility to reduce the size to 42 by adding a thin rubber insole. The rubber insole will also serve to cover the inner circuitry of the instrumented insole described in the following paragraph. The shoe was picked from a local store and is compliant to the afore-mentioned requirements. The photo of the shoe is shown in Figure 5.2.

INSTRUMENTED INSOLE DESIGN

The instrumented insole design must be precise and well optimized to ensure accurate measurement of gait and balance parameters. In terms of application requirements, the instrumented insole must be light (< 300g [START_REF] Bamberg | Gait analysis using a shoe-integrated wireless sensor system[END_REF]) and flexible to fit inside FIGURE 5.2 -A pair of shoes for the foot wear system the shoe. It must be very thin and of the same size of the shoe. Typically, a wireless instrumented insole is advantageous for this kind of applications since it reduces the cabling cumbersome on the patient to ensure a safe and natural gait. In the process of the insole design, a particular attention must be drawn to the location and to the number of sensors on the plantar surface of the foot. In order to bear the full weight of the body, the sensors must be located at specific points on the insole. [START_REF] Shu | Inshoe plantar pressure measurement and analysis system based on fabric pressure sensing array[END_REF] divided the surface of the sole into 15 areas claiming that 15 sensors are needed in each foot to cover most of the body weight changes. [START_REF] Perttunen | Effects of walking speed on foot loading patterns[END_REF] followed a different distribution but with 16 sensors. Other systems such as those discussed in the related work use more or less number of sensors. In fact, the number and the location of the sensors is highly dependant on the application. Typically, the whole insole must be covered with sensors. Commercial systems such as those manufactured by Tekscan (www.tekscan.com) and Vista Medical (www.pressuremapping.com) have 960 and 128 sensors respectively. These systems have high cost (c. 20,000 USD) and they are mainly used to assess extrinsic and intrinsic pathologies detected by foot plantar pressure measurement inside clinics (e.g. foot deformation, diabetes, strokes, etc.). In addition, these systems require complex processing electronics to operate.

According to [START_REF] Nordin | Basic biomechanics of the musculoskeletal system[END_REF], the load distribution on the foot is distributed as follows : 60% in the Rear-Foot (under the heel), 8% in the Mid-Foot, 32% in the Fore-Foot (28% under the metatarsal and and 4% under the toe). The load distribution is shown in colored areas on Figure 5.3. Following this load distribution, the number of sensors can be reduced to 8, minimizing by such the complexity of the processing electronics. In order to cover a good amount of the foot area and thus cover the body weight changes, we deployed sensors with different sensing area sizes. 5.1 corresponds the location of the sensors with respect to the load distribution on the foot.

In terms of sensor requirements, there are five types of force sensors that can be used to measure the body weight and thus the foot plantar pressure distribution. These sensors can be of type optical, capacitive, resistive, piezo-resistive, and piezo-electric. The functioning principle of these sensors is described is details in [H ürzeler et al., ]. For optimal sen- sor performance, key specifications must be considered in the choice of a force sensor.

The key specifications are but not limited to, output linearity, low hysteresis, low temperature sensitivity, high accuracy, and high dynamic range (typically >1000 KPa for gait analysis) [START_REF] Bamberg | Gait analysis using a shoe-integrated wireless sensor system[END_REF]][Lee et al., 2001][Shu et al., 2010]. Our instrumented insole is equipped with 8 Force Sensing Resistors (FSR). These sensors are of piezo-resistive type which measures the resistance of conductive foam between two electrodes when force is exerted. The applied force causes conductive particles to touch which increases the current through the sensor [Abdul Razak et al., 2012]. The force sensors used in our instrumented insole are manufactured by Tekscan. The sensors are commercially known as "FlexiForce sensors". They can measure both static and dynamic forces (up to 4500 N). They are very thin (0.23 mm) which enables a non-intrusive force measurement. The chosen sensors have 3 sizes depending on their location under the foot. The sensors and their dimensions are illustrated in Figure 5.4. From left to right, the sensors references are FLX-A301, FLX-A401 and FLX-A502. Table 5.2 lists the key specifications for the deployed force sensors.

WEARABLE ELECTRONIC BOARD

The second part of the system design architecture is the wearable electronic board. It is composed of different functional blocs as illustrated in Figure 5.6. In the subsequent paragraphs of this subsection, we will describe the hardware development of the wearable board namely, the force sensor interface block, the processing and control unit block, the notification block, the communication interfaces block with the remote system, and finally the power management block.

PROCESSING AND CONTROL BLOCK

The incoming analogue voltage produced by the sensor interface is converted to digital using the A to D converters of the processing and control block. This block is typically composed of a microcontroller unit. This is where the force data can be collected, processed or analyzed. The microcontroller unit can be either programmed to collect raw force data and transmit them to a remote computing system for further processing and analy-sis, or, it can be programmed locally as a stand-alone processing and analysis unit. In the latter case, an early fall detection method can be programmed to alert the patient if a fall is about to happen. For instance, parameters such as the CoP trajectory can be extracted from the raw force data and analyzed locally to predict a fall. An audible notification can be triggered to alert the person. An early fall detection method is proposed in Chapter 4.

The microcontroller unit used in our wearable electronic board is the PIC18F45K22 manufactured by Microchip. The choice of this microcontroller is based on different criteria such as the oscillator speed (up to 16 MHz), the size of the program and data memories (64 KB and 4 KB respectively), the number and the resolution of the A to D converters (28 ADC with 8 or 10 bits resolution) and the number of serial interfaces (2 UART). The microcontroller is programmed using the In-Circuit Serial Programming (ICSP) interface available in the circuit design of the wearable electronic board. The microcontroller unit and its connection to the rest of the components is shown in the design of the wearable electronic board circuit schematic in Figure B.3 of Appendix B.

NOTIFICATION BLOCK

The output of the data analysis produced by the processing and control block during the stand-alone analysis mode can be generated using an audible notification made available by a small buzzer. For instance if a fall is about to happen, an audible notification can be generated using this buzzer. This piezo-buzzer is ideal for use with microcontrollers for its low current consumption (5 mA) and high sound pressure level (≤ 70 dB). In addition, the wearable board is also designed to accommodate a Liquid Cristal Display (LCD) that can be used for sensor calibration. The notification interface circuit schematic of the buzzer and the LCD are shown in Figure B.3 of Appendix B.

COMMUNICATION INTERFACE BLOCK

The communication interface of the wearable electronic board has two modules : The Bluetooth module and the ZigBee module. These can transmit raw data to a remote system. In our proposed Force Sensing Shoe design architecture in Figure 5.1, the Bluetooth module can communicate with a Smartphone, while the ZigBee module can communicate with the Infrared Sensing Floor which will be introduced later in chapter 6 as the second functional entity of our proposed WMFL v1.0 Platform. The Bluetooth and the ZigBee modules are connected to the microcontroller unit through its two serial UART interfaces. The Bluetooth and the ZigBee communication modules are integrated in the design of the circuit schematic of the wearable electronic board shown in Figure B.3 of Appendix B.

POWER SUPPLY BLOCK

The wearable electronic board is powered by a 12 V/1300 mA rechargeable battery. At the time of the research, the battery is charged using a separate charger. The new wearable electronic board design considers an on-board battery charger. The 12 V battery voltage is converted to 5 V and 3.3 V using LM7805 and LM1086-3.3V voltage regulators respectively. These voltage regulators are used to supply the low power components such as the microcontroller, the amplifiers and the communication modules

PARAMETERS EXTRACTION

The biomechanics of the human gait and balance can be characterized by kinetic and kinematic parameters. The following parameters are extracted from our Force Sensing Shoe :

-Weight : The human body weight is evenly distributed on both feet during normal standing. The weighing scale is the device that can measure the body mass in kilograms. This device typically use load sensors for this purpose. The weight is therefore determined by Equation 5.1 as the product of the mass of the body (Kg) and the gravitational constant (g = 9.81N/Kg). To measure the weight with our Force Sensing Shoe, the sum of the vertical forces at the points of maximum load is determined (Figure 5.5). Our instrumented insole is particularly designed to determine the weight by using sensors of different area sizes located at the points of maximum load. The body weight is determined using our Force Sensing Shoe by Equation 5.2.

W = m • g (5.1) W = • n i=1 F i (5.2)
where n is the total number of sensors under both feet (n = 16 in our case) and F i is the force in (N) measured at sensor i.

-Center of Pressure : The CoP is translated as the application point of the Ground Reaction Force (GRF), with the GRF being known as the sum of all forces acting between a physical object and its supporting surface. It is also defined as the projection of the Center of Mass (CoM) on the ground. During quiet standing, the CoP is located between the feet at about 5 cm from the ankle joint [Kirtley, 2006].

During gait, the CoP moves from about the heel to the forefoot under the foot plantar surface. This excursion is called the CoP trajectory or pathway. The CoP location is determined using the moment of the vertical forces applied on the foot plantar surface. In our instrumented insole, the force data is used to determine the moment M F i of the force F i at each sensor i using Equations 5.3a and 5.3b :

M F i |x = F i • y i (5.3a) M F i |y = F i • x i (5.3b)
Where M F i |x and M F i |y being the moments of each force F i with respect to x and y axis respectively. x i and y i being the coordinates of each sensor i with respect to the foot coordinate system of the instrumented insole as shown in Figure 5.5. Subsequently, the CoP location defined by the CoP coordinates x CoP and y CoP is determined using Equations 5.4a and 5.4b :

x CoP = n i=1 M F i |x n i=1 F i (5.4a) y CoP = n i=1 M F i |y n i=1 F i (5.4b)
5.5 EXPERIMENTS AND RESULTS

EXPERIMENTAL SETUP

At this stage of the research, the prototype of the Force Sensing Shoe has been set for testing. The force sensors underwent conditioning and calibration before being welded on the insole at the required points. Note that the sensors were calibrated to bear a weight of 135 Kg at each foot which makes a total of 270 Kg on both feet taking into the consideration the load distribution mentioned in [START_REF] Nordin | Basic biomechanics of the musculoskeletal system[END_REF]. The insole is then inserted inside the shoes. The wearable board is attached to the waist at the lower back using a custom belt. The connection between the instrumented insole and the wearable electronic board is made by a shielded cable fixed at the level of the limbs. The microcontroller is programmed to read the raw force data from the sensors. Listing D.1 in Appendix D shows the code downloaded on the microcontroller. The data is transmitted to a remote computer for parameter extraction at a rate of 50 Hz. In other words, each sensor is sampled at 50 Hz making a total of 800 readings per second. The data is collected and displayed on our proper developed WMFL v1.0 software that will be later presented in Chapter 10.

EXPERIMENTAL PROTOCOL

To prove the concept of our Force Sensing Shoes prototype, two simple tests were carried. The purpose is to extract the weight and the CoP trajectory. To validate our results for the weight, a digital weighting scale was used. A group of 10 voluntary university students of different weights participated in the experiments. The participants have a foot size between 42 and 44 (EU). After having their weight measured by the digital weighting scale, the subjects wore the Force Sensing Shoe and performed a simple quiet standing on both feet. During normal standing, the weight was recorded. The weight being the sum of all the forces exerted on the force sensors in both feet according to equation 5.2. To extract the CoP pathway, the students performed a series of 20 stances. At each stance the CoP coordinates were recorded according to the Equations 5.4a and 5.4b. Each student performed the same walk 5 times.

RESULTS AND DISCUSSION

Table 5.3 shows the results of the quiet standing test for the ten subjects. It can be seen that the values are dependant on the size of the foot with respect to the size of the shoe. Note that for subjects 1, 6, 7, 8, 9 and 10 the error is less than 2% while for the subjects 2, 3, 4, and 5, the error is higher (4.09% for subject #3). In fact, this difference is due to the miss location between the position of the sensor on the insole and the points with maximum load of the foot. In other words, the sensors are not completely covering the points of maximum load, the reason for which discrepancies are found with feet of size 42. To minimize the accuracy error, the sensors location must be adjusted to cover the load points sliding the sensors on the designed tracks. Moreover, different insole sizes must me considered for each foot size. The number of CoP points is dependant on the number of sensors and the transmission rate. The CoP trajectory as well as the instantaneous GRF are important to study the kinetic of the human contact with the ground in order to assess gait. This parameter will be considered in determining the amount of sway in the Medial-Lateral plane and estimating the risk of falling.

FORCE SENSING SHOE PHOTOS

In this section, we provide the photos of the prototype of the Force Sensing Shoe namely, the shoe, the instrumented insole and the wearable electronic board. The photos are illustrated in Figures 5.9 and 5.9.

CONCLUSION

The continuous monitoring of elderly during their daily activities can be easily performed using low cost and non-clinical systems. In the context of elderly falling, these systems are able to extract and analyze parameters related to human gait and balance in order to alert the person of a risky situation. In this chapter, we discussed the design and development of our proposed footwear system which consists of a Force Sensing Shoe for elderly fall monitoring. The Force Sensing Shoe is the first functional entity of our proposed WMFL v1.0 Platform that makes the object of Part IV of this thesis. The Force Sensing Shoe was designed to collect the weight and the trajectory of the CoP on the plantar surface of each foot during gait. The latter feature will be used in our early fall detection approach discussed in Chapter 6. The Force Sensing Shoe was tested and validated with a group of ten students. Results has shown that the shoes can be adopted to assess the gait with a minor adjustment in its design to reduce the error. Future works will consider adding a layer of rubber with an emphasis over the sensing area of each sensor for accurate measurement of the force and thereby the weight. The wearable board will also accommodate an Inertial Measurement Unit (IMU) consisting of an accelerometer, and a gyroscope. This unit will provide more insights on the investigation of falls if combined with the force sensors.

EARLY FALL DETECTION APPROACH

INTRODUCTION

Various analysis methods and algorithms were developed to detect the abnormality of human gait and balance, and estimate the falling risk. In this chapter, we present a method to estimate the falling risk and alert the patient when a fall is about to happen. The proposed method consists in monitoring and analyzing the amount of body sway in the Medial-Lateral (M-L) plane by computing the Center of Pressure (CoP) displacement at each foot plantar surface during the stance phase of the gait cycle. In this method, we define the profile of the amount of sway during normal gait between two thresholds on the Medial and Lateral sides of the foot. Since a fall might be induced through several consecutive sways, we made use of the spatio-temporal sliding window correlation in order to benefit form the history of recent stances and to generate fall alerts in real time. Moreover, we used the logarithmic correlation to minimize false positive alerts during the consecutive normal stances following abnormal sways. The method was validated via a two-phase experimental protocol with five adults who performed a walk of 20 stances with simulated sways using the Force Sensing Shoe described in Chapter 5. This method can be applied in real-life and clinical settings with real time processing.

DEFINITION OF GAIT AND BALANCE

Walking is the basic aptitude that allows humans to move from one place to another and pursue their daily life activities. But, walking is exhibited in a proper manner called gait that is specific to each person's locomotion. As people age, they become more fragile and exhibit difficulties in maintaining their gait and balance. Gait analysis is the study of human manner of walking. One purpose of gait analysis is to determine intrinsic pathologies. Neuro-degenerative diseases for instance, have the potential to alter human gait and may lead to a falling incident. In fact, 50% of falls in older adults occur during walking [Kenny et al., 2010a] [Lord et al., 2007]. In the same way, [START_REF] Kenny | Falls prevention in the home : Challenges for new technologies. Intelligent Technologies for Bridging the Grey Digital Divide[END_REF] quantify the assessment of intrinsic fall factors according to their impact on the human biomechanics such as gait and balance. As a result, the assessment of gait and balance 55 parameters is to estimate among other pathological conditions the risk of falling. Below, we will give a definition on gait and balance.

-Gait : By definition, gait is a functional task requiring complex interactions and coordination among most of the major joints of the body, particularly of the lower extremity [START_REF] Nordin | Basic biomechanics of the musculoskeletal system[END_REF]. In other words, gait refers to the locomotion achieved in the movement of the lower limbs in which a person must initiate a fall forward and recapture their momentum through the appropriate placement of their leading foot [Kenny et al., 2010a]. Many diseases incite deficits in gait which can lead to a fall incident. Alternately, the evolution of different diseases (e.g. neurological, cardiopathies, strokes, etc.) can be monitored by an accurate and reliable knowledge of gait parameters over time. -Balance : By definition, balance is the ability of a person to maintain the body in a steady upright position in response to the surrounding sensory information generated by the brain sensory systems (visual, vestibular and somatosensory). But, these systems are affected by aging and diseases. As a result, incorrect sensory information is transmitted to the brain balance processing system can delay the generation of motor control forces to maintain steady posture which can induce a loss of balance exacerbated by the weakening of the musclo-skeletal system.

GAIT CHARACTERIZATION

Human gait is characterized by different kinetic and kinematic characteristics as well as spatial-temporal parameters. These characteristics follow a certain behavior during normal gait. Since falling may be the result of the alteration of these characteristics, the early detection of this alteration could save the person from a potential fall. The implementation of an early fall alert method depends on the selection of one or more of these characteristics. These will define the profile of the person under investigation. In our study, we considered the CoP trajectory on each foot plantar surface as an input gait characteristic to our early fall detection method. Recall that the CoP trajectory is a mathematical concept but extremely useful in assessing gait. The CoP used in this study is extracted from our Force Sensing Shoe described in Chapter 5.

PARAMETER SELECTION

The CoP is a very common mathematical concept widely used in assessing the biomechanics of human gait and balance. It represents the spatial-temporal instantaneous locations of the Ground Reaction Force (GRF) acting on the foot plantar surface during the stance phase of the gait cycle. Recall, that a gait cycle is a series of consecutive stances and swings of both lower limbs alternately. During the stance phase of a normal gait (foot in contact with the ground), the CoP excursion begins from the lateral border of the heel at initial contact and moves along the foot to the big toe at the toe-off as illustra-ted in Figure 6.1 [Kirtley, 2006]. This will define the trajectory of the CoP also known as the pathway or gait-line. The trajectory followed by the CoP over time can be represented by a vector V j of instantaneous coordinates (x j (i), y j (i)) of CoP with respect to the foot reference system (Figure 6.1). Vectors of CoP are collected during the stance phase of each limb as shown in Figure 6.2. The extraction of the V j from each foot can be realized using an insole with implanted pressure sensors located at points of maximum load on the foot plantar surface as was already seen in Chapter 5. The vector V j is defined by Equation 6.1 as follows :

V j = x j (i), y j (i) , i = 1...n j ; j = 1...m (6.1)
Where j is a stance during a walk ( j = 1, ..., m) and x j (i) represents the i ith instantaneous position of the CoP along the x-axis in the Medial-Lateral (M-L) plane, and y j (i) is the i th instantaneous position of the CoP along the y-axis in the Anterior-Posterior (A-P) plane. During normal gait, the excursion of the CoP, over the time of a single stance, follows quasi-similar trajectories. However, if the foot pronates or supinates, the CoP trajectory will tend to move toward the medial or toward the lateral sides of the foot. Thus, if a person sways to the left or the right during locomotion, the sway can be easily detected because the trajectory of the CoP deviates from its normal path in the direction of the sway. It follows that if the CoP overshoots the medial or the lateral edges of the foot, the person will undergo a fall. When a person sways, the displacement δx j of the CoP in the M-L plane increases compared to its displacement δy j in the A-P plane in the foot reference system. For the j th stance, these displacements are represented in Figure 6.1 and computed in Equations 6.2a and 6.2b as follows :

δx j = max i=1•••n j x j (i) -min i=1•••n j
x j (i) (6.2a)

δy j = max i=1•••n j y j (i) -min i=1•••n j y j (i) (6.2b) = y j (n j ) -y j (1)
Without loss of generality, the amount of sway for the j th stance is defined as the ratio r j of the x-axis displacement δx j over the y-axis displacement δy j as shown by Equation 6.3 :

r j = δx j δy j = max i=1•••n j x j (i) -min i=1•••n j x j (i) max i=1•••n j y j (i) -min i=1•••n j y j (i) (6.
3)

The reason behind this normalization is to render the range of possible values for the ratio r j comparable for a set of people with similar postures and shoe sizes. For a complete walk, the set of all the ratios r j defines a vector R formulated by Equation 6.4 :

R = r 1 , ..., r m = r j , j = 1...m (6.4)
During normal gait, the displacement δx j of the CoP is minimal and thus the value of the ratio r j can be bounded between two thresholds. It follows that these thresholds define the profile of the amount of sway in the M-L plane during normal gait for a set of people with similar postures. Nevertheless, this profile can also be defined for a particular person with particular gait pattern. We set Th L as the upper bound of the maximum value of the ratio r j on the lateral side of the foot and Th M as the lower bound of the minimum value of the ratio r j on the medial side of the foot. Th L and Th M will be extracted during the normal gait and will remain constant through the rest of the scenarios.

Furthermore, it should be noted that the ratio r j is not relevant during normal stances (Th M r j Th L ). However, when a person sways, we are interested in the excess sway of the ratio r j compared to the thresholds Th L and Th M . For this purpose, we introduce the sway tolerance as the level for which the patient is still able to maintain his balance during the sway without undergoing a side fall. The sway tolerance is defined by the levels R L and R M on the lateral and medial sides of the foot respectively. These tolerance levels also noted as risk levels will be determined experimentally and can affect the number of alarms during an unbalanced walking. Taking these levels into consideration, the excess sway of the ratio r j will be therefore bounded as shown in the Equation 6.5.

R M < Th M r j Th L < R L (6.5)
As stated earlier, the amount of excess sway is a variable defined by χ L j and χ M j on the lateral and medial sides of the foot. χ L j and χ M j are defined in Equations 6.6a and 6.6b as follows :

χ L j = max 0, r j -Th L (6.6a) =        0 if r j Th L r j -Th L if r j > Th L χ M j = max 0, Th M -r j (6.6b) =        0 if r j Th M Th M -r j if r j < Th M
The total excess sway χ j is considered as the sum of lateral excess sway χ L j and the medial excess sway χ M j . The latter is weighted so that the spatio-temporal correlation can be equally considered when sway is analyzed on both feet in a continuity of this research. The total excess sway is expressed in Equation 6.7.

χ j = χ L j + R L -Th L Th M -R M • χ M j (6.7)

STANCE HISTORY ANALYSIS

As imbalance is not directly related to one particular stance and its indicators may show up in several consecutive stances, we made use of a spatio-temporal sliding window technique in order to benefit form the history of recent stances. For this aim, we introduce a sliding window W of size s defined in equation 6.8 as follows :

W = ω 1 , ... , ω s = ω k , k = 1...s (6.8) 6.4.1 SPATIO-TEMPORAL CORRELATION 6.4.1.1 LINEAR SPATIO-TEMPORAL CORRELATION
The linear spatio-temporal technique will correlate the current measure of the sway excess χ j and the its past values, and extract one single indicator ζ j representing how risky were the last s stances. This spatio-temporal indicator ζ j is computed as the sum of the excess sway χ j weighted by the coefficients of the sliding window W as expressed in the following Equation 6.9 :

ζ j = s k=1 χ j-k+1 • ω k (6.9) = χ j • ω 1 + χ j-1 • ω 2 + ... + χ j-s+1 • ω s
Spatio-temporal coefficients ζ j equal to zero correspond to a normal gait. For slightly positive values of ζ j , this is usually an indicator that the ratio r j have slightly exceeded the normal threshold Th L or has slightly fallen below the normal threshold Th M . In the first case, the gait can still be considered as normal, however, in the second case, the patient might be recovering from an imbalance situation where his second foot could tend to its medial side. When the coefficients ζ j become important (> R L ), this usually indicates an imbalance and an alert should be triggered. Reciprocally, when ζ j fall largely below (< R M ), the foot tend to pronate inwards which could either indicate that the patient is recovering from a severe sway or the foot is undergoing an over-pronation caused by arch collapse of the foot.

LOGARITHMIC SPATIO-TEMPORAL CORRELATION

The linear spatio-temporal correlation has the inconvenience of generating fall alerts even when the person had recovered from an imbalance situation. That is when values of r j has dropped but are still slightly above the Th L for the next couple of stances. For this reason, we applied the Logarithmic log 10 (.) function on the excess sway. The logarithmic function will suppress these values under the normal threshold reducing by such the number of alerts. The sum of product will correlate the log of the sway excess χ j with the weighted coefficients ω k and extract the indicator ζ j as defined by Equation 6.10.

ζ j = χ j • ω 1 + s k=2 log 10 (1 + χ j-k+1 ) • ω k (6.10) = χ j • ω 1 + log 10 (1 + χ j-1 ) • ω 2 + ... + log 10 (1 + χ j-s+1 ) • ω s 6.

SWAY ANALYSIS

As mentioned earlier, we will analyze the last s stances of the gait cycle. For this purpose, we will add up the excess sway measured during each stance weighted by a coefficient. This coefficient should reflect how much the previous stance do contribute to the imbalance observed during the current stance. This spatio-temporal correlation technique is commonly known as the sliding window. The number s of past stances considered when analyzing the current stance, i.e., the size of the sliding window, as well as the weighting coefficients have a big impact on the performance of the proposed method namely, on the number of False-Positive and False-Negative alarms. On one hand, considering a very large sliding window will try to correlate the current stance with the one achieved some instant ago. At the opposite, considering a very narrow sliding window tends to consider that each stance is independent from the previous one.

In our study, we assume that the stances within a 5-meter walk are correlated among them. For the one-foot analysis, this would correspond to a sliding window of size 5

W = ω 1 , ω 2 , ω 3 , ω 4 , ω 5 .
In order to estimate the coefficients/weights of this sliding window, we define the following set of rules :

-Without loss of generality, we set to 1 the weight of the current stance. The remaining coefficients of the sliding window will be determined accordingly.

ω 1 = 1 (6.11)
-The weight of an old stance should not exceed the weight of a more recent one. This is formulated as follows :

ω 1 ω 2 ω 3 ω 4 ω 5 (6.12)

-Whenever two consecutive abnormal stances occur, an alert should be triggered. This is expressed as follows :

ω 1 • χ j + ω 2 • χ j-1 R L -Th L (6.13)
-In the particular case where the excess sway of two consecutive stances is in the lateral direction (the foot tends to supinate), Equation 6.13 resolves into :

ω 1 • χ L j + ω 2 • χ L j-1 R L -Th L (6.14)
-Similarly, in the case where the excess sway of two consecutive stances is in the medial direction (the foot tends to pronate), Equation 6.13 resolves into :

ω 1 • χ M j + ω 2 • χ M j-1
Th M -R M (6.15)

-Whenever the number of abnormal stances exceeds 50% of the total number of stances, an alert should also be triggered. After removing the redundant possibilities, this resumes to the following equations :

-If two out of the last three stances are abnormal, we trigger an alert.

ω 1 • χ j + ω 3 • χ j-2 R L -Th L (6.16)
-If three out of the last five stances are abnormal, we trigger an alert.

ω 1 • χ j + ω 4 • χ j-3 + ω 5 • χ j-4 R L -Th L (6.17)
-Finally, if two abnormal stances are separated by three normal stances, no alert should be triggered.

ω 1 • χ j + ω 5 • χ j-4 < R L -Th L (6.18)
In order to determine the optimal coefficients ω k=1...s of the sliding window, the problem was formulated as a Linear Programming (LP) problem with the objective to minimize the sum of the coefficients. This objective can be expressed as follows :

min s k=1 χ j-k+1 • ω k (6.19)
By solving the previous LP problem using traditional solvers such as Cplex [Cplex, 2000], we obtained the following optimal set of coefficients :

W = 1, 0.67, 0.67, 0.33, 0.33 (6.20)

EARLY FALL DETECTION ALGORITHM

The pseudo-code of the one-foot fall alert algorithm is shown in Algorithm 1. The inputs are the normal thresholds in the Lateral and Medial sides, Th L , Th M with their respective risk levels R L and R M . For each new stance j, the displacements in the M-L and the A-P planes are computed to obtain the ratio r j . After that, the excess sway χ is determined.

Having the window weights and the excess sway, we apply either the linear or the logarithmic spatio-temporal correlation. The correlation result is simply compared to R L -Th L for the final decision.

DETERMINATION OF FALLING RISK

The risk of falling is computed for the total number of stances in a walk. We distinguish between the low risk indicator RS K l when the spatio-temporal coefficients χ j are slightly positive and the high risk indicator RS K h when the coefficients χ j exceeds the Risk Level R L in the lateral side of the foot. The equations of the low and high risks are given by Equations 6.21 and 6.22, respectively.

RS K l = m j=1 0<χ j ≤R L -Th L r j ≥Th L r j m j=1 r j (6.21) RS K h = m j=1 χ j ≥R L -Th L r j ≥Th L r j m j=1 r j (6.22)
The total risk RS K t is therefore computed as the sum of RS K h and RS K l as given by Equation 6.23 :

RS K t = RS K l + RS K h (6.23)
Algorithm 1 Early fall detection algorithm

Input: W = ω 1 , ω 2 , ω 3 , ω 4 , ω 5
The history of past stances χ j-1 , χ j-2 , ..., χ 1

The thresholds R L , Th L , R M , Th M Output: Decision if an alert should be triggered for the current stance 1: for each new stance j do // Compute the M-L and the A-P displacements 2:

δx j ← max i=1•••n j x j (i) -min i=1•••n j
x j (i)

3:

δy j ← max i=1•••n j y j (i) -min i=1•••n j y j (i)
// Compute the ratio r j 4:

r j ← δx j /δy j // Compute the excess sways : Lateral, Medial and Combined 5:

χ L j ← max 0, r j -Th L 6:
χ L j ← max 0, Th Mr j 7:

χ j = χ L j +         R L -Th L Th M -R M         • χ M j
// Compute the spatio-temporal correlation 8:

ζ j ← χ j • ω 1 9:
for k ← 2 to 5 do 10:

ζ j ← ζ j + χ j-k+1
• ω k //for linear correlation or 11: end if 16: end for 6.8 EXPERIMENTS AND RESULTS

ζ j ← ζ j + log 10 (1 + χ j-k+1 ) • ω k //

HARDWARE SETUP : FORCE SENSING SHOES

The experiments involve the extraction of the CoP trajectory from each foot during locomotion. The hardware setup consists of a shoe equipped with eight pressure sensors for gait characterization. The shoe was developed using Teckscan piezo-resistive sensors. These sensors have the property to change their resistance when a force is applied. They are low cost sensors and can be easily deployed in any type of applications that require force measurement. The sensors are fixed at the bottom of the insole in the points of maximum load. The design and development of the force sensing is described in Chapter 5. The CoP is determined by weighting the magnitude of the force by the position of the sensor to resolve all pressures into one single point. Data from the sensors is acquired and transmitted via a wireless connection to a remote computer at a rate of 50 Hz. The computation of the CoP trajectory is performed using a customized software interface developed on MATLAB.

EXPERIMENTAL PROTOCOL

To validate our early fall detection approach, we conducted an experimental protocol divided in two phases : 1) the normal walk and 2) the altered walk.

THE NORMAL WALK

During the first phase, five adults were asked to perform a normal walk of 20 stances wearing the Force Sensing Shoe. The subjects have an average height of 172 cm and average weight of 93.8 Kg. They all have the same shoe size 44. The shoe was calibrated for zero weight before the test. During the test, each walk was repeated 5 times. A total of 25 data sets representing the vectors coordinates of the CoP were collected. The mean of each of the 5 data sets representing the walk of each subject is determined. The results are five data sets representing the walk of the five subjects. The amount of sway for each subject is determined using the ratios r j for the j th stance as given by Equation 6.3. Recall that the amount of sway is minimum during normal walking for sound adults and the gait lines are quasi-similar. The thresholds Th L and Th M for each of the five data sets are computed. The common thresholds Th L and Th M for the five walks can be obtained by computing their mean. Statistics of the "normal walk" are shown in Table 6.1. The altered walk consists of performing six scenarios with simulated excess sway in the M-L plane. The same five subjects were asked to increase their sway at specific stances during the walk to simulate the sway of an elderly person in a manner that he is about to fall. The subjects were instructed to perform sways on the right side. Since our early fall detection method is applied on each foot severalty, the same scenarios can be considered if the sway happens on the left side. At the time of the research, we didn't consider the correlation between both feet. The excess sway χ j is computed using Equations 6.6a and 6.6b. The six scenarios for the "altered walk" are shown in Figures 6.3 to 6.8 (scenarios 1 to 6). Three scenarios represent sways at different stances of the walk such as those represented by scenarios 1, 3 and 6. For example, in scenario 1, the subjects sway in the 2 nd stance at the beginning of the walk. In scenario 3, the subjects were instructed to sway at the beginning (2 nd ) and the end (19 th ) of the walk. In scenario 6, the subjects performed sways at the beginning (2 nd ), the middle (10 th ) and the end (19 th ) of the walk.

Consecutive sways are also considered. They are illustrated by scenarios 2, 4 and 5. The reason behind such scenarios is to highlight the impact of the sliding window coefficients and the output alarms. For example, in scenario 2, two consecutive sways are performed at the beginning of the walk at the 2 nd and 3 rd stances. In scenario 5, the subjects were instructed to perform the three sways during the last stances. In scenario 4, we combined consecutive sways with intermittent sways. 6.2 shows the results obtained for the five subjects during the experiments. The number of alarms for each subject is recorded using the conditions given in Equations 6.13 taking into consideration different risk levels R L in the lateral side for each scenario.

The objective is to determine the best R L for all subjects that minimizes the number of false positives and false negatives alarms for the six possible scenarios. It can be noticed that the number of alarms is high when R L is close to the normal threshold Th L . This is due to the number of coefficients ζ j obtained from the spatio-temporal sliding window correlation. To remove the excess of false alarms, the R L is increased gradually taking into consideration the amount of sways r j of abnormal stances during the walk. As a result, when R L increases, the number of alarms decreases. The choice of R L is therefore important. It must not exceed the amount of maximum sway recorded and should minimize the false alarms during the walk. Thus, the optimal R L considered for these scenarios happens to fall between 0.256 and 0.27.

Moreover, Table 6.2 shows the high and low risk indicators computed for all subjects during each walking scenario. It can be seen from table 6.2 that these indicators are also affected by the choice of the risk level R L . According to Equations 6.21 and 6.22, R L acts on the amount of sway to determine the low and high risk indicators. In fact, when R L is increased, RS K l is increased and RS K h is decreased and vice versa. The total risk of falling is the sum of RS K l and RS K h . The total risk RS K t is relatively high in scenarios where the sway amount is increased such as in scenario 4, 5 and 6. Moreover, the risk is dependant on the subject's manner of walking. Although, Table 6.2 shows the risk for the five subjects as a group, the risk can be computed for each subject as it can be different from one subject to another. The determination of the risk of falling is obtained at the end of the walk.

At the same time, table 6.2 shows a comparison between the linear and the logarithmic spatio-temporal correlations in terms of number of alerts. Recall that the logarithmic func-

CONCLUSION

The development of an early fall detection method has been the object of this chapter.

Our proposed approach takes the CoP trajectory of the stance phase of the gait cycle as an input gait characteristic to determine the amount of sway in the M-L plane. The output are alerts generated to warn the patient from a potential fall. The main objective is to demonstrate that imbalance motion is due to consecutive wrong stances that can induce a fall incident.

Our proposed method is currently designed to work on each foot independently, but it can be adapted to work on both feet together. Fall alerts are generated using both linear and logarithmic spatio-temporal correlations. When results are compared, it showed a drop in the number of alerts generated by the logarithmic correlation eliminating the hassle on the subject. The experiments shown are sufficient to validate the method, however more scenarios are needed to be carried to validate it on elders that exhibit M-L sway during walking. The estimation of the risk of falling is also a good indicator for health professionals who might be interested to apply this method in a clinical setting. Finally, the method described in this chapter is easy to implement in real-life with real time processing. In fact, the method can be hard coded and downloaded on the wearable electronic board microcontroller of our proposed Force Sensing Shoe previously described in Chapter 5.

BACKGROUND ON FLOOR AND CARPET SYSTEMS

INTRODUCTION

Knowing that elderly people have a higher risk to fall at homes, researchers and scientists are developing systems that are able to detect and prevent these incidents. These systems employ different types of sensing technologies that can be deployed in a domestic environment or an elderly care facility. These systems would help complement the shortage in health-care practitioners that are facing developed countries. With reference to our generic classification introduced in Chapter 3, Section 3.5, ambient systems belong to both Fall Detection (FD) and Fall Prevention (FP). Existing ambient systems in these groups differ by their type of sensors as well as their processing algorithm with respect to the context of the application (FD or FP). In this chapter, we provide a summary of relevant researches on floor and carpet systems that employ different types of sensors namely, pressure sensors, optical sensors, vibration sensors, and radio-frequency sensors.

7.2 RELATED WORK

SYSTEMS WITH PRESSURE SENSORS

Pressure sensors are the type of sensors used in most existing ambient systems. They are used for localization and gait analysis. There are many types of pressure sensors (resistive, capacitive, piezo-electric, etc.). The following studies exploit different types of pressure sensors in their design concept of ambient systems. The surveyed systems are summarized in Table 7.1.

[ [START_REF] Al-Naimi | Advanced approach for indoor identification and tracking using smart floor and pyroelectric infrared sensors[END_REF] proposed an advanced approach for indoor identification and tracking. They suggested a system using two types of sensors. The first sensor is a Pyroelectric Infrared Sensors PIR sensor, mounted on the ceiling. This sensor is used to detect the body shape of the person in the room. The second type of sensor is a matrix of pres-sure sensors. The sensor used is a polymer thick film sensor (FSR 406 from Interlink).

The choice is based on cost/performance ratio. Pressure sensor distribution and resolution are chosen taking into consideration several factors : required accuracy, identification and tracking problem, cost and hardware complexity. The distribution is a 4 x 4 array of 16 FSR sensors located in 0.5 x 0.5 x 0.022 m on a playwood floor tile. A Medium Density Fibreboard (MDF) board is added on the top of the sensors. The parameters that could be extracted from the proposed smart floor are : person's speed, direction of motion, position at the time of crossing the FOV (Field of View) of the PIR sensor. The authors also mentioned that more features can be extracted such as the footstep profile and gait. As we are interested in the smart floor results, the centroid of the footstep is measured using this smart floor. Calculating the difference between the measured centroid and the real centroid would give the error. This smart floor could measure the centroid of the footstep with an error of 12 cm.

In an other paper, [START_REF] Al-Naimi | Indoor identification and tracking using advanced multimodal approach[END_REF] were able to identify and track people using their proposed multi-modal approach. The paper focuses on the data analysis that are gathered from the smart FSR floor and the PIR sensor. The gait data analysis process uses region growing algorithm to detect the gait from sensors. Data processing uses different stages : pre-processing, data-level fusion, feature extraction, feature level fusion, identification. To extract features from the smart floor, the system uses three consecutive footsteps. One of the extracted features is the gap between the right and the left foot. It is obtained by calculating the distance between the second footstep and the line connecting the first and the third. The experiments are done with a test bed having 16 tiles. Ten persons (9 males and 1 female) participated in the experiments. The tests were carried in two phases. In the first phase, the persons had to walk in a precise way. In the second phase, they walked freely. Results show that combining both sensors data has led to localize and identify the person.

In these two papers, the authors present a system that is the most suitable for gait analysis. The resolution of the sensors is good enough to extract all the required parameters. However, this technology presents two major problems. At high spatial resolution, the distance between pressure sensors is small. When a load is applied on a sensor, the nearby sensors will also be affected. This will generate noise leading to a shadowing effect. Another problem of such technology is the floor indentation. That is when the physical aspect of the floor changes after applying load many times. This will cause the wood on the top of the sensors to exert a pressure that does not exist in reality. The processing algorithm will not be able to differentiate between a real load and the load generated by indentation.

The resolution of the sensors presented in [START_REF] Al-Naimi | Advanced approach for indoor identification and tracking using smart floor and pyroelectric infrared sensors[END_REF] solves the problem of pressure overlapping which is cause by narrow adjacent pressure sensors. Moreover, the design has a low price (32 pound /unit) but the cost increases with the number of sensors per tile and the number of tiles. The proposed solution didn't highlight the fusion between the ceiling and floor sensors displaying the results separably. Although the proposed design could be used for gait analysis with very low accuracy due to the low resolution (2 to 6 sensors are activated during one footstep), it was only exploited for footstep centroid calculation and for identification.

[ [START_REF] Morgado | Low-power concept and prototype of distributed resistive pressure sensor array for smart floor and surfaces in intelligent environments[END_REF] proposed a low-power concept and a prototype of distributed resistive pressure sensor array for smart floor and surfaces in intelligent environments. These sensors are combined into matrix with a scalable resolution. The main focus is on the power consumption of the sensor's matrix. The solution is based on connecting the end of the columns of the matrix to a multiplexer controlled by a Data Acquisition (DAQ) board.

The DAQ is connected to the rows of the matrix. It enables one column at a time using the multiplexer and read from the rows. A method for screening the sensors is discussed. It is called Zero Potential Method (ZPM). As indicated in the article, the ZPM is unreliable in case of more than one sensor is pressed at the same time. One of the biggest advantage is that the solution implement a power saving mode that will make the matrix act as a single sensor if no footstep is detected. The article provides a great method for reading sensors and saving power, but it does not provide a method for gait analysis. The power saving method can be used on any array of sensors of any kind.

[ [START_REF] Cheng | Recognizing subtle user activities and person identity with cheap resistive pressure sensing carpet[END_REF] designed a cheap resistive pressure sensing carpet for recognizing subtle user activity and person identity. The hardware used for sensing is formed by an Electro Static Discharge (ESD) foam with carbon powder. When pressure is applied on the floor, the density of the carbon increases and the resistance drops. Integrating a matrix of wires into this floor will let the researchers measure the resistance at the crossing points between columns and rows. The spacing between sensors is 3 x 3 cm with a resolution of 32 x 32 on a 60 x 60 cm floor tile. The experiments were carried on eleven (9 males, 2 females) healthy subjects. Each subject has to perform 7 activities. Each activity is repeated 20 times. Data frames were collected for processing. The system identifies the user activity with a 75% to 89.3% accuracy. One of the limitations of such system is that the person must not change the location of his feet while doing the requested activity. Moreover, this system is intended for activity recognition and cannot be applied to gait analysis due to noise and standing requirement.

[ [START_REF] Chang | Ubi-floor : Design and pilot implementation of an interactive floor system[END_REF] developed and implemented their interactive Ubi-Floor. The main goal is to create a floor that interacts with the user for multimedia and many other uses. It uses pressure sensing technology. A 47" LCD screen is used with tempered glass as protection. Pressure sensor are placed on the four corners of a grid (2.6 x 2.2 m). Gathering data from the sensors, the system can detect the location of the user. A RFID reader is placed in the room with 9 m detection distance to detect the identity of the user. A mouse pointer follows the user on the screen. A delay of 1 second indicates a mouse click. The size of the grid with only 4 sensors is a very bad resolution for gait analysis.

[ [START_REF] Contigiani | Indoor people localization and tracking using an energy harvesting smart floor[END_REF] proposed an indoor people localization and tracking approach using an energy harvesting smart floor. The authors claim that a well-functioning floor should have some requirements such as low cost, ease of installation, and invisible to the user, etc. They developed a smart floor using capacitive sensors. These sensors are installed between solid wood and the wooden part of the floating floor. The distance between sensors is 180 mm. The sampling frequency of the system is 7 Hz. The advantage of this system is its low power. In fact, the floor will be pressed to be activated. Similarly, the proposed system is not used for gait analysis.

[ Jideofor et al., ] proposed a system with pressure sensors (FlexiForce A401) as they are low cost and easy to use. Eight pressure sensors are embedded into the tile. The sensors are used to extract the distribution of the pressure as well as the Center of Pressure (CoP). Ethylene Propylene Diene Monomer (EPDM) rubber is used as a puck to concentrate the weight on the pressure sensors. The article is a systematic description of the design. No experiments were carried to validate the system.

The use of pressure sensors in high resolution is not advantageous. In fact, systems using pressure sensors send a huge amount of analogue data which require complex electronics for acquisition and processing. Moreover, pressure sensors at high resolution can cause shadowing and indentation. This problem is usually found in pressure resistive smart walkways. The shadowing effect is when the size of the foot as extracted by the system is larger that the foot itself.

The research lab INRIA, Nancy-France, proposed a smart floor for elderly tracking and fall detection [START_REF] Daher | Elder tracking and fall detection system using smart tiles[END_REF]. Two sensors technologies are used in this floor : Four force sensors are placed on the corners of each tile and one accelerometer is placed at the center of the backside of the tile. Pressure sensors are used to measure pressure forces on the tile. These forces are used to localize the subject and determine the way this subject is standing. These pressure sensors helps finding the posture of the person by combining pressure forces, pressure time, and the area of the pressure. A matrix of LED display is embedded in the tile to display the weight. The posture detection using only force sensors succeeded to detect posture when walking, standing and sitting. The only problem is the differentiation between falling and lying on the ground. For this reason, the 3-axis accelerometer is introduced. This system shows a high rate of success in posture detection after introducing the accelerometer. Each tile is 60 x 60 cm. These tiles are implemented in a smart apartment (104 tiles). The system is developed for fall detection and user tracking. By using 4 force sensors and 1 accelerometer limits the possibility of extracting gait parameter such as the base of support.

SYSTEMS WITH OPTICAL SENSORS

Optical sensors are also found in the literature of ambient systems. For instance, [START_REF] Feng | Floor pressure imaging for fall detection with fiber-optic sensors[END_REF] used fiber optic sensors to capture pressure image for fall detection in bathrooms. The proposed system consists of embedding fiber optic cable as a pressure sensor. When pressure is applied on the fiber, the receiver detects the drop in power of the transmitted signal. This variation in voltage indicate the power of the applied pressure. When a heavy load is detected indicating a lying posture, the system has to decide wether it is a normal or a risky fall. If the person is still lying, an alarm is triggered after a preset time. Experiments were conducted on eight subjects (4 males and 4 females) with different height and weights. The system shows 100% accuracy on detecting lying per-son. It confuses sometime between standing and sitting situations. This type of sensors is ideal for wet environments such as a bathroom as mentioned by the authors. In this system, the floor is considered as one sensor which makes it difficult to extract gait parameters. Moreover, the fiber is subject to deformation when load is applied many times causing noise in the system. [Yun, 2011] proposed a system for user tracking and identification. Their system is called the UbifloorII. This system uses the walking pattern of the person for tracking and identification. The sensor technology used in this system is a photo interrupter. Photo interrupter sensors are infrared sensors having a digital output. This sensor outputs a logical "1" (5 V) when an object is its the detection range, otherwise it outputs a logical 0 (0 V). These sensors are embedded in a wooden tile, each tile having 64 sensors. The dimension of this wooden tile is 30 x 30 cm. A Controller Area Network (CAN) bus is used to connect the tiles to the computer. The computer extracts features from the data and recognizes the user using a neural network. For testing, a walkway is constructed using 12 x 2 wooden tiles. Five parameters are used for testing : coordinates of the back-most sensor, coordinates of the center of footprint, number of active sensors, heel-strike time and toe-off time. The results of the system shows a 90% accuracy of user recognition.

SYSTEMS WITH VIBRATION SENSORS

Ambient sensors such as accelerometers, vibration and sound sensors are commonly used to detect falls. They are used to detect vibrations caused by falling bodies. These sensors are deployed in abient systems to collect and analyze data to detect a fall event.

[ [START_REF] Zigel | A method for automatic fall detection of elderly people using floor vibrations and sound proof of concept on human mimicking doll falls[END_REF] proposed a method for automatic fall detection of elderly people using floor mounted vibration and sound sensors. The proposed method uses an accelerometer and a microphone placed close to the wall. Those sensors are attached to the floor by adhesive tapes. The first step is to find the vibration event from the collected signal coming from the microphone and the accelerometer. The second step is to distinguish between a person falling and a fall of another object using signal processing techniques.

Experiments were conducted on "Rescue Randy" mimicking human falls and four popular objects than can be found in our daily life. A total of 80 falls were performed. This algorithm showed a fall detection accuracy near 97%.

[ [START_REF] Werner | Fall detection with distributed floor-mounted accelerometers : An overview of the development and evaluation of a fall detection system within the project ehome[END_REF] developed a system for fall detection with distributed floor-mounted accelerometers. This system is used to detect falls inside homes. The system consists of a sensor box using accelerometers. In fact, accelerometers are sensitive to the vibration created by the human bones in contact with the floor. Each box is also connected to a central server via a wireless connection. The size of the box is 7 x 5 x 3 cm. The authors of this paper also assume that the vibration generated from a human fall differs from every other source. Each room contains 3 boxes. If a room is larger than 25 m 2 , an additional box is added. This system was tested with a dummy that simulated data gathered from young volunteers. The boxes were placed in the corners of the room so that they don't interfere with the residents. After a fall is recorded, 3 sensors detect vibration and the algorithm interprets the signal to take the decision. If it is a fall the system starts a timer and checks for any user activity (e.g. door opened, light switched on/off). If no activity is detected an alarm is triggered.

Systems using these types of sensors in ambient environments have proven their capability in fall detection. When it comes to early detection, the data generated by these sensors is not helpful since the vibration signal is detected after the fall has occurred.

Trying to extract gait parameters from these systems is also difficult with little number of sensors. Increasing the number of sensors inside the same room, lead to the problem of shadowing. That is when a vibration is detected by the other sensors cause high noise generation. Therefore, detecting a walk on the floor can be more difficult as the noise generated may be as high as a simple vibration of a stance.

SYSTEMS WITH RADIO-FREQUENCY IDENTIFICATION TECHNOLOGY

Many smart floor designs are based on RFID technology. This technology is used in systems focusing on object tracking and/or object/human identification. RFID technology is composed of two parts, the RFID tags and the RFID reader. Each tag has a unique identifier (ID) that can be read when scanned with the reader. The reader is an active device that powers the tag and reads the unique identifier.

[ [START_REF] Ramos | Timedomain uwb rfid tags for smart floor applications[END_REF] embedded RFID tags in big tiles (1 x 2 m) and used a mobile device to detect and localize the moving object that is carrying the mobile detector or the RFID reader. They tested three type of RFID tags : passive reflectors, chipless time-coded tags and semi-passive time-coded tags. [START_REF] Jung | Rfid footwear and floor system[END_REF] implemented the RFID technology for localization inside homes. The RFID tags are embedded in the floor with a distance of 30 cm between tags. The RFID reader is mounted on the footwear. While walking on the floor, the RFID reader detects the nearby tags and sends the information to a Webserver. At the server end, a map is created that represents the spatial distribution of the tags. The server receives the coordinates from the footwear and displays the location on the web page. A RFID reader is also mounted on the glove in attempt to recognize daily activity. Indoor and outdoor navigation based on RFID technology as an enabler for smart environments and smart cities is discussed in [START_REF] Goncalves | Smart environment technology as a possible enabler of smart cities[END_REF] and in [Gonc ¸alves et al., 2013]. The authors implemented a system in a way a human can localize himself in indoor and outdoor premises easily. The systems discussed in these papers integrate RFID tags inside floor tiles or on the streets while the reader is mounted on the person. The main purpose of such system is identification and localization.

[ [START_REF] Kang | Implementation of smart floor for multi-robot system[END_REF] implemented a smart floor for multi-robots navigation using RFID tags and a reader. The RFID tags are embedded in the floor and the reader is mounted on a robot. The main purpose is to use these tags to help the robot during indoor navigation. The robot navigate inside the room and learn from reading the tags to find the correct path. Similarly, [START_REF] Hvizdoš | Object identification and localization by smart floors[END_REF] embedded RFID tags in floor tiles. The resolution of the tags differs between places, where important places have a higher resolution. A Lego robot carried the reader. The experiments were conducted to test the location of 2 distributions of sensors.

Systems with RFID tags/reader technology are mainly used in researches for identification and localization purposes. In this type of applications, RFID tags are typically mounted on a fixed object (e.g. floor, asset), whereas, the reader is mounted on the moving subject (e.g. robot or human). This technology offers a cheap solution for localization and identification, and requires low power to operate. In the context of elderly falls, RFID technology could be suitable for activity monitoring, however, trying to deploy this technology in gait analysis, is difficult and almost unapplicable. In fact, gait analysis requires the collection of large amount of spatial-temporal and kinetic parameters. All the systems described previously are advantageous for localization and identification but cannot be used for gait analysis. This chapter focused on ambient systems using different sensing technologies for Fall Detection and Fall Prevention. These can be floor tiles or smart carpets deployed in the domestic environments. As we can conclude from this brief overview, that systems using vibrations/sound sensors and RFID are not practical for gait analysis in the context of Fall Prevention. However, systems using pressure sensor are more suitable for this type of applications. They can provide enough data to model human gait and locomotion, the reason why, most of medical systems found in gait labs use this type of sensors. Despite their wide use, these systems suffer from two main issues : The indentation and the shadowing effects, that should be taken care of in the design of such systems at high resolution. Systems using optical sensors such fiber optics have high cost and are application specific (e.g. fall in a bathroom). On the other hand, using small optical sensors on a floor tile will enable us to extract the required parameters for gait analysis. This amounts to detecting the footprint on the optical floor and thereby all the required parameters. In the next chapter (Chapter 8), we will describe the design concept and the development of our proposed Infrared Sensing Floor. This floor will be used to extract spatial-temporal gait parameters for gait analysis.

INFRARED SENSING FLOOR

INTRODUCTION

The second entity of our proposed WMFL v1.0 platform introduced in Chapter 3, Section 3.9 of this thesis, is the Infrared Sensing Floor. The latter belongs to the Ambient systems (AMB) sub-category of Fall Prevention (FP) systems, with reference to our proposed generic classification described in section 3.5 of Chapter 3. The focus of the present chapter is to describe the design and the development of our proposed Infrared Sensing Floor from the hardware perspective. Our designed Infrared Sensing Floor will be able to extract the footprint of any human in direct contact with the infrared plates. The algorithm of extracting the footprint is also described in section 8.4 of this chapter. The footprint will be used to determine the spatial-temporal gait parameters. The accuracy of our Infrared Sensing Floor is determined by comparing the Active Contact Area (ACA) with the Real Contact Area (RCA) of she footprint/shoeprint.

INFRARED SENSING FLOOR DESIGN CONCEPT 8.2.1 SYSTEM DESIGN REQUIREMENTS

The objective of our proposed Infrared Sensing Floor is to be able to extract the spatialtemporal parameters of the human gait. In the context of Fall Prevention, spatial-temporal parameters such as the step length/time, stride length/time, stance and swing times, walking speed, cadence, angle of feet, and Base of Support (BoS) are very essential in understanding the variability of gait in the objective of determining among other pathologies the risk of falling. The system must therefore answer specific constraints that should be taken into consideration in the design. First, the system must provide objective measurement of gait parameters. In other words, the system must ensure functions such as data collection, parameter extraction and data analysis. Data analysis is usually performed on a computer system because of the large amount of data that are collected from an ambient system such as the proposed. Second, the system should be extensible in the sense that it should be able to communicate with other systems such as the Force Sensing Shoe described in Chapter 5 or other indoor systems (e.g. dry contact sensors, motion detectors, power system, etc.). Third, the system should be scalable with respect to the surface of application and reconfigurable to cover corridors or flat surfaces inside indoor premises. Finally, the system must have a low cost relatively to existing gait analysis solutions typically found in clinical gait labs. Our proposed Infrared Sensing Floor system is non-intrusive and able to work during day and night times.

SYSTEM DESIGN ARCHITECTURE

From the hardware perspective, the proposed ambient system is an Infrared Sensing Floor designed under three main parts :

-The infrared sensing plates -The plate controller electronic board -The mediator electronic panel These parts are connected together and exchange data in a hierarchical way in order to extract the gait parameters. The system design architecture of our proposed Infrared Sensing Floor is shown in Figure 8.1. The main functions of these parts are described in the following paragraphs.

-The infrared sensing plates : These are the basic components of our proposed Infrared Sensing Floor. These plates are grouped next to each other to form an "I"-shaped straight walkway. This is where elderly people walk and the system extracts their gait parameters. Controversially to existing gait analysis systems that have typically a straight walkway, our proposed Infrared Sensing Floor can be reconfigured into "L"-or "H"-shaped walkways. This functionality is important to study gait during complex turns or activities. Moreover, the plates can also be distributed to cover a whole surface inside a room. Our Infrared Sensing Floor prototype has 8 infrared sensing floor plates. Each plate is equipped with an IRsensor matrix. The walkway has also a dummy type of plates. The dummy plates are simply made of wood and are of the same size as the infrared-sensing plates.

There are 4 dummy plates in our walkway prototype placed at the beginning at the end of the walkway. The reason behind this configuration is to allow correct measurement of gait parameters. In fact, according to [START_REF] Nordin | Basic biomechanics of the musculoskeletal system[END_REF], the first and the last steps are not considered during a walk test. That is when the person accelerates or decelerates during a normal walk. At the beginning of the walkway, the person climbs a short ramp of 1m long to start his gait.

-The plate controller electronic board : The plate controller is placed at the bottom of the each infrared sensing plate. It detects the changes occurring on the IR-sensor matrix. For instance, when the foot is placed on the plate, the infrared sensors in the affected region under the foot are activated. At this time, the plate controller logs the location (x-y coordinated of the sensors), the time (activation and deactivation times) and the status (ON/OFF) of the sensors and sends them for analysis. This information will help construct the footprint on the Infrared Sensing Floor at each foot contact. All the plate controllers are connected to each other using a 2-wire RS-485 network bus. The plate controllers access the RS-485 bus after being requested by the mediator panel.

-The mediator electronic panel : The mediator is at the center of the design architecture between the infrared sensing plates and the computer. It is connected to the plate controller electronic boards through the RS-485 network bus from one side and to the computer through its Serial-USB interface from the other side.

It has also a wireless communication interface made of Bluetooth and ZigBee modules to communicate with other systems such as the Force Sensing Shoe as a part of the WMFL v1.0 platform described later in Chapter 10. The mediator electronic panel ensures functions such as synchronization, communication and processing of data streams from the plate controller electronic boards and to the computer. At the time of the research, the mediator receives its commands from the computer. At the same time, the mediator commands the plate controllers to send their data. The mediator collects the data and sends it back to the computer for processing. The software of the mediator is currently being update to work separately from the computer.

The following section describes the hardware design and development of the Infrared Sensing Floor, namely the floor plate design with the IR-sensor matrix, the plate controller electronic board and the mediator electronic board.

INFRARED SENSING FLOOR DEVELOPMENT 8.3.1 INFRARED-SENSING PLATE DESIGN

The first part of the system design architecture is the infrared sensing plate. The electronic and the mechanical designs of this basic component are described in the following paragraphs.

FLOOR PLATE ELECTRONIC DESIGN

The electronic design of the plate must be conceived in a way to detect the footprint of a person on the floor. The sensing elements that must be used should be able to detect the footprint wether the person is standing or walking. The footprint will be later used to extract spatial-temporal gait parameters for further analysis. The electronic design of the floor plate is mainly composed of two layers, namely the sensor layer and the connection matrix layer. In the remaining of this thesis, we refer to these two layers as the infraredsensor matrix or IR-Sensor matrix.

-The sensor layer : The sensing element used in our design is an optical type sensor. It uses infrared light reflection to detect any object in its sensing range. It belongs to the family of reflective object sensors. It consists of an infrared Light Emitting Diode (LED) that emits infrared light that is detected by a phototransistor after being reflected on the surface of an object. In our case, the insole of the shoe is the reflective surface. We used in our design the QRD1114IR reflective sensor from Fairlchild semiconductor (www.fairchildsemi.com). The sensor has a relatively small sensing range requiring its installation on the same level of the contact surface. The sensor provides an analogue output with respect to the distance of the object. Moreover, the sensor has a shield for daylight filtering. The compact size of the sensor makes it possible to be fit in our floor plate at high resolution. To ensure an accurate footprint detection, each floor plate has 225 infrared sensors (15 x 15). These sensors are hardwired in a matrix-like connection to the plate controller electronic board. Figure 8.2 shows the sensor and its packaging dimensions (top and profile views). In other words, the sensors are equidistant from each other. The distance between sensors is 3cm in both directions. Even if the sensors does not belong to the same plate, the distance is always respected. In fact, taking the size of the floor plate (45 x 45cm) and the package of the sensor (6.1 x 4.39mm), the optimal resolution for the footprint detection is 15 x 15 at 3cm. Moreover, distributing the sensors at this resolution is compatible with the analogue I/O ports of the microcontroller. In this case, we have 15 rows and 15 columns, thereby requiring 2 analogue input ports in the microcontroller. These ports are available on the PIC18F45K22 that we used for processing and control in the core of the plate controller electronic board. The process of scanning the sensors is being done by the microcontroller.

The operation is as follows. The microcontroller activates a column and scans for an active line. If an active line is found, then the active sensor is the intersection between the line and the activated column. Moreover, the process of scanning on column at a time reduces the power consumption inside the matrix. The power consumption inside the matrix is 15 x 30mA instead of 225 x 30mA, where 30mA is the current consumption of one infrared sensor and 15 is the number of sensors per column. The sensor distribution on the connection matrix is shown in The mechanical design of the floor plate is particular in the sense that it should cover both feet of a person. In other words, the person should be standing or walking without difficulties on the plates and therefore on the walkway. In fact, the distance between the outer extremities of both feet is less that 45 cm for an average person during walking and standing. This distance was determined experimentally during several walking tests of different persons on a graph roll of paper. Moreover, the plate mechanical chassis should be designed to hold the weight of the person without any deformation to the plate or to the IR-sensor matrix. Taking into consideration the above requirements, the floor plate mechanical is composed of three layers :

-The mechanical chassis layer : It is the first layer of the floor plate. The mechanical chassis is responsible for holding the person. It is made of iron metal to support heavy weights (> 500Kg). The size of the mechanical chassis is 45 x 45cm, which fits the normal distance between the feet. The chassis is raised about 10cm from the ground using eight 1 x 1cm square metal legs fixed on the outside and on the inside corners. This height makes room to easy access the plate controller and the connection cables. From the inside, the bars have a width of 2cm and a thickness 8mm making a total of 9 empty squares. The design of the mechanical chassis resembles the # symbol. This design is at the same time very robust and light weight. -The surface contact layer : This layer is made of black plexiglas. It has a thickness of 5mm slightly higher than the height of the sensor (4.65mm). The size of this layer is 45 x 45cm. The plexiglas layer is drawn using Autocad software. The plexiglas is engraved using a laser cut machine according to the package dimensions of the sensors.

Finally the infrared sensing plate was assembled using screws and nuts. The different layers were placed on top of each other by order. The decomposed design of the floor plate is shown in Figure 8.5-Top. The assembled design of the infrared sensing plate is shown in Figure 8.5-Bottom.

Mechanical chassis layer (metal)

Insulation layer (Rubber) Matrix-connection and sensor layer (PCB)

Surface contact layer (Plexiglas)

Floor plate assembly Despite their little number (8 plates in our proposed prototype), the number of the infrared sensing plates can be extended to cover a larger surface. In fact, the maximum allowable load units on the RS-485 network bus is 256 covering in this case a surface of 52m 2 , taking into consideration the physical dimensions of the each plate (45 x 45cm in our case). In our prototype, the infrared sensing walkway has a length of 3.6m (8 x 45cm) and a width of 45cm. Moreover, different configurations are possible such as L-shape and H-shape.

PLATE CONTROLLER ELECTRONIC BOARD

The second part of our proposed Infrared Sensing Floor is the plate controller electronic board. The design architecture of the plate controller is shown in Figure 8.6. The architecture is composed of several functional blocks. These blocks are the IR-sensor matrix driver block, the network interface block, the processing and control block and the power interface block. The hardware implementation of the plate controller electronic panel is illustrated in Figures C.6

NETWORK INTERFACE BLOCK

The network interface is used for communication between the plate controller electronic board and the mediator. It allows the plate controller to send and receive data over the RS-485 bus. Hierarchically, the plate controller electronic board exchanges data with its immediate superior that is the mediator electronic panel. The network interface is managed by the processing and control block using its asynchronous serial interface (UART). The network interface circuit uses the MAX485 signal amplifier to drive the RS-485 network bus. The circuit is shown in Figure C.6 of Appendix C.

PROCESSING AND CONTROL BLOCK

This block is the core of the plate controller electronic board. It is responsible for commanding the IR-sensor matrix driver and collecting the statuses of the infrared sensors. Sensory information is processed into data streams and sent to the mediator over the RS-485 network bus. This block is a microcontroller unit. The microcontroller unit used in the plate controller is the PIC18F45K22 manufactured by Microchip. In the plate controller, we made use of the microcontroller analogue and digital Input/Output ports to drive the IR-sensor matrix. The microcontroller is programmed using the In-Circuit Serial Programming (ICSP) interface available in the circuit design of the plate controller electronic board. The microcontroller unit and its connection to the IR-sensor matrix is shown in the design of the mediator circuit schematic illustrated in Figures C.6 of Appendix C.

POWER INTERFACE BLOCK

The power interface at the level of each plate controller electronic board is simply a circuit composed of two capacitors. The capacitors ensure the filtering of the 5 VDC supply voltage delivered from the main power supply of the system. The 5 VDC input voltage supplies the IR-sensor matrix through the power interface circuit of the plate controller. The power interface circuit is shown in the schematic of plate controller The main power supply of the Infrared Sensing Floor is located inside the enclosure with the mediator electronic panel. The power supply ensures the delivery of a 5V DC voltage to all the electronic components including the IR-sensor matrix, the plate controller electronic boards and the mediator electronic panel.

MEDIATOR ELECTRONIC PANEL

The third part of our proposed Infrared Sensing Floor is the mediator electronic panel. The design architecture of the mediator is shown in Figure 8.7. The architecture is composed of several functional blocks. These blocks are the downstream network interface block, the upstream network interface block, the wireless communication interface block, the real time clock block, the processing and control block, the command and visualization interface block, and finally the power interface block. These blocs are described in details in the following paragraphs. The hardware implementation of the mediator electronic panel is illustrated in Figures C.8, C.9 and C.10 of Appendix C.

DOWNSTREAM NETWORK INTERFACE BLOCK

The downstream network interface allows the mediator to send its commands to the plate controller electronic boards and collect their sensory data. It connects the mediator to the 2-wire RS-485 network bus. The interface is managed by the processing and control block using its asynchronous serial interface (UART). The downstream network interface used is the "RS485 click" module from Mikroelektronika (www.mikroe.com). The module is integrated in the circuit schematic design of the mediator panel illustrated in The upstream network interface allows the mediator to communicate with the computer. Hierarchically, this interface receives the commands from the computer and sends it the data streams collected from the plate controllers. It connects to the computer through one of its USB ports. The upstream network interface is managed by the processing and control block using its asynchronous serial interface (UART). The upstream network module used is the "USB-Serial click" from Mikroelektronika (www.mikroe.com). The mediator electronic panel has a command and visualization interface for manual control and debugging of the system. The interface is composed of a five-buttons keypad for command and a Liquid Crystal Display (LCD) for visualization. The interface gives physical access for more configuration options and monitoring. The interface is also managed by the processing and control block. This command and visualization interface is integrated in the circuit schematic design of the mediator panel illustrated in Figure C.9 of Appendix C.

PROCESSING AND CONTROL BLOCK

This block is the processing core of the mediator electronic panel. It is responsible for the processing and synchronization of data streams from the various communication interfaces. Commonly, this block is composed of a microcontroller unit. This unit is programmed to receive commands from the computer and send them to the plate controllers. Reversibly, data streams containing sensory information are collected from the plate controllers and sent to the computer for parameter extraction and further processing.

Similarly to the plate controller and the wearable electronic boards, the microcontroller unit used in the mediator electronic panel is the PIC18F45K22 manufactured by Microchip. This microcontroller is used in all three electronic boards for compatibility. In the mediator, we made use of the microcontroller serial interfaces ( 2

FOOTPRINT EXTRACTION ALGORITHM 8.4.1 PRINCIPLE OF OPERATION

There are different methods of extracting the spatial-temporal gait parameters. These are dependants on the sensing technology that is being deployed in the system. In the case of our proposed Infrared Sensing Floor, the basic element used for determining these parameters is the footprint. Recall that the footprint is the Active Contact Area (ACA) between the foot and the IR-sensor matrix. When the foot is placed on the floor plate, the infrared sensors in the contact area are activated forming a group of active sensors. The footprint extraction can be realized using a clustering algorithm. Sensor data being collected from each infrared sensing plate, represent a matrix of active and inactive sensors. The clustering process will separate active sensors into different group of sensors called clusters having the same ID number. We adopted a simple, yet powerful algorithm that is very specific to our platform. Despite of being specific, there are many parameters that can be adjusted to fit any change in the system. The core concept of the algorithm is to find the neighboring active sensors and add them to the same cluster. In other words, the algorithm takes every sensor that is represented by a binary "1" in the input matrix, and search among its direct neighbors for another active sensor. We define S as the input matrix of MxN elements, where M and N being the number of sensors in each row and column respectively. Usually M and N are equal, however, in our case we considered the whole floor as a single matrix thus taking M = 15 and N = 120, with s i, j being the status of each the sensor at position (i, j). The matrix S is defined in Equation 8.1 as follows :

S = s i, j ∈ {0, 1}, 0 i M -1, 0 j N -1 (8.1)
Since an active sensor may be surrounded by other active neighbors, we define N as the vector of adjacent neighbors a l,k i, j to the active sensor s i, j . N is defined in Equation 8.2 as follows :

N = s i, j-1 , s i, j+1 , s i-1, j , s i+1, j , s i-1, j-1 , s i-1, j+1 , s i+1, j-1 , s i+1, j+1 (8.2) = a l,k i, j = s i+l, j+k , -1 l 1, -1 k 1
In the process of adding the group of active sensors in one cluster, we define d s i, j , a l,k i, j

the distance from the active sensor s i, j to the adjacent neighbor a l,k i, j in Equation 8.3 as follows :

d s i, j , a l,k i, j =        D if l = 0 or k = 0, l 0 and k 0 D √ 2 otherwise ; (8.3)
Figure 8.9 illustrates the sensor s i, j with its adjacent neighbors a l,k i, j and their corresponding distance inside the matrix S .

si,

j S = M x N : IR-Sensor Matrix s0, 0 s0, 1 s0, N-1 s1, 0 sM-1, 0 sM-1, N-1 M Rows N Columns si, j-1 si, j+1 si-1, j si-1, j+1 si-1, j-1 si+1, j+1 si+1, j-1 si+1, j IR-Sensor 𝑎 𝑖,𝑗 -1,0 𝑎 𝑖,𝑗 -1,1 𝑎 𝑖,𝑗 -1,-1 𝑎 𝑖,𝑗 0,-1 𝑎 𝑖,𝑗 1,-1 𝑎 𝑖,𝑗 1,0 𝑎 𝑖,𝑗 1,1 𝑎 𝑖,𝑗 0,1 D D 𝐷 2 si, j si, j-1 si, j+1 si-1, j si-1, j+1 si-1, j-1 si+1, j+1 si+1, j-1 si+1, j 𝑎 𝑖,𝑗 -1,0 𝑎 𝑖,𝑗 -1,1 𝑎 𝑖,𝑗 -1,-1 𝑎 𝑖,𝑗 0,-1 𝑎 𝑖,𝑗 𝑗 1,-1 𝑎 𝑖,𝑗 1,0 𝑎 𝑖,𝑗 1,1 𝑎 𝑖,𝑗 0,1 D D 𝐷 𝐷 2
Neighborhood FIGURE 8.9 -Neighborhood of sensor s i, j

CLUSTERING ALGORITHM

The clustering algorithm starts by setting the GroupID to all active sensors (s i, j = '1 ) to -1 using the function SetAllSensorGroupID(). This function takes as input the list of all active and inactive sensors in the matrix S . The preset GroupID will help determine if a sensor has been scanned or not. This is essential in the scanning procedure as the algorithm won't run into the same sensor twice. The first cluster of active sensors has a GroupID = 0. The clustering algorithm starts by picking an active sensor with GroupID = -1. Then, the function SearchForNeighbor() is called. It is a recursive function used in the clustering algorithm that searches for any active sensor in the neighborhood of the current sensor being processed. In other words, the function is executed for all active sensors at distance D and D √ 2 having a status s i, j = '1 . The function reruns on each active neighbor until no active neighbor is found. The function takes as input the GroupID and the position of the active sensor being processed. In our case, one of the two conditions may be valid : 1) the tested sensor is a active neighbor or, 2) the tested sensor is active but it is not a neighbor. If the first condition is true, the algorithm assigns the same GroupID using the function ChangeGroupID() to this sensor, and the function SearchForNeighbor() is rerun again for this sensor. If the second condition is true, then the GroupID is incremented and the process starts over for another cluster of active sensors. The second condition is verified using the function CheckForRemaining(). Finally, if no active neighbors were found and the algorithm had run through all the sensors having the GroupID = -1, the algorithm stops and exits. In this case, the algorithm returns a list of sensors having each a different GroupID. The sensors having the same GroupID are gathered into clusters that correspond to the active area representing a footprint. Those clusters are passed to another section of the software for extracting spatial-temporal gait parameters. The pseudo-code of the clustering algorithm is shown in Algorithm 2.

Algorithm 2 Clustering Algorithm for Footprint Extraction

Input: S = s i, j ∈ {0, 1}, 0 i M -1, 0 j N -1
The for each s i, j in S do 4:

if (s i, j = '1 ) then for each s i, j = '1 do 10:

if (d(s i, j , a l,k i, j ) = D) then 11:
ChangeGroupID(a l,k i, j ) ; //assigns groupID to all active adjacent sensors at distance D 12:

SearchForNeighbor(GroupID, a l,k i, j ) ; //searches for active neighbors to the adj sensor 13:

else if (d(s i, j , a l,k i, j ) = D √ 2) then 14:
ChangeGroupID(a l,k i, j ) ; //assigns groupID to all active adjacent sensors at distance D √ 2

15:

SearchForNeighbor(GroupID, a l,k i, j ) ; //searches for active neighbors to the adj sensor This algorithm is run on each packet received from all the plate controllers. The packet is composed of 8 frames having each the status of the 8 IR-sensor matrices. The packet contains therefore the M x N matrix having the statuses of all the sensors of the Infrared Sensing Floor.

EXPERIMENTS AND RESULTS

EXPERIMENTAL SETUP

At this stage of the research, the prototype of the Infrared Sensing Floor has been set for testing. Each infrared sensing plate was assembled separately taking into consideration the four layers discussed previously in the design. The sensors were being welded on the IR-sensor matrix PCB. The plate controller electronic board was also fixed at the bottom side of the IR-sensor matrix between the rails of the metal chassis. Recall that each plate in our floor has its own plate controller electronic board that interfaces with the RS-485 network in a bus-like topology. The RS-485 network bus reaches the mediator electronic panel. The latter is being connected to a computer where the WMFL v1.0 Software Application is running (see Chapter 9). We carried a sequential programming for all plate controller electronic boards considering that each one has a unique address on the RS-485 bus. Addresses from 1 to 8 were assigned to the plate controller electronic boards. Listing D.2 in Appendix D shows the code downloaded on each plate controller. The IR-sensor matrix underwent calibration regarding the amount of ambient light to be filtered in order for the infrared sensors to be operational. In fact, despite their ambient light filtering shield, the sensors are affected by daylight. This issue was taken care of by defining a threshold over which the infrared sensors become operational. The threshold is also fixed in the microcontoller software. Similarly, the microcontroller of the mediator electronic panel was programmed. Listing D.3 in Appendix D shows the code downloaded on the mediator electronic panel. The plates are now installed on the ground next to each other along with the dummy plates in a "I"-shaped walkway. After being requested by the mediator, each plate controller sends its data frame over the RS-485 bus. Data frames are assembled into data packets at the mediator panel and transmitted to the computer at a rate of 3.8 Hz. At this rate, walking at a normal speed can be easily detected. In fact, [START_REF] Ji | Frequency and velocity of people walking[END_REF] found that people walk with an average frequency of 1.8 to 2 Hz, therefore, picking up data from a 3.6 m walkway at 3.8 Hz is sufficient. After being collected on the Software Application, data packet undergo parsing where the footprint extracting algorithm is applied. Footprints are displayed on the Software Application Graphical User Interface (GUI) in real time.

EXPERIMENTAL PROTOCOL

To test the operation of our infrared sensing plates as well as its precision in determining the spatio-temporal gait parameters, we conducted a simple experiment of two phases. The purpose is to measure the accuracy (% error) between the Real Contact Area (RCA) of the foot with the ground and the Active Contact Area (ACA) of the foot with the IRsensor matrix. For this purpose, a group of 12 voluntary students (8 males and 4 females) of different foot sizes (from 37 to 47 EU) and types participated in the experiment. All subjects have their shoe size equal to their foot size. Tables 8.1 and 8.2 show the statistics of the recruited population. Subjects S4 and S7 happen to have flat feet. In the first phase, the students have to stand with bare feet on one of the infrared sensing plates. In the second phase, the students have their own shoes on. During both phases, the software will display the footprint/shoe print using the footprint extraction algorithm and computes the Active Contact Area (ACA). In fact, the average ACA (Avg ACA) is determined taking into consideration 10 different locations and directions of the feet on the infrared sensing plate. To validate our measurements, the students stood on a graph paper where we measured the Real Contact Area (RCA). The footprint and the shoe print Real Contact Areas (RCA) are both traced on the graph paper where the number of 1cm 2 squares is determined (half squares are added together), and compared to the Active Contact Areas (ACA) given by the output of our algorithm. This process was also verified by computing the RCA on a computer. Figure 8.10 shows the layout of the RCA and the ACA on the infrared sensing plate.

RESULTS AND DISCUSSION

The results of the measurement of the ACA and the RCA for both bare feet and shoes experiments are presented in Tables 8.3 and 8.4 respectively. Results are presented using statistical variables such as the range ([min,max] ACA), the average (Avg ACA) and the standard deviation (std ACA) for each subject. By comparing the RCA, it is clear that the RCA of a bare foot is less than the RCA of the shoe. However, two exceptions were found with subjects S4 and S11. Subject S4 has a flat foot. When he was asked, he admitted that his shoe is straining on his foot. For subject S11, the lady was wearing a balerina made of tissue and a small sole at the bottom. In general, this type of shoes has a small sole. Similarly, the Avg ACA is larger for a bare foot than for the shoes for subject S4 and almost the same for subject S11. Recall that the Avg ACA is determined after a series of random foot/shoe locations on the infrared sensing plate. For almost all subjects, the Avg ACA is larger than the RCA. The reason is that any active sensor is considered to have a area of 9cm 2 . This is causing the difference between the RCA and the ACA identified by the measurement error (M. Error). This error has to be as minimum as possible to validate the resolution of the sensors and be able to extract the spatio-temporal gait parameters accurately. Thus, the accuracy of the measurements is determined by the % Error which happens to be between 0.65% and 5.72% for bare foot and between 1.6% and 7.29% for shoes. This error range is good considering our proposed sensor resolution.

For our Infrared Sensing Floor to be adopted as clinical assessment tool or as a final product, the reliability of the system must be evaluated. The reliability reflects not only the degree of correlation but also agreement between measurements. Mathematically, the reliability is defined by the Reliability Index (RI). It represents the ratio of T rueVariance over T rueVariance plus ErrorVariance [Ebel, 1951]. It is given by Equation 8.4 :

Reliability Index (RI) = T rueVariance T rueVariance + ErrorVariance * 100 (8.4) We determined the (RI) for both experiments taking into consideration the T rueVariance of the RCA fields and ErrorVariance of the M. Error field. The (RI) is equal to 99% for bare foot and 99.23% for shoes. Our proposed Infrared Sensing Floor is thus highly reliable in determining the Active Contact Area (ACA) of a footprint/shoe print.

CONCLUSION

The impact of elderly falls at homes can be leveraged by developing indoor solutions capable of monitoring early signs of risky situations. These systems use sensors deployed in the environment to detect or to predict a falling incident by performing special analysis on extracted gait and balance parameters. In this chapter, we discussed the design concept and the hardware development of our proposed Ambient (AMB) system which consists of an Infrared Sensing Floor for elderly fall monitoring. The Infrared Sensing Floor is the second functional entity of our proposed WMFL v1.0 Platform that makes the object of Part IV of this thesis. The Infrared Sensing Floor is composed of 8 floor plates where infrared sensors are implanted at high resolution. A special clustering algorithm is developed to extract the footprint/shoe print on the floor. The footprint will be used to collect the spatial-temporal gait parameters in Chapter 10. The accuracy of the infrared sensing plate was validated with a group of students of different foot and shoe sizes by determining the Active Contact Area (ACA). The ACA and the Real Contact Area (RCA) are compared during a two-phase experiment (bare foot and shoes). Results showed a very good accuracy between the measured ACA and the RCA with an average error of 2.82% for bare feet and 5.08% for shoes. The Reliability Index (RI) is found to be greater than 99% for both experiments. Futures works will consider adding load sensors at the 4 corners of each floor plate and an Inertial Measurement Unit (IMU) at the center that render the system completely adequate for indoor fall detection and prediction. The third functional entity of our proposed WMFL platform is the Data Collection and Processing Software Application. The functions performed by this software range from simply collecting data to displaying it on the Graphical User Interface (GUI). The focus of this chapter is to describe the design and development of the WMFL v1.0 Software Application. In the first section, we introduce the software functional architecture that is composed of two types of modules : 1) the nonuser-dependant modules and 2) the userdependant modules. The first type is being the functional blocks related to data collection and processing whereas the second type being controlled by the user. In the second section, we present the Software Application Graphical User Interface (GUI).

SOFTWARE FUNCTIONAL ARCHITECTURE

With reference to our proposed WMFL v1.0 Platform architecture introduced in Chapter 10, Figure 10.1, the mediator electronic panel provides the Software Application with the data after requesting it from both the Force Sensing Shoe and the Infrared Sensing Floor.

Received data packets are verified and processed to extract the basic components namely, the footprint/shoe print and the Center of Pressure (CoP) trajectory. These basic components are combined to extract the required parameters for gait and balance analysis. The set of parameters constitute the actimetric profile of the target individual. These parameters can be stored/retreived in the database and/or displayed on the Graphical User Interface of the software. The software functional architecture is currently composed of three modules. These functional modules are developed in C# under the Microsoft .Net environment. The WMFL v1.0 Software Application runs on a computer with a multi-core processor. These modules are the following :

-The data processing module -The data storage module -The data visualization module These modules can also be classified into two categories : 1) user-dependent and 2) nonuser-dependent. For instance, data processing functions such as data collection, preprocessing, parameters extraction and post-processing are independent from the user. They interact only with the hardware of the platform. On the other hand, user-dependant modules are generally developed to interact with the user. Data storage and visualization modules belong to this category. They consist of a database for storing the data and a GUI for visualization. Figure 9.1 illustrates the modular architecture of the WMFL v1.0 Software Application. It shows the interactions between the three modules and the functions realized by each one. Notice, that the user interacts only with the data visualization module. The data storage module is controlled by the GUI where the user can perform data storage/retreival operations from the database. The data processing module is also controlled by the GUI where the user can start/stop the data processing functions. In the following subsections, we will describe the functional development of these modules.

Data Processing Module

Data Storage Module

Data Visualization Module

DATA PROCESSING MODULE

This module is also called the data layer module. It interacts directly with the hardware entities of the platform mainly the mediator, from where the data is gathered. It ensures func-tions such as data collection, pre-processing, parameter extraction, and post-processing. These functions usually run in the background of the application. This series of functional blocs depend on each other in the sense that the output of a bloc is the input to the next bloc. The functional bloc architecture of the data processing module is shown in Figure 9.2. The figure illustrates how the data is passed from one block to another in a chain series. In the following paragraphs, we will describe the role of each functional block as well as the algorithm that is being developed to ensure its function taking into consideration the input and output of each block. This is the first bloc to be activated in the chain. The role of the data collection block is to gather data after sending special request packets to the mediator electronic panel. The data communication between the software application and the mediator electronic panel follows a simple request/response model. The tasks accomplished by this block are :

-Data request -Data error check -Data parse These tasks are performed in a logical chain as follows. When the user initializes a walking test, the data collection block constructs a broadcast request packet. Typically, the mediator will respond with the data packets containing data from the Force Sensing Shoe and the Infrared Sensing Floor. After sending the broadcast request packet, the software initializes an event listener on the serial port of the computer used for the communication with the hardware platform. The event listener waits for any received data packets on the serial port. Note that, using an event listener instead of simply scanning the serial port is advantageous for it reduces the CPU load. When data packets are transmitted on the RS-485 bus (a new event), the software lunches a new thread that takes care of the received data automatically. Using threads is also important since they don't prevent the software from blocking other operations. When data packets are received, they are stored temporarily inside a buffer until a specific length is reached. At this moment, the data parse task begins. The algorithm of data parsing runs through the bytes of each data packet and parse them into specific variables. The data error check is performed at the same time to verify the order of the bytes. If an error is detected (e.g. missing byte, loss of information, etc.), the packet is purged. If no errors are detected, the data collection block The pre-processing phase is automatically activated by the data collection block. The role of this block is to map correctly each value in both input lists to the correct sensor location in the application software. Recall that the first list contains 16 values ranging from 0 to 255. This list is decomposed into 16 variables having the location of the sensor as names. Note that each sensor is identified by its location on the shoe plantar surface with reference to the foot coordinate system. These values are packed again inside a list relatively to their location. Similarly, the second list contains 225 bytes representing the statuses of 1800 sensors (1800 is the total number of infrared sensors). This list identifies the active infrared sensor by a logical '1' and the inactive sensor by a logical '0'. After receiving this list, each bit is mapped to a sensor location inside the software. After the mapping, a list of active sensors with their relative location is created. The purpose of this process is to consider the floor as a whole big tile. The flow chart of the pre-processing block used for mapping sensor values is illustrated in Figure 9.3-Left.

-Ínput : Two separate lists of raw data packets from the shoe and the floor -Óutput : Two separate lists of mapped sensor values

PARAMETERS EXTRACTION BLOCK

The parameters extraction block is the main core of the WMFL v1.0 Software Application. At this stage, raw data from previous blocks are converted into relevant information. The input to this block are lists of mapped sensor values from the Infrared Sensing Floor and the Force Sensing Shoe. The output are gait parameters. These parameters will constitute the actimetric profile of the subject under test. There are three sources of parameters categorized as follows :

-Parameters from the Force Sensing Shoe system -Parameters from the Infrared Sensing Floor system -Parameters from the combination of both systems These parameters will be further described in section 10.3 of Chapter 10. In this block, we are more interested in describing the logic behind the extraction of these features. Mapped lists data packets from the Force-Sensor Shoe are used to extract the kinetic parameters such as the weight and the CoP trajectory. These parameters are simply obtained without complex processing. However, spatial-temporal parameters extracted from mapped lists data packets originated from the Infrared Sensing Floor, require complex processing to be determined. The first step in this process is to group active sensor data to obtain groups of sensors called clusters. These will determine the Active Contact Area (ACA) that represent the footprint/shoe print on the floor. This step is delivered by the clustering algorithm described previously in Chapter 8, Section 8.4. In a second step, current and previous clusters of active sensors are compared to extract other parameters that require the presence of 2 or more clusters (e.g. step length, stride length, etc.). The 112CHAPITRE 9. WMFL V1.0 DATA COLLECTION AND PROCESSING SOFTWARE APPLICATION remaining parameters are obtained by combining those from the Force Sensing Shoes and from the Infrared Sensing Floor.

The main challenge in this block is to be able to extract gait parameters from the Infrared Sensing Floor. By analyzing data packets, these parameters can be divided into three groups as follows :

-Group 1 : Parameters extracted from one data packet (e.g. footprint/shoe print) -Group 2 : Parameters extracted from more than one data packet (e.g. stride length, step length) -Group 3 : Parameters extracted from all data packets (e.g. average speed)

The output of the clustering algorithm are groups of sensors having the same identifier (GroupID) representing the footprint/shoe print on the floor. The parameter extraction process starts by removing any noise in the system. In fact, at the beginning of the gait cycle (heel contact), any footprint will occupy 3 to 8 sensors from its apparition (stance phase) on the floor until it disappears (swing phase). This number may vary with the size and the location of the foot/shoe over the sensors. Groups containing 1 or 2 active sensors are considered as noise and removed. Moreover, if any active sensor is detected for a long time, the software triggers an alert indicating a hardware defect. After noise removal, the remaining groups of sensors having more than 3 active sensors are labeled as footprints. But, the footprint formation and detection are by themselves delicate processes. In fact, during normal gait, the footprint starts to appear as a small number of active sensors, then this number begins to grow until the footprint is complete on the floor and finally the footprint starts to disappear. These phases of footprint formation are all related to a single footprint and must be grouped together as per each footprint where meta-data such as the time stamp is added. As a result, each footprint will have the following attributes :

-List of current active sensors related to a specific footprint.

-List of all clusters related to a specific footprint indicating the formation phases of this footprint. This attribute will allow us to detect the maximum and the two minimums of the footprint. -Time stamp when the footprint appears.

-Time stamp when the footprint disappears.

Where the time stamps allow us to determine the time for the footprint which will be labeled as the stance time in the spatial-temporal parameters of the next section.

The result of the first stage in the parameter extraction block are clusters representing the different footprints. In summary, these clusters may belong to either of one the following options :

-Option 1 : Clusters are for a new footprint.

-Option 2 : Clusters are for a changing footprint (appearing or disappearing).

-Option 3 : Clusters are for a previous footprint.

In order to detect either one of these options, clusters are compared with those from the previous analysis. In other words, the algorithm determines the intersection between each footprint of the current data packet with every footprint of the previous one. The result of the intersection determines the number of sensors that is shared between two footprints of two different data packets. Thus, we identify three cases :

-Case 1 : No common sensors are detected. In other words, there are no common sensors between the current footprint and any footprints from previous data packets. This means that a new footprint appeared. In this case, new instance for the footprint is created and associated with the above mentioned attributes. The list of current sensors is filled with the sensors of the new cluster.

-Case 2 : The number of common sensors is lower/greater than the one of a previous footprint. This case describes a footprint that is appearing or disappearing.

In this case, if the number of common sensors of the current footprint is less then the number of sensors of a footprint in a previous data packet, this indicates that the footprint is disappearing. Otherwise, if the number of common sensors is greater then the number of the previous footprint, then the footprint is appearing. In this last case, the area if the footprint is growing.

-Case 3 : The number of common sensors is the same. This case indicates simply that no change has occurred in the footprints. This option occurs as the refresh rate of acquiring frames is faster than the normal walk of a person.

After finishing all the current clusters and comparing them with clusters from previous data packets, the algorithm loops again on all the previous clusters to find a footprint with no intersection with the current footprint. This means that the previous tested cluster is not present in the current data packet anymore. The footprint is labeled as inactive and disappears. The time stamp for the deactivation of this footprint is set to the current time. Finally, the software sends a request frame to receive a new packet. The real time processing of this block ends at this point. Active footprints are now saved along with all the modifications on the previous footprints. The data is now ready for the next stage, that is the parameter extraction. This stage is fired by the user at the end of each walking test. It includes the extraction of spatial-temporal and kinetic gait parameters with respect to the previously mentioned groups. These parameters will be described in the Section 10. The post-processing phase is triggered by the user after extracting all gait parameters from the previous block. The role of this block is to prepare the data to be displayed on the GUI or to be stored in the database. All gait parameters and features including raw data from previous phases such as the list of active infrared sensors and the force sensors values are post-processed into graphs, charts or tables to be displayed on the GUI. For instance, the footprint/shoe print are displayed on the GUI in a section that simulates the look of the real walkway. This bock can be customized to deliver the most meaningful data representation. In fact, data can be rearranged to be saved and results can be reviewed through user generated reports. The logic of the post-processing block is illustrated in Figure 9.5.

-Ínput : Raw data lists, gait parameters.

-Óutput : Graphs, charts, tables, reports.

Data Visualization Module

Data

DATA STORAGE MODULE

The data storage module is developed inside the Software Application. It runs in the background and it is controlled by the user. Therefore, it is a user-dependant module. The main function of this module is to store/retreive information concerning the subject as well as data related to the test results. Thus, the core of this module consists of two main databases that are connected to each other. The first is used to store/retreive subjects' information whereas the second is for the test results. Details concerning the subjects are entered at the beginning of a new test through a specific GUI. The database model used to store subjects' information is consists of four tables : 116CHAPITRE 9. WMFL V1.0 DATA COLLECTION AND PROCESSING SOFTWARE APPLICATION -Patient : This is the main table of the database. The patient ID is the unique identifier to the table. It contains basic information to identify the patient or the test subject such as his name, birthday, age, gender, address (e.g. city, phone). Physical body dimensions such as the height, weight and the foot size are also entered in this table. They can be useful for gait classification. The foot size is required to estimate the area of the footprint. In addition, we are also interested in the medical history of the person such as the presence of diseases and whether the patient is on certain medications or in case of falls, the history, the cause and the date of any previous fall.

-Patient test : This table is filled at the beginning of a new test. The test ID is the unique identifier to this table. The user enters data related to the patient's vital signs namely, the heart-rate, the oxygen level (SPO2) and the arterial pressure for their impact on gait. The date of the last test is also recorded.

Tables Disease and Causeoffall contain information regarding the diseases and the causes of a fall for a specific patient.

During each trial, many footprints are collected from the Infra Sensing Walkway as well as pressure data from the Force Sensing Shoe. The database related to the test results is transparent to the user and it is accessed within the software application. It is also composed mainly of four tables :

-Footprint : This table contains the meta data related for each footprint during a test. These data are the ID of the undergoing test, the time of appearance of the footprint, the time of disappearance, the Actove Contact Area (ACA), and the center of footprint coordinates.

-Parameters : This table is filled with gait parameters that are extracted from more than one footprint, such as the stride time, stride length, step length, etc. These parameters are stored as pairs of (name, value). The relations between the parameters of distinct footprints are maintained in the table footprints parameter.

-Shoe sensor : This table contains the force sensor values. These values are associated to a footprint as they are acquired multiple times for each footprint, the reason for which the Footprint ID field is added to this table. In other words, it indicates the corresponding footprint where force sensor values are mapped.

DATA VISUALIZATION MODULE

The data visualization module provides interaction between the Software Application and the user of the system. It offers a variety of options to start or stop other modules and change some hardware settings. Thus, it is used to visualize data in real time. It allows the user to perform hardware testing and debugging to the platform. It provides the user with access to store or retrieve data from the database. The data visualization module consists of several GUIs that run in the foreground. Therefore, it is always visible to the user. This module runs into its own thread in parallel with the other two modules of the software. The idea of multi-threading in case of this module is very important for avoiding any blockage to running algorithms where execution time is essential. The data visualization module is mainly composed of three GUIs :

-GUI for real time data visualization -GUI for hardware testing/debugging -GUI for data storing/retrieving

The GUI development is described in the next subsection.

SOFTWARE GRAPHICAL USER INTERFACE

The Graphical User Interface (GUI) is the main component of the data visualization module. It is the part that provides interaction between the user and the WMFL v1.0 Software Application. It contains sections to edit essential parameters, initialize hardware communication, visualize test data and acquire data for the database. The software GUI was developed using Windows Presentation Foundation (WPF). WPF is a graphical development environment by Microsoft for rendering user interfaces in Windows-based application. It provides the essential components for creating a GUI. This environment is highly customizable due to the use of the Extensible Application Markup Language (XAML). Figure 9.7 shows the main view of the GUI of the WMFL v1.0 Software Application. This interface is used to access the different functionalities of the software. All other GUIs are launched from this window and the software returns to it when they are all closed. The previously mentioned GUIs are described in the following subsections. -The Infrared Sensing Walkway sub-form (#1) -The Force Sensing Shoe sub-form (#2) -The data logging sub-form (#3) Sub-form views (#1), (#2) and (#3) of the main GUI are used to display data in real time. The sub-form view (#1) emulates the Infrared Sensing Walkway. This view displays data processed in real time. The data are mainly active infrared sensors clustered into footprints. This helps the user trace the subject's motion on the floor. This will also give a general overlook of the footprints generated on the floor. Beside the footprint, the rest of the parameters will be displayed on this sub-form view after being processed. In this case, the Infrared Sensing Floor will be working in non-real time mode where it can display data from both the Force Sensing Shoe and the Infrared Sensing Floor.

The sub-form view (#2) emulates the instrumented insole of the shoe. This view displays the excursion of the CoP at the bottom of each foot. The CoP trajectory is also displayed in real time. The idea of the shoe data visualization is to allow the user to keep track of the CoP trajectory during a walking session.

The sub-form view (#3) displays the values of some parameters obtained in real time and that does not require heavy processing (e.g. the area of the footprint, the CoP coordinates, the forces, etc.). It indicates the beginning and the end of a walking trial and logs trouble events such as the serial communication failure.

The main GUI has a toolbar shown at the top. It allows the user to access to the rest of the GUIs namely, the hardware testing/debugging and the data storing/retrieving GUI.

#1 #2 #3

Infrared-Sensing Walkway sub-form Force-Sensing Shoe sub-form Data logging sub-form This GUI allows the user of the software to store and view data from the database regarding the test subjects as well as their corresponding test results. It is composed of two GUIs : 1) the subjects information GUI and 2) the data results GUI. These are accessed from the main toolbar of the software application under the "Patient" and "Data" tabs.

SUBJECT INFORMATION GUI

This interface is developed to enter the subject basic information in the database. Before each session, the user of the software collects a data record for each subject. Each record contains information regarding the subject identification (e.g. name, gender, age, etc.), his/her body physical dimensions (e.g. height, weight, foot size, etc.) and his/her medical history (e.g. attained deceases, medicines, history of falls, etc.). The user is also capable of viewing data from the database in case of another walking session. The subject information GUI is shown in Figure (9.10). The design and development of the WMFL v1.0 Software Application is described in this chapter. The main function of this application is to collect, visualize and store gait and balance parameters for future analysis. The functional architecture of this application is composed of three core modules. These modules interact with the user and with the hardware of the platform. The hardware dependant modules (nonuser-dependant) are related to data collection and processing. They are described using logic flow charts representing their functional algorithms. On the other hand, user-dependent modules such the data visualization and the data storage/retreiving are described by their GUI and conceptual designs respectively. The WMFL v1.0 Software Application is still in its early phase. Future works will add more modularity allowing the integration of data analysis based on the application (e.g. fall detection, prediction, behavior analysis, etc.). New changes will be made to the Graphical User Interface allowing more interaction with user and easy visualization of the data (e.g. graphs, reports, etc.) Tracking System (ETS), force plate, etc.) [Chaccour et al., 2016]. They are also capable of detecting and classifying early signs of abnormal walk through complex algorithms (e.g. Machine Learning, Neural Network, etc.). Despite their high efficiency in providing objective assessment of gait and balance measurement, these systems are only deployed in clinical settings and require technical skills to run them. Moreover, they suffer from the "white syndrome" that is when people improve their posture and gait during tests [START_REF] Kenny | Falls prevention in the home : Challenges for new technologies. Intelligent Technologies for Bridging the Grey Digital Divide[END_REF]. These systems are also very expensive to carry them at home. Researchers are currently putting efforts to make low cost devices to be deployed in real-life environment with the same accuracy and reliability of the clinical systems. In the latter, real time algorithms that can assess gait and balance can run on these devices to detect or to predict a fall incident.

In this context, we will introduce in this chapter the system architecture of our proposed WMFL v1.0 Platform identified based on our generic classification introduced in Chapter 3 and that to the best of our knowledge, doesn't seen to be among the existing solutions.

In addition, we will define and describe the spatial-temporal, kinetic and kinematic gait parameters as displayed by our WMFL v1.0 Software Application introduced in Chapter 9. Finally, the reliability of the platform is evaluated by determining the Inter Correlation Coefficient (ICC) between 6 trials divided in 2 sessions of 8 young adults and 8 elderly. Moreover, we used the Onev-Way ANOVA (ANalysis Of VAriance) to identify the gait parameters that differentiate between the young and the elderly profiles.

10.2 PROPOSED WMFL V1.0 SYSTEM ARCHITECTURE WMFL is the name given to our proposed platform to answer the needs of the elderly in terms of mobility, security and independence. The objective is to maintain the elderly in his daily life activities by extracting and analyzing his actimetric profile in order to predict and alert either a change in behavior and/or in gait or posture. The actimetric profile is being the list of gait and balance parameters of the person that identify the human locomotion. As a consequence, the system architecture of this platform should be designed to be generic and open to implement various application cases (e.g. fall detection, fall prediction, behavior analysis, activity monitoring, etc.). It must also consider the level of intrusiveness, the maintenance, the cost of installation, the scalability, the reliability, etc. if needed to be considered for home premises or elderly care facilities.

Referring to our proposed generic classification of fall-related systems in section 3.5, our proposed WMFL v1.0 Platform belongs to the Fusion Systems (FS) category under the Fall Prediction (FP) group. It combines a Foot Wear (FW) system and an Ambient (AMB) system. Therefore, it uses sensors deployed on a footwear device as well as sensors deployed in the environment. The fusion of these two systems will enable the extraction of almost all gait and balance parameters (e.g. step length/time, stride length/time, stance/swing time, CoP, etc.). These are being the kinetic, kinematic and spatial temporal parameters that compose the actimetric profile of the person. The platform can also be controlled by a Smartphone (SP) for indoor/outdoor modes. Data collection and processing are currently performed on the WMFL v1.0 Software Application. Although, it was originally designed to be deployed inside premises, WMFL can also be used in clinical settings. The proposed WMFL system architecture is illustrated in Figure 10.1. The version 1.0 of this platform is mainly composed of four parts :

-Force Sensing Shoe : This is the moving part of our proposed WMFL v1.0 platform. It consists of two main components : 1) the insole with the force sensors and 2) the wearable electronic board. It can work in stand-alone and dependants modes. The Force Sensing Shoe is used to collect dynamic and static kinetic parameters such as the GRF vertical component and CoP trajectory on each foot. When combined with the floor, the location of the body CoP can be extracted.

The hardware design and development of the Force Sensing Shoe is described in Chapter 5.

-Ínfrared Sensing Floor : This is the fixed part of our proposed WMFL v1.0 platform. It consists of three main components : 1) the infrared sensing plate, 2) the plate controller electronic board and 3) the mediator electronic panel. The Infrared Sensing Floor is used to collect spatia-temporal gait parameters such as the stride length/time, stance/swing times, Base of Support, foot progression angle, etc. When combined with the Force Sensing Shoe, the distance between the CoF-CoP can be determined. The hardware design and development of the Infrared Sensing Floor is described in Chapter 8.

-Data collection and analysis Software Application : This part is used when the platform is deployed in a clinical setting. In this case, a health-care professional must carry out the walking session. It is composed of modules related to data collection and processing, and modules for data control and user interaction. The design and development of the WMFL v1.0 Software Application is described in Chapter 9.

-Śmartphone Application : It consists of a mobile application that allows the WMFL v1.0 to be configured in different modes of operation. These modes are describes as follows :

-Outdoor no smartphone : In this mode of operation, the Force Sensing Shoe is operating as a stand-alone device. In this case, an early fall detection approach such as the one proposed in Chapter 6, can be running on the microcontroller of the wearable electronic board. The Smartphone can be used for notification in case of a risky situation. -Outdoor with smartphone : In this mode of operation, the Force Sensing Shoe is communicating with the Smartphone. In this case, raw force data are collected from the insole and transmitted to the Smartphone. The force data are combined with data from the IMU of the Smartphone for analysis. The early fall detection approach is running on the Smartphone. As a consequence, the notification can be generated from the Smartphone or from the electronic wearable board depending on the user preference. -Indoor no shoe : In this mode of operation, the Infrared Sensing Floor is operating as a stand-alone device. The person is not wearing his shoe. In this case, spatial-temporal gait parameters are only extracted from the Infrared Sensing Floor. The analysis can be done according to the context of the application (FD, FP, activity monitoring, behavior analysis) using a specific algorithm. -Indoor with shoe : In this mode of operation, data from the Force Sensing Shoes are combined with data from the Infrared Sensing Floor to extract the kinetic and spatial-temporal gait parameters. Similarly, the analysis can be done according to the context of the application (FD or FP, activity monitoring, behavior analysis) using an specific implemented algorithm. The Smartphone mobile application is not yet developed and it will be considered in the future works of this thesis. The mobile application is thought to be simple and easy to use by the elderly. In our case only 4 modes of operations are available.

It is easy noticeable from the system architecture of the platform, the different aspects of the design. For instance, having a combination of two separate systems, allows the platform to be used in indoor and outdoor modes. These modes are operated by the mobile application. Besides this combination, these two systems can be used separately as two independent platforms. The design of the Infrared Sensing Floor gives the platform the aspects of scalability, flexibility and extensibility. In fact, more floor plates can be added offering a larger coverage of the surface. Moreover, the floor plates can be installed in a way depending on the application. For example, it might happen that the floor plates can be assembled like an I-shape for gait analysis or as square shape for activity and behavior analysis depending wether they need to be installed inside corridors, rooms or both. The extensibility of this platform allows the adding of extra hardware namely to answer the various applications (e.g. fall detection, fall prediction, activity and behavior analysis, etc.). These extensions are made available by the modular design of the hardware. For instance, an IMU module can be added at the center of each floor tile to detect the impact of the fall. Note that a lying person can be easily detected by analyzing the contour of the shape on the floor. In addition, load sensors at the four corners of the infrared floor plate can be added to extract the three components of the GRF (the vertical component is currently extracted by mapping the Force Sensing Shoe on the Infrared Sensing Floor). Presence and activity sensor can also be added at the level of the mediator panel for activity monitoring and behavior analysis. Kinematic parameters such as joint rotation and joint angles can be obtained by integrating Microsoft Kinect skeleton detection in the Software Application. Note that only spatial-temporal and kinetic parameters are currently extracted in this version of the platform. From the software perspective, the WMFL v1.0 must be installed on the computer to run the hardware. The software application must be used in clinical settings by a health-care professional. Alternately, when the platform is deployed inside an elderly care facility, the installation of the software application is not mandatory since the core of the software will be embedded in the hardware.

The way the WMFL platform is designed, provides a low level of intrusiveness to the elderly. Despite the fact that one of its parts is considered as Wearable System (WS), it belongs to the Foot Wear (FW) systems. These are less intrusive and less cumbersome to the elderly than other body worn devices. In addition, a conscious person won't forget to put his shoes on. Regarding the user acceptability, wearing a shoe is much better than wiring the entire body with motion sensors. With reference to our proposed Force Sensing Shoe, the wearable electronic board currently fixed at the waist will be replaced by two electronic boards inside the sole of the shoe. The new configuration will provide the elderly with a free gait while performing his Activities of Daily Living.

In terms of installation, maintenance and cost, the WMFL platform is easy to install and maintain. The modular design of the platform allows easy replacement of a defected component. For instance, the plate controller electronic board can be easily removed and replaced. Network communication failure can be easily detected by simple trouble shooting. On the other hand, the infrared sensors are difficult to repair since they are sealed inside the plate. But the design for manufacturing should consider a solution for replacing defected sensors. In terms of cost, the design of this platform is made of low cost sensors allowing it to be an option for home or elderly care facility installations. Compared to commercial products (e.g. GAITRite, Tekscan, etc.) deployed in clinics or gait labs and that are relatively expensive, our proposed solution offers the same functionalities at lower costs for home use.

WMFL GAIT AND BALANCE PARAMETERS

Human locomotion is exhibited in a proper manner called gait that is specific to each person. Gait is performed using a sequence of cyclic motions of both limbs often known as the gait cycle. The cycle is initialized at the heel contact of one foot and terminates and the heel contact of the same foot. The gait can be cut into two cyclic phases : The stance phase and the swing phase. The stance phase is when the foot is in contact with the ground. It occupies about 60% of the gait cycle. The swing phase is when the foot is in the air. It occupies about 40% of the gait cycle. The phase are identified by several parameters. Gait analysis is the study of these parameters to evaluate potential risks of certain pathologies or incidents. These parameters can be grouped into three categories as follows :

-Spatial-temporal parameters : This category is a set of parameters that inform about the course of the gait in time and space (e.g. step length/time, stride length/time, cadence, walking speed, etc.). They are commonly used in gait analysis. These parameters often vary with age.

-Kinematic parameters : This category defines the set of parameters that inform about the aspect of the walk considering variables extracted from the lower limbs (e.g. joint rotation of the hip, knee and ankle, joint angles of the hip/knee/ankle, and thigh/trunk/foot angles).

-Kinetic parameters : This category defines the set of parameters that inform about the cause of motion namely, the forces and the moments generated during locomotion (e.g. GRF components, CoP location, plantar pressure distribution)

The extraction of the previous gait parameters is often carried in clinics using objective measurement tools. Taking gait analysis into homes is challenging. With reference to our proposed WMFL design architecture, we were able to extract the majority of gait parameters that belong mainly in the first and the third categories. The following subsections describe the gait parameters as they are extracted by our WMFL platform and displayed on the WMFL v1.0 Software Application. The figures are taken from test experiments on real subjects. As was previously mentioned, these parameters are divided with respect to the parts of the WMFL architecture where they are extracted from namely, the Force Sensing Shoe, the Infrared Sensing Floor and the combination of both.

PARAMETERS FROM THE FORCE SENSING SHOE

Parameters extracted from the Force Sensing Shoe belong to the kinetic category. Recall that our instrumented insole is equipped with force sensors at specific locations of the foot plantar surface. This configuration allows the extraction of the force level exerted on each sensor as well as the GRF. Knowing the surface of each sensor, the pressure can be determined. Using the force levels and the location of each sensor, the instantaneous CoP coordinates are computed. These parameters are described in the following :

10.3.1.1 GROUND REACTION FORCE
Body weight is the main cause for exerting force on the foot plantar surface. During quit standing, the weight is divided on both feet. At the same time, the ground produces a reaction force equal and in the opposite direction of the body weight. This force is known as the Ground Reaction Force (GRF). The GRF is therefore detected at the surface of contact with the ground underneath each foot. To measure the GRF, sensors are mounted at specific locations of the insole at the points of maximum load as described in Chapter 5. The GRF is therefore the sum of the forces under each foot with respect to the foot coordinate system. These forces are collected from the instrumented insole taking into consideration the load distribution at each sensor as explained in [START_REF] Nordin | Basic biomechanics of the musculoskeletal system[END_REF]. The use of force sensors will only provide the vertical component of the GRF. The Anterio-Posterior (A-P) and the Medial-Lateral (M-L) components are not provided with such sensor. The Force Sensing Shoe provides the total GRF as being opposite and equal to the body weight.

The GRF on each foot is determined using Equation 5.2 from Chapter 5.

CENTER OF PRESSURE TRAJECTORY

During dynamic gait, the Center of Pressure (CoP) trajectory marks the line of action of the GRF [Kirtley, 2006]. In other words, it represents the instantaneous location of the GRF during the stance phase of the gait cycle. The CoP trajectory under each foot starts at the heel and propagates along the bottom of the foot until it reaches the area of the toes. Knowing the coordinates of the force sensors and the force magnitude at each sensor, the location of the CoP is determined using Equations 5.3 and 5.4 from Chapter 5. The Force Sensing Shoe provides the instantaneous location of the CoP during dynamic gait with respect to the foot reference system as shown in Figure 5.7. On the other hand, When the person is standing still, the body CoP is somewhere between the feet at about 5cm anterior to the ankle joint. The location of the body CoP should be determined with respect to a ground reference system. This is accomplished by combining the Force Sensing Shoe and the Infrared-Sensing Floor. parameter (spatial or temporal), lengths are computed by taking the cartesian distance between two point coordinates for spatial parameters and their units are expressed in centimeter (cm) or square centimeter (cm 2 ). Time stamps are added to compute time differences for temporal parameters and their units are expressed in milliseconds (msec). It results that parameters of speed are expressed in (m/s). Spatial-temporal gait parameters are shown in Figure 10.2. This figure and the rest of the illustrations in this subsection are taken from the WMFL v1.0 Software Application. The spatial-temporal gait parameters are described in the following paragraphs.

FOOTPRINT AREA

As described in Chapter 8, this is the Active Contact Area (ACA) between the foot and the IR-sensor matrix. In other words, it is the area that gathers all active sensors under the foot. The footprint area is a spatial parameter that is determined by interpolating the sensing area of each sensor by 1.5cm from each side considering it as a square of 3 x 3cm 2 . The footprint area is therefore determined by multiplying the number of active sensors by 9 according to Equation 10.1. The footprint area is shown in Figure 10 The Center of Feet (CoF) represents the center of the line connecting the center of the right foot to the center of the left foot. The center of footprint is a spatial parameter representing the geometric center of the sole or its centroid. Having the group of active sensors that defines the shape of the footprint, we compute the convex hull of this shape. The convex hull is defined as the smallest set of points that envelops a group of active sensors. The results is a finite set of n points that determine the boundary of the footprint. The center of the footprint is obtained using Equation 10.2. Knowing the centroids for each foot, the CoF is easily determined using Equation 10.3, where C f p/r and C f p/l are the centroid of the right(r) and left(l) footprints respectively. Figure 10.4 shows the CoF as displayed on the WMFL v1.0 Software Application. The step length is by definition the distance in cm between the heel contact point of one foot and that of the other foot. Having the left and right group of active sensors forming the footprint, the heel contact point is chosen as the most distant point from the centroid in the opposite direction of the footprint. The step length is thus computed in our software application by taking the distance between the tangents at the points of maximum distances for two consecutive footprints of both feet. We can distinguish the right step from the left step based on the foot that steps forward. As described earlier, the stance is when the foot is in contact with the ground for 60% of the gait cycle. The stance length is therefore the length of the footprint. In our software application, it is computed as the distance between the first and last contact points of the footprint shape. The first contact point is at the heel and the last contact point is at the toes at the maximum distance from the centroid. But the stance phase of the gait cycle is often measured by its time rather than its distance. The stance time (t stance ) is the duration between the first and last contact points of the footprint. In the software application, the stance time is computed by subtracting the time stamps of appearance (t a ) and disappearance (t d ) of the same footprint as shown in Equation 10.6. The stance length and time are shown in Figure 10.2.

C f p (x, y) =         1 n n i=0 x i , 1 n n i=0 y i         (10.2) CoF(x, y) = x C L + x C R 2 , y C L + y C R 2 (
t stance = t d( f p i ) -t a( f p i ) (10.6) 10.3.2.7 SWING TIME
The swing phase is when the foot is in the air for 40% of the gait cycle. The swing is often characterized by its duration or the swing time. This is the time duration when the foot leaves the floor and reappears again. In the software application, the swing time is obtained by subtracting the time of disappearance (t d ) of footprint from the time appearance (t a ) of the footprint of the same foot again as shown in Equation 10. The foot progression angle designates the angle formed between the segment joining the median point of the heel and the direction of walk also known as the progression line. In our software application, the progression line is the line crossing all the footprints on the Infrared Sensing Walkway. It is therefore parallel to the x-axis of the walkway. The foot progression angle is expressed in degrees. The foot progression angle is shown in Figure 10.2.

PARAMETERS FROM THE COMBINATION OF BOTH SYSTEMS

Parameters extracted from the combination of the Force Sensing Shoes and the Infrared Sensing Floor belong to the spatial-temporal category. By mapping the force sensors of the Force Sensing Shoe on the Infrared Sensing Floor, we can emulate a force plate without considering the shear forces (Anterior-Posterior and Medial-Lateral components) provided by the load sensors of the force plate. In this case, we can determine the location of the CoP as well as the distance between the CoF and the CoP.

CENTER OF PRESSURE LOCATION

In Chapter 5, we determined the CoP trajectory during dynamic gait on each foot plantar surface. During static gait, the CoP is located around the centroid of the foot with respect to the foot coordinate system. In order to locate the CoP of the whole body, the CoP coordinates from the Force Sensing Shoe must be mapped on the footprint detected by Infrared Sensing Floor. For this mapping to be done, we must find the coordinates of the origin of the foot reference system with respect to the plate reference system. This is done by mapping the bounding rectangle of the shoe on the infrared sensing plate.

In other words, this corresponds to the bounding rectangle of the foot print. Once the mapping is done, the location of the body CoP is determined with respect to the plate reference system. At this stage of the research, the WMFL platform is set for testing. The Force Sensing Shoe and the Infrared Sensing Floor are now combined to extract the actimetric profile of the participating subjects. The actimetric profile is being the set of spatial-temporal, kinetic and kinematic gait parameters that is specific to each subject. In the mean time, the system is being installed inside the lab to carry out the experiments. Recall that the WMFL platform is intended for use in real-life situations. Inside the lab, the infrared sensing plates are assembled in an I-shape walkway (Figure 10.1). Four dummy plates are added on both ends of the Infrared Sensing Walking (2 at each end). A dummy ramp of 1m is also added at one end marking the starting point of the walk. The dummy plates and the starting ramp help the subject finish the acceleration phase at the beginning of the walk before reaching the infrared sensing plates. Similarly, the dummy plates at the end are for the decelerating phase. Between these two phases, the walk is considered as steady. This will help the platform to acquire a uniform walk. As was previously described in Chapter 8, all the plate controllers were programmed and calibrated for the ambient light threshold. The code for the mediator was also downloaded. The connection with WMFL v1.0 Software Application was also verified.

On the other hand, the Force Sensing Shoe was also set. The force sensors are calibrated and the microcontroller code is downloaded on the wearable electronic board. This can be mounted on the waist of the subject and wired to the instrumented insole. The wireless communication between the wearable electronic board and the mediator was also verified by displaying the data on the shoe testing GUI 9.9. The infrared sensing plates and the shoes are being tested regularly during the tests for any unexpected defects. Before the beginning of any trial, each subject was instructed about the test procedure with a simple demonstration performed by the experimenter in the lab. The testing procedure does not include any familiarization trials with the platform, as a normal walk without any alteration is needed. In addition, the height and weight of each subject were also recorded using the lab equipment for common reference between participants. The subject is then instructed to stand on the footprint markings located on the dummy ramp. The participants were also instructed to walk at their normal speed as if they were walking on the ground. The walk starts when the subject feels comfortable and when the green light is given by the experimenter to start walking and ends when the subject steps off the last infrared sensing plate. The experimenter strongly recommended the participants to avoid any alteration in the walk. After the end of each trial, the subjects starts over with the next one until all three trials are done. The same subjects showed after 2 weeks to perform another 3 trials. The testing procedure was suggested and approved by the department of physiotherapy at the Antonine University.

During each trial, gait parameters were collected using the WMFL v1.0 Software Application. The number of steps recorded during a single trial ranged between 4 and 6 steps.

A session regrouped between 12 and 18 steps. Therefore all sessions were normalized considering 12 steps per session. After each trial, the data from the platform were collected and the gait parameters were computed on the software application. These parameters were described in section 10.3 of this chapter. These are being the spatial-temporal and kinematic gait parameters which make the actimetric profile of each participating subject. One of the objectives of this experiment is to identify the parameters that classify between the young and elderly profiles. Therefore, parameters that are closely related to a specific subject or remained constant between subjects are not considered in the analysis. For instance, the area of the footprint is closely related to the sole of the Force Sensing Shoe that was worn by all subjects and thus was excluded from the analysis, whereas the Base of Support is taken into consideration. In addition, forces computed from the Force Sensing Shoe were also excluded as they were related to the weight of each person and does not represent any specification for a group since all the subjects were using the same Force Sensing Shoe. The Center of Pressure (CoP) and Center of Feet (CoF) were also excluded as they represent the location coordinates. The gait parameters selected for the classification between the young and elders actimetric profiles are presented in Table 10.2.

STATISTICAL ANALYSIS

As we mentioned earlier, this study is intended to demonstrate the possibility to identify the gait parameters that classify between the young and elder actimetric profiles. In this scope, a statistical analysis is applied to test the selected gait parameters that could differentiate between the young and elders subjects. As explained earlier, these parameters were extracted from our WMFL platform according to the described test procedure. They are shown in Table 10.2. The analysis is applied according to two phases.

The first phase consists of testing the reliability of the obtained results within each group over the 2 weeks time interval. In other words, a test of statistical similarity inside the same group for each parameter is needed. Therefore, we determined the test-retest repeatability measures by computing the Interclass Correlation Coefficient (ICC) for each parameter inside each group. This indicator measures the relative similarity of values using the same measurement process [START_REF] Kotz | Encyclopedia of statistical sciences[END_REF]. We followed the flow chart in [START_REF] Koo | A guideline of selecting and reporting intraclass correlation coefficients for reliability research[END_REF] to choose the model, the type and the definition of the ICC form. We applied a two-way mixed effect model with a consistency definition. In this model, the subjects were considered as a random effect, whereas the measurement error is considered as a fixed effect. The results will show the ICC and its Confidence Interval (95% CI) for each parameter. each group. By comparing the mean for each parameter during the walks of the first and the second sessions, we can identify the parameters that are statistically different and thereby we can differentiate between the profiles using these parameters. These parameters could be later used in a classification algorithm to identify a young from an elder. We used for this purpose the One-Way ANalysis Of VAriance (ANOVA). ICC and ANOVA are widely used in the literature. They can be obtained using the IBM SPSS statistical package version 23 (SPSS Inc, Chicago, IL). The results of the statistical analysis regarding the ICC are presented in Table 10.3. This table shows that gait parameters present a relative similarity within each group. In other words, the proposed WMFL platform and practically the Infrared Sensing Floor measured the spatial-temporal gait parameters with high repeatability. This is indicated by the high ICC computed between trials for each group during during the two weeks experiments. The recorded ICC for the young and elders for the test-retest repeatability of the WMFL platform indicates an excellent reliability. The lowest ICC recorded for both groups of subjects is that of the Double Support time with 0.959 (0.794 to 0.992) for the young and 0.95 (0.765 to 0.991) for the elders. Although the ICC of Double Support time shows an excellent reliability, the 95% CI indicates a good reliability. This means that there is 95% chance that the ICC of the Double Support falls on any value between (0.794 to 0.992). Moreover, we compared the ICC of the spatial-temporal parameters of the present study to that of other gait analysis systems such the GAITRrite in [START_REF] Menz | Reliability of the gaitrite R walkway system for the quantification of temporo-spatial parameters of gait in young and older people[END_REF] and the force-measuring gangway in [START_REF] Veilleux | Gait analysis using a force-measuring gangway : intrasession repeatability in healthy adults[END_REF]. The results were quasi-identical for the spatial-temporal parameters commonly present in the other two studies and ours namely, the step length and time, cadence and velocity. We assume that the overall variability in test results depends more on the gait variability of the subjects or the experimental protocol rather on that of the platform. Moreover, we also presume that the use of different sensing technologies (pressure sensors in [START_REF] Veilleux | Gait analysis using a force-measuring gangway : intrasession repeatability in healthy adults[END_REF], load sensors in [START_REF] Menz | Reliability of the gaitrite R walkway system for the quantification of temporo-spatial parameters of gait in young and older people[END_REF] and IR sensors in WMFL) could affect the measurement error. From the results of the test-retest repeatability, we can conclude that the WMFL platform is reliable for most spatial-temporal gait parameters in both young and older subjects. The results of the second phase of the statistical analysis are shown in Table 10.4. The objective is to identify the gait parameters that can differentiate between the young and the elders profiles. The identification of the gait parameters is done by looking at the Pvalue provided by the output of the One-Way ANOVA. If P is less than 0.05, the difference between elderly and young for a specific parameter is statistically relevant. Based on this definition, parameters such as step length, stride length, cadence and walking speed represent a statistical difference between young and elderly. These parameters will allow to classify any new subject between elderly or young. We can clearly differentiate between elders and young subjects by their step lengths and stride lengths and therefore their effect on the walking speed and cadence. In fact, elders tend to walk at lower speeds due to ageing and their state of frailty. At lower speed and with a small step length, the Double Support time is longer for the elders. This was exhibited by the low P-value (P=0.008) of this parameter showing a statistical difference between young and elders. A particular attention is needed to analyze the result of the Base of Support (BoS) area. Table 10.4 indicates a larger BoS area for the young subject and a relatively small BoS area for the elderly. Although this might seem contradictory as the BoS area for the elderly should be larger. In fact, we computed the dynamic BoS area. In this case, the area of the BoS is depending on the shoe print surface area, the step length and the foot progression angle. Since these are higher for the young subjects, that explains the relatively small BoS area for the elders.

We also notice from Table 10.4 that the step time (P = 0.051), the swing time (P = 0.663) 144CHAPITRE 10. COMBINING FORCE SENSING SHOES AND INFRARED SENSING FLOOR and the stance time (P = 0.102) are parameters that does not represent any statistical difference between young and elders. This is actually caused by two factors : the variability of the subject's gait and the sampling rate of the system. In fact, the human factor changes as the subjects (elders or young) tend to alter their walk between trials as well as between sessions. On the other hand, the sampling rate of the received data packets from the Infrared Sensing Walkway is 3.8 Hz. Critical temporal parameters such the one discussed tend to be altered at this frequency. For higher time value such as for the stride time, this frequency does not alter any time data. This limitation is being eliminated in the updated version of the WMFL platform. Taking gait analysis into the every day life of the elderly is the object of our proposed WMFL platform. In this chapter, we described the architecture of the WMFL v1.0 Platform as the combination of the Force Sensing Shoe and the Infrared Sensing Floor. This combination allows the platform to be operational in indoor and outdoor premises by selecting the corresponding mode using a mobile application. The latter will be considered in the continuity of this work. Using this platform, we were able to extract the kinetic and spatial-temporal parameters that define the actimetric profile of the subject under monitoring. The gait parameters were defined and illustrated as they were extracted by the WMFL v1.0 Data collection and Processing Software Application. To test the reliability of our platform, we performed a test-retest repeatability measurement that determined the Inter Correlation Coefficient during a 4 weeks experiments on young and elderly subjects.

The results showed excellent reliability. Then, we applied the One-Way ANOVA to identify the parameters based on which we can classify the actimetric profile as belonging to a young or to an elderly subject. related systems. These were divided under three categories : The Wearable Systems (WS), the Non-Wearable Systems (NWS) and the Fusion Systems (FS) for both FD and FP tracks. Each of these categories has been divided according to their sensor deployment. On the light of the ground reference scheme, we positioned the main contribution of this thesis. The object is to ensure that elders can remain as independent, secure and safe as possible in their own premises wether inside at home or outside and during their ADL. Back to the problem of falls, the goal is to develop a system that is capable of detecting and/or predicting a fall incident in indoor and/or outdoor premises. More precisely, we believe that it is more important to prevent or to predict a fall from happening. In this context, and with reference to our proposed generic classification, we combined sensors from a Foot Wear (FW) system and sensors from an Ambient (AMB) system to propose our Walk More Fall Less (WMFL v1.0) v1.0 Platform. Fall prediction is currently performed by analyzing the gait and balance parameters of the elder using either semi-subjective or objective assessment tools inside clinics or hospitals. The latter require technical skills to run them and they are very expensive. Taking this analysis into real-life scenarios is the main objective of the proposed WMFL platform and therefore the main contribution of this thesis. In other words, it amounts of extracting and analyzing the actimetric profile of the elderly in order to predict and alert him of a potential fall. The actimetric profile being the list of kinetic, kinematic and spatial-temporal gait parameters that describe the human locomotion. By installing this platform at home, we will have an objective measurement on the variability of the person's gait on a daily basis which could indicate the presence of intrinsic factors or a change in behavior that could lead to a potential fall. The proposed platform has been designed to be tested in the premises where the elderly might be present. It can also be used in clinical settings.

From the technical point of view, the WMFL platform is described in the chapters of Parts II, III and IV of this report. It is mainly composed of the Force Sensing Shoe and the Infrared Sensing Floor. In Part II, after we surveyed the existing systems on foot wear devices in Chapter 4, we introduced our proposed Force Sensing Shoe in Chapter 5. The Force Sensing Shoe is the first functional entity of the WMFL platform. In this chapter, we described the system design requirements and architecture from the hardware perspective.

The Force Sensing Shoe is composed of the instrumented insole equipped with force sensors and the wearable electronic board mounted on the waist. In the design of the instrumented insole we minimized the number of force sensors to 8 by increasing their sensing surface at the points of maximum load. We also adapted the design of the insole to fit different foot sizes. The Force Sensing Shoe was intended to extract the kinetic gait parameters such as the GRF (weight) and the CoP trajectory. The accuracy of the insole was computed for the weight. In Chapter 6, we used our Force Sensing Shoe to analyze the displacement of CoP trajectory induced by the Medial-Lateral sway. We determined the profile of the trajectory for the normal walk and computed the excess sway provoked by the altered wall. The number of generated alerts was determined using the spatiotemporal correlation between the excess sway and the window of weighted coefficients representing the importance of consecutive sways in inducing a fall with respect to a risk level. The number of generated false alerts generated by the linear spatio-temporal correlation when the person corrects his balance was minimized by applying the logarithmic function. We also formulated the risk of falling which was determined at the end of the walk. Similarly, in Part III of this thesis, we surveyed the existing solutions on ambient systems in Chapter 7. The second functional entity of WMFL platform is the Infrared Sensing Floor. In this chapter, we described the system design requirement and architecture from the hardware perspective as well. The Infrared Sensing Floor is composed of the infrared sensing plate, the plate controller electronic board and the mediator electronic panel. These components are connected together using the RS-485 communication bus. They are also powered using the main power supply located at the mediator panel. In the design of the infrared sensing plate, we installed 225 IR-sensors with 3 cm spacing making it suitable to extract the footprint/shoe print. The shape of the infrared sensing plates is reconfigurable based on the application usage (clinical, home-based). The Infrared Sensing Floor was intended to extract the spatial-temporal gait parameters such as the step length/time, stride length/time, cadence, etc. For this, we developed the footprint extraction algorithm. The footprint being the area of active sensors on the floor. The accuracy of the Infrared Sensing Floor in determining the Active Contact Area (ACA) was computed and compared to the Real Contact Area for different foot and shoe sizes. Moreover, the Reliability Index (RI) was also computed. The results shown that our proposed Infrared Sensing Floor can be adopted as an objective measurement tool. In the course of Part IV, we introduced the third functional entity of the WMFL platform in Chapter 9. This is being the WMFL v1.0 Software Application. In this chapter, we described the Software Application functional architecture as being composed of three main modules : The Data Processing Module, the Data Visualization Module and the Data Storage Module. The data processing Module is the main core of the software. Its functional blocks are described in details with their respective functional logics. The different GUIs are also described in this chapter. In Chapter 10, we combined the Force Sensing Shoe and the Infrared Sensing Floor to form our proposed WMFL v1.0 Platform. Back to the objectives of this thesis, we were able to combine two systems to extract the kinetic and the spatio-temporal gait parameters that compose the actimetric profile of a person. The design architecture of the WMFL platform is illustrated in this chapter. The dynamic architecture of this platform allows the extraction of the gait parameters as well as the implementation of a fall prediction scenarios are controllable from a mobile application via 4 modes of operation. The mobile application being the fourth functional entity of the platform which will considered in the future works of this these. In this chapter we have defined and illustrated the kinetic and the spatial-temporal gait parameters as they were processed and visualized by the WMFL v1.0 Software Application. The reliability of this platform has been validated by an experimental protocol that was carried on young and elderly subjects during 4 weeks alternate sessions. We computed for this purpose the Inter Correlation Coefficient (ICC) which indicated an excellent reliability. Moreover, we performed the One-Way ANOVA statistical test to identify the gait parameters that differentiate between the young and elderly actimetric profiles. Finally, the flexibility, scalability and the extensibility of this platform renders it to perform in indoor and outdoor premises.

FUTURE WORKS AND PERSPECTIVES

The culmination of this work is far from over. Further perspectives will be tackled in the future works of this thesis. The continuity of these works can be seen on different levels starting from the hardware and software design and development of our proposed WMFL v1.0 Platform to the analysis of gait and balance and activity monitoring.

Regarding the WMFL platform, novel modifications to the hardware and to the software are highlighted in the new version : WMFL v2.0. Recall that our proposed WMFL v1.0 is composed of a Force Sensing Shoe and a Infrared Sensing Floor. The design concept of the Force Sensing Shoe is being rethought to provide free natural gait to the elderly taking into consideration different shoe sizes. The wearable electronic board will be replaced by two miniature electronic cards in each shoe. To have more insights on the gait analysis, the electronic cards will accommodate new sensors (e.g. Accelerometer and gyroscope).

Beside being used in combination with the Infrared Sensing Floor, the shoe will be able to operate as an independent system where the data extraction and processing can be performed locally on the programmable unit of the electronic cards. Our Force Sensing Shoe could also be a part of a larger e-Health platform deploying various Internet of Things (IoT) sensors and combining gait analysis with other vital signal sensors giving more insights on the impact of intrinsic factors on gait and thereby on the prediction of falls or perhaps on the progression of a certain disease.

Similarly, modifications are being thought of concerning the hardware design of our proposed Infrared Sensing Floor. For instance, new functionalities are being considered such as fall detection. Beside the fact that our Infrared Sensing Floor is currently capable of detecting a lying person, adding an accelerometer at the center of each plate will detect the fall impact validating by this a fall incident. This hardware add-on will allow a deep understanding on the cause of falls taking into consideration recent gait parameters. Moreover, adding load sensors at the four corner of each plate will allow the extraction of kinetic gait features such as the Center of Pressure (CoP) leaving the Infrared Sensing Floor in a stand-alone mode without coupling the Force Sensing Shoe. In this case the elderly will be able to move freely inside his home. Regarding this point, we believe that combining position sensors (IR-sensors in our case) and load sensors on the floor plate is a new way of rethinking the force plate for dynamic gait assessment. Knowing the location of the Center of Pressure with respect to the Center of Feet (CoF) could help study the stability of static or dynamic gait inside the Base of Support (BoS). Our Infrared Sensing Floor can be further extended with other indoor systems. In fact, a new hardware design of the mediator electronic panel will accommodate inputs from other systems such as sensors for activity monitoring and motion detectors. In this version of the platform, we were able to collect kinetic and spatial-temporal gait parameters only. By adding a kinect sensor, kinematic parameters will be obtained.

The WMFL platform as it is imagined is controlled by a mobile application. This application defines the four modes of operations of the platform as described in Chapter 10,

FUTURE WORKS AND PERSPECTIVES

Section 10.1. Future works will consider developing and testing this application allowing the person to choose the mode of operation that is suitable for his situation (indoor or outdoor) if we want to install the platform inside homes or elderly care facilities. In the current version, the platform is controlled by the WMFL v1.0 Software Application. The Software Application in its first version provides functionalities for data collection and processing, visualization and storing allowing our platform to be exploited as a clinical system or a small gait lab. Future works regarding the software will consider adding other modules that explore the extracted data for different purposes such fall detection, fall prediction, behavior and activity monitory, gait and balance analysis, user tracking and identification, etc. These modules use preset algorithms that act on the extracted data with respect to the purpose of the application. In addition, the user of the platform will also be able to develop and compile his own algorithm on the software by using the data of the platform.

For home implementation, the software can be used to configure the platform for the application in question by downloading the corresponding algorithm on the hardware. In this case, the platform will be running independently from the software. This modularity of the software is accompanied with the modularity of the hardware with respect to the purpose of the application.

From the analysis point of view, the algorithms that tackle the problem of elderly falls and that can be implemented on our platform use either Analytical Methods (ANM) or Machine Learning Methods (MLM). These are usually found in the literature and are classified in Chapter 3. If the first (ANM) use a relatively small amount of data to detect or predict a fall, the second (MLM) can give more insight on data using large amount of collected gait parameters constituting the actimetric profile of the subject. For instance, taking the whole profile as input into a machine learning algorithm will help predict output decisions on the variability of the actimetric profile induced by the variability of one or more gait parameters that can indicate if a fall is likely to happen. In the next phase of our research, we will consider dealing with data analysis of the actimetric profile of fall-sensitive patients taking advantage of the gait parameters extracted from our WMFL platform.

This dissertation is dedicated to my mother whom without her prayers this work wouldn't have been reached an end. I also dedicate it to the memory of my father. I would have loved him to see me reach my goals.

R ésum é :

La croissance et le vieillissement sont in évitables pour la race humaine. Chez les personnes âg ées, le vieillissement est souvent accompagn é par de nombreuses formes de maladies et de dangers dont les chutes qui affectent la qualit é de vie et qui posent un enjeu socio-économique. Mais les chutes sont évitables. Les acteurs de sant é, les scientifiques et les chercheurs combinent actuellement des efforts pour d évelopper des syst èmes de d étection et de pr édiction des chutes. Dans le contexte de la pr édiction des chutes, l'objectif de cette th èse est d' élaborer le profile actim étrique des patients sensibles aux chutes afin de les alerter d'une possible chute. Ceci consiste principalement à d évelopper un syst ème capable de surveiller les param ètres de la marche des personnes durant leurs activit és quotidiennes avec un minimum d'intrusivit é. Dans une premi ère contribution, nous avons propos é une classification g én érique des syst èmes li és aux chutes en fonction du d éploiement de leurs capteurs. Nous avons distingu é les syst èmes portables, les syst èmes nonportables et les syst èmes qui combinent les deux. En se basant sur cette classification, nous avons propos é notre plateforme WMFL v1.0 dans une deuxi ème contribution. WMFL combine une chaussure équip ée par des capteurs de force avec des dalles o ù nous avons int égr és des capteurs optiques infrarouges. La fusion de ces deux syst èmes assure une pr évention à l'int érieure et à l'ext érieure des locaux. WMFL peut être aussi d éploy ées dans une clinique. Dans une troisi ème contribution, nous avons propos é une m éthode de pr édiction des chutes en se basant sur l'analyse du d éplacement du centre de pression (projet é du centre de masse) sur la surface plantaire du pied durant la marche. La m éthode utilise la fen être glissante spatio-temporelle pour alerter le patient d'une chute potentielle et pour d éterminer le risque de chute à la fin de la marche.

Mots-cl és : Chutes, d étection et pr édiction des chutes, centre de pression, analyse de la marche

Abstract:

Growth is the normal change of the human body and getting old is inevitable to human race. As a result, elderly people are subject to many forms of diseases and dangers among which falls are considered very serious in terms of quality of life and socio-economic costs. But falls can be manageable. Health practitioners, scientists and researchers currently combine efforts to develop systems capable of detecting and predicting falls. In the context of fall prediction, the goal of this thesis is to elaborate the actimetric profile of fall-sensitive patients to alert them from a potential fall. It consists mainly of developing a system capable of monitoring gait and balance parameters during their daily activities with minimum intrusiveness. These are usually assessed in clinical settings using high-cost tools. In our first contribution, we proposed a generic classification of fallrelated systems based on their sensors deployment. These are classified as Wearable, Non-Wearable and Fusion Systems. Based on the generic classification, we proposed the WMFL v1.0 platform in our second contribution. WMFL fuses a Foot Wear Force Sensing device with an Ambient system using IR-sensing floor tiles. The platform can be deployed at homes or in clinics. It ensures an indoor/outdoor protection. In a third contribution, we proposed an early fall detection approach to determine the risk of falling by analyzing the displacement of the Center of Pressure projecting the amount of sway of the Center of Mass on the foot plantar surface. The method uses the spatio-temporal sliding window to alert the patient of a potential fall.

Keywords: Elderly falls, Fall detection, Fall prediction, Center of Pressure, Sliding window, Gait analysis

Figure 1 .

 1 Figure 1.1 presents a comprehensive view on the overall organization of the thesis. It emphasizes the logical links between the various contributions found in each chapter.

FIGURE 2 .

 2 FIGURE 2.1 -Three-dimensional classification and interaction of fall risk factors.

FIGURE 2 . 2 -

 22 FIGURE 2.2 -Three-level impact of fall consequences.

FIGURE 3 .

 3 FIGURE 3.1 -A three-category based global classification scheme of fall-related systems

FIGURE 5 . 1 -

 51 FIGURE 5.1 -System design architecture of the Force Sensing Shoe

Figure 5 .

 5 3 illustrates the location and the shape of the sensors (shown in grey) of our instrumented insole compared to Shu et al. (Figure 5.3-Left) and Perttunnen et al. (Figure 5.3-Right). Table

FIGURE 5 . 3 -

 53 FIGURE 5.3 -Location and shape of sensors on the foot plantar surface. Left : Comparison with Shu et al. Right : Comparison with Perttunnen et al.

FIGURE 5 . 4 -

 54 FIGURE 5.4 -Tekscan "FlexiForce" sensors. Left : FLX-A301, Center : FLX-A401, Right : FLX-A502

Figure 5 . 5 -

 55 Right illustrates the realization of the insole. We note that the insole is made of a thin (0.3 mm) flex Printed Circuit Board (PCB).

  Figure B.1 in Appendix B, illustrates the PCB design of the instrumented insole.

FIGURE 5 . 5 -

 55 FIGURE 5.5 -Implementation and prototyping of the instrumented insole. Left : Insole software design, Right : Realization using a flex PCB

FIGURE 5 . 6 -

 56 FIGURE 5.6 -Design architecture of the wearable electronic board

  . The power supply circuit schematic is shown in Figure B.3 of Appendix B.

Figure 5 .

 5 Figure 5.7 displays the CoP trajectory of 5 stances performed by one subject. Notice that the CoP starts at the bottom of the foot at the ankle and reaches the forefoot at the toe.The number of CoP points is dependant on the number of sensors and the transmission rate. The CoP trajectory as well as the instantaneous GRF are important to study the kinetic of the human contact with the ground in order to assess gait. This parameter will be considered in determining the amount of sway in the Medial-Lateral plane and estimating the risk of falling.

FIGURE 5 . 7 -

 57 FIGURE 5.7 -Left and Right feet CoP trajectory for 5 stances

FIGURE 6 .

 6 FIGURE 6.1 -Center of Pressure (CoP) trajectory at the foot plantar surface

FIGURE 6 . 2 -

 62 FIGURE 6.2 -Temporal sequence of the gait cycle showing the gait phases (Stance and Swing) of both Left and Right feet (HC : Heel Contact, TO : Toe Off and double limb support), the CoP trajectory and the vector V j of instantaneous CoP for each stance

FIGURE 6 . 3 -

 63 FIGURE 6.3 -One sway at the beginning of the walk

FIGURE 8

 8 FIGURE 8.1 -System design architecture of the Infrared Sensing Floor

FIGURE 8 . 2 -

 82 FIGURE 8.2 -Infrared reflective object sensor Left : QRD1114, Center : Functioning principle, Right : Packaging dimensions

Figure 8 . 3 .

 83 The circuit design of the IR-Sensor matrix is illustrated in Figures C.1 through C.4 in Appendix C. The layout design of the IR-sensor matrix is also illustrated in Figure C

FIGURE 8 . 3 -

 83 FIGURE 8.3 -Sensor distribution layout on the connection matrix

  Figure 8.4 illustrates the mechanical design of the floor plate. The design of the metal chassis was drawn using Autocad software. The top metal layer is laser cut and the square legs are welded manually on the 8 corners.

FIGURE 8 . 4 -

 84 FIGURE 8.4 -Floor plate mechanical chassis design

FIGURE 8 . 5 -

 85 FIGURE 8.5 -Infrared sensing floor plate Top : Floor plate mechanical and electronic layers, Bottom : Floor plate final assembly

  , and C.7 of Appendix C.

FIGURE 8 . 6 -

 86 FIGURE 8.6 -Design architecture of the plate controller electronic board

  Figure C.6 of Appendix C.

  Figure C.8 of Appendix C.

FIGURE 8 . 7 -

 87 FIGURE 8.7 -Design architecture of the mediator electronic panel

  UART, 1 SPI, 1 I2C) to connect the different communication interfaces and the RTC. The microcontroller is programmed using the In-Circuit Serial Programming (ICSP) interface available in the circuit design of the mediator electronic panel. The microcontroller unit and its connection to the rest of the modules is shown in the design of the mediator circuit schematic illustrated in Figures C.8 and C.9 of Appendix C.8.3.3.7 POWER INTERFACE BLOCKThe power interface at the mediator level is simply a circuit with two regulators allowing 12 VDC and 5 VDC supply voltages to be used. These voltage regulators are used to supply the low power components such as the microcontroller and the communication interfaces with adequate voltage (5V or 3.3V) for operation. The regulators have coupling capacitors for filtering the supply voltage. The supply voltage of the mediator electronic panel is also delivered from the main power supply of the system located inside the mediator panel enclosure. The power interface circuit is shown in the schematic of the mediator in Figure C.8 of Appendix C.

  Figure 8.8 illustrates an example showing the function of our clustering process. In this example, the input to the algorithm is a 15 x 15 binary matrix representing the statuses of active and inactive sensors of the IR-sensor matrix. Active and inactive sensors are represented by a binary '1' (red color) and binary '0' (grey color) respectively. The output of the algorithm is a matrix where active sensors are clustered into groups 1 and 2 as shown in the figure. The clustering algorithm runs in real time while the software is still gathering data.

FIGURE 8 . 8 -

 88 FIGURE 8.8 -Input and output of the clustering process

  distance D and D √ 2 Output: Cluster of sensors with common GroupID function SetAllSensorGroupID(S ) //sets GroupID to all active sensors to -1 function SearchForNeighbor(GroupID, Current s i, j ) //searches for active neighbor of the current active sensor and assigns it the same GroupID 1: for each new matrix S do

FIGURE 8 . 10 -

 810 FIGURE 8.10 -Layout of the ACA and RCA on the infrared-sensing plate

FIGURE 9 . 1 -

 91 FIGURE 9.1 -Modular architecture of the WMFL Software Application

FIGURE 9 . 2 -

 92 FIGURE 9.2 -Functional blocks of the data processing module

FIGURE 9 . 3 -

 93 FIGURE 9.3 -Data processing module. Left : Data Pre-Processing flow chart, Right : Data collection flow chart

3 .FIGURE 9 . 4 -

 394 FIGURE 9.4 -Parameters extraction flow chart

FIGURE 9 . 5 -

 95 FIGURE 9.5 -Data post-processing flow chart

Figure 9 .

 9 Figure 9.6 illustrates the whole picture of the database model used to save subjects' data and test results. The database is still in its early development phase. More tables and fields can be added while we expand the development of the WMFL v1.0 Software Application in the next version.

FIGURE 9 . 6 -

 96 FIGURE 9.6 -Database design model for the WMFL Software Application

9. 3

 3 .1 GUI FOR REAL TIME DATA VISUALIZATION This is the main GUI form view of the WMFL v1.0 Software Application. It consists of three sub-form views :

FIGURE 9 . 7 -

 97 FIGURE 9.7 -Main GUI of the WMFL v1.0 Software Application

FIGURE 9 . 8 -

 98 FIGURE 9.8 -Infrared sensing plate testing GUI

FIGURE 9 . 9 -

 99 FIGURE 9.9 -Force Sensing Shoe testing GUI

FIGURE 9 . 10 -

 910 FIGURE 9.10 -Subject information GUI

FIGURE 10

 10 FIGURE 10.1 -Proposed WMFL v1.0 system design architecture

10. 3 . 2

 32 PARAMETERS FROM THE INFRARED SENSING FLOORParameters extracted from the Infrared Sensing Floor belong to the spatial-temporal category. Recall that our proposed floor is composed of infrared sensing plates where the footprint/shoe print is detected when the person steps on it. This basic component helps generate the rest of the spatial-temporal gait parameters. Depending on the type of each FIGURE10.2 -Spatial-temporal gait parameters as displayed on the WMFL v1.0 Software Application

  .

FIGURE 10 . 3 -

 103 FIGURE 10.3 -Base of Support as displayed on the WMFL v1.0 Software Application

FIGURE 10 . 4 -

 104 FIGURE 10.4 -Center of Feet (CoF) as displayed on the WMFL v1.0 Software Application

FIGURE 10 . 5 -

 105 FIGURE 10.5 -Distance between CoF and CoP as displayed on the WMFL v1.0 Software Application

10. 5 FIGURE 10 . 7 -

 5107 FIGURE 10.6 -Elderly photos performing a free walk on the Infrared Sensing Walkway

Table 2

 2

.1.

TABLE 2 .

 2 1 -Classification of fall risk factors according to their measurability

					Fall risk factors	Measurability
					-Age	Measurable
	Factors related	to the person	-History of falls -Acute or chronic pathologies (e.g. Parkinson's, osteoporosis, diabetes, etc.) -Neurological functions -Gait, balance, lower limbs joint function, muscle strength	Measurable Non-measurable Non-Measurable Measurable
					-Cardiovascular status (e.g. blood pressure, heart rate, rhythm)	Measurable
					-Cognitive functions (Visual acuity)	Measurable
					-Fear of falling, independence, efficacy	Non-measurable
	Factors	related to the	environment	-Outdoor environment (e.g. uneven paving, ice) -Indoor environment (e.g. loose carpets, sloppy floors, cumbersome furniture) -Foot wear, clothes	Non-measurable Non-measurable Non-measurable
	2.5 CONSEQUENCES OF FALLS

Falls continue to be a predominant cause of adverse consequences on many levels.They do not only affect the person himself by increasing his dependencies, but also impact his environment. Review article

[START_REF] El-Bendary | Fall detection and prevention for the elderly : A review of trends and challenges[END_REF]

provides a five-dimension classification based on the physical, psychological, social, financial, medical and governmental consequences. The five-dimensions classification proposed by El-Bendary et al. can be simply reassembled into three groups that can be related to the person, the environment and the government. Figure

2

.2 illustrates the new hierarchy based on which fall management strategies can be established to reduce fall consequences.

TABLE 3 .

 3 1 -Existing FD and FP solution with respect to the global classification scheme

	# Existing paths	Example Ref.
	1 FD → WS → BW	[Tolkiehn et al., 2011]
	2 FD → NWS → AMB	[Yazar et al., 2014]
	3 FD → NWS → VIS	[Rougier et al., 2011a]
	4 FD → FS → (BW + VIS)	[Leone et al., 2008]
	5 FD → FS → (BW + SP)	[Chang et al., 2011]
	6 FD → FS → (BW + AMB)	[Huang et al., 2009b]
	7 FD → SP	[Fang et al., 2012]
	8 FP → SP	[Majumder et al., 2013a]
	9 FP → WS → BW	[Liu et al., 2014]
	10 FP → WS → FW	[Light et al., 2015]
	11 FP → NWS → AMB	[McGrath et al., 2012]
	12 FP → NWS → VIS	[Stone et al., 2013]
	13 FP → NWS → RF

TABLE 3 .

 3

	Sensor	Measurement
	Accelerometer	Acceleration, velocity, position
	Gyroscope	Angular velocity, flexion angle
	Magnetometer	Orientation
	Barometer	Change of pressure
	Goniometer	Flex angle
	Inclinometer	Tilt angle
	Ultrasonic	Distance, clearance
	Force and pressure Vertical force, pressure

2 -Typical sensors used in Wearable FD and FP Systems

TABLE 3 .

 3

	3 -Vital signs monitoring devices
	Sensor	Measurement
	Pressure	Blood pressure
	CO2 gas	Respiration
	Electrocardiography (ECG)	Cardiac activity
	Electroencephalography(EEG) Brain activity
	Electromyography(EMG)	Muscle activity
	Electrooculography (EoC)	Eye movement
	Galvanic skin Response (GSR) Perspiration
	Oxymeter	Blood oxygen
	Thermal	Body temperature
	3.6.2 NON-WEARABLE SYSTEMS SENSORS

Classification of fall detection systems and data processing techniques Data processing techniques Non-wearable systems (NWS) or Context-aware systems New New Wearable systems (WS) Ambient systems (AMB) Vision systems (VIS) Fusion systems (FS) Smartphone systems (SP) Analytical methods (ANM)

	3.7.1 ANALYTICAL METHODS
	3.7 DATA PROCESSING TECHNIQUES IN FD AND FP SYSTEMS Fall data processing techniques are dependant on the parameters extracted from the Analytical Methods are based on traditional techniques that use statistical models for gaining interpretation on data for prediction (e.g. linear regression, time series, transfor-mations, etc.). Among these methods are Thresholding techniques (TH), that is if a fall is reported when peaks (level of impact), valleys or other shape features in data signals are detected. Such methods are commonly used in wearable fall detectors with inertial sensors to distinguish between posture (inactivity) and basic motion patterns (activity). Ambient based systems use event sensing techniques through vibrational data that can be useful for monitoring, tracking and localization. Camera based systems use image processing techniques that extract spatial-temporal parameters (e.g. ratio of silhouette height, weight, orientation of main body axis, body width, skin color, etc.) to identify lying or standing postures in the scene. Other image processing techniques use vector ana-lysis to detect abrupt motion. Therefore, various analytical strategies can be adopted to classify fall from non-fall events. 3.7.2 MACHINE LEARNING METHODS Machine Learning Methods rely on complex algorithms to get close insight on data and predict output decisions. Starting from observation and then classification, wearable, am-bient and camera based fall detectors can benefit from techniques such as Support Vec-tor Machine (SVM), Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge, Multilayered Perceptron, Naive Bayes, Decision Trees, ZeroR, and OneR to gain insights in to the data to detect and even predict future falls. An investi-gation on the performance of the afore mentioned classification techniques is detailed in [Kerdegari et al., 2012]. 3.8 REVIEW ON FD AND FP SYSTEMS Tables 3.5 and 3.6 provide the state of the art of some existing FD and FP systems respectively. An explicit search was conducted deploying the main databases like Google Scholar, IEEEXplore, PubMed. Studies are sorted according to their number of citations, the year of publication (> year 2006), the type of sensor deployment and their application of various Analytical and Machine Learning Methods. TABLE 3.5 -Classification of Fall Detection systems with respect to their sensors deployment and data processing techniques Thresh-holding (TH)
	sensors. Wearable, Non-Wearable and Fusion Systems for both FD and FP can benefit
	from techniques using Analytical Methods (ANM) or Machine Learning Methods (MLM).

TABLE 3 .

 3 6 -Literature review on Wearable, Non-wearable and Fusion Fall Prevention Systems

Article Methods Experiments and results Ref. Sensor (class) Sensor location (Obtrusiveness) Parameters analyzed Data Processing technique Experimental environment Subjects Results Wearable Systems (WS)

	Sensitivity = 71% 3.9 PROPOSED WMFL V1.0 PLATFORM Specificity = 73% Recognition rate = 90% to 100% Sensitivity = 71% Specificity = 73% Inter correlation coefficient > 75% Correlation with pressure sensor = 80%	Symmetry of stride	in normal people	Designed force plate	= AMTI force place	Fall detection	SEN =90%	SP= 100%	Accuracy = 94%	Fall prevention	Probability of	Fall Alarm (FA)	function of ADL	+ fall event (FE)	Emotion	Accuracy = 84%	Wellness indices	β1 and β2 < TH=0.5	→ assistance
		Elders			Elders			Elders				Dwelling elders		Middle-aged	Adults			Young volunteers						Young volunteers	Elders
		Clinic			Lab			Clinic				Elderly home		Lab	+	Home			Lab					Setup	(ice skating)	+	Apartment	Apartment
	acceleration signals Logistic regression	from a Directed Routine models	acceleration and Hidden Markov Model angular velocity	acceleration signals Logistic regression	from a Directed Routine models	Non-Wearable Systems (NWS)	CoP	(Mean & RMS distance) TH (ANM) sway ML-AP	(velocity, length, frequency)	Joint and CoM Multiple additive (position, speed, regression trees acceleration) (MART) walking speed (MLM) direction of Progress	Multivariate	GRF and CoP multi-scale Entropy	(ANM)	Fusion Systems (FS)		Impact orientation TH + + Contact recognition DBN (ADL)	Naive Bayes	Emotion +	+ K-mean clustering	behavior (activities) +	TH
	Waist	(Medium)	Waist	(Medium)	Waist	(Medium)			Floor	(Low)		Vicinity	(Low)			Floor	(Low)						Waist	(Medium)	Wrist, finger	+	Home appliances	sensing units	(Low)
	Triaxial	Accelerometer	(BW)	Triaxial	Accelerometer	(BW)	Accelerometer	Gyroscope	(BW)			Pressure mat	(AMB)		Kinect	(VIS)			Force plate	(AMB)			Smartphone	Accelerometer	Oximeter	+	PIRMotion	pressure mat	door contact	Power detector	(SF+AMB)	ECG, GSR	+	Contact switches	+	Zigbee node	(BW+AMB)
		[Liu et al., 2011]			[Shi et al., 2009b]			[Liu et al., 2014]				[McGrath et al., 2012]		[Gabel et al., 2012]	[Stone et al., 2012]	[Stone et al., 2013]			[Gonz ález et al., 2014]	[Marasović et al., 2009]						[Burchfield et al., 2007]	[Suryadevara et al., 2012]

TABLE 5 .

 5 1 -Location of the force sensors with respect to load distribution

Load distribution / Sensor location Shu et al. Perttunnen et al. Our insole

	Fore foot 32%	Areas 6 to 15	Areas 9 to 16	FS5 to FS8
	Mid foot 8%	Areas 4, 5	Areas 4 to 8	FS3, FS4
	Rear foot 60%	Areas 1 to 3	Areas 1 to 3	FS1, FS2

TABLE 5 .

 5 2 -Tekscan "FlexiForce" sensors specifications

		FLX-A301	FLX-A401	FLX-A502
	Linearity		<+/-3% (at 50% of load)
	Dynamic Range	0 to 4,448 N	0 to 32,000 N	0 to 44,482 N
	Hysteresis		<4.5% (at 80% of load)
	Temperature sensitivity		0.36% /

o C Sensing Area 9.53 mm diam. 25.4 mm diam. 50.8 mm x 50.8 mm

sensor Interface MCP6004 Op-Amp Force sensor Interface MCP6004 Op-Amp Power Management (5V and 3.3V) Communication Interface Bluetooth & ZigBee IMU Unit Notification Input from Force Sensors Input from Force Sensors

	Processing and control PIC18F45K22	Force
	Microcontroller

The hardware implementation of the wearable electronic board is illustrated in Figures B.

2, B.3 and B.4 of Appendix B.

TABLE 5 .

 5

	Subject	Foot Size (EU)	Mass (Kg)	Weight (N)	Quiet Standing (N)	Error (%)
	S1	44	75.4	739.67	731.04	1.17%
	S2	42	73.5	721.04	695.82	3.5%
	S3	42	59.2	580.75	557.01	4.09%
	S4	42	81	794.61	773.13	2.7%
	S5	42	66.1	648.44	631.47	2.62%
	S6	44	97.6	957.46	949.8	0.8%
	S7	44	103	1010.43	1004.25	0.61%
	S8	43	92.9	911.35	899.58	1.29%
	S9	44	104	1020.24	1009.25	1.08%
	S10	44	100.3	983.94	968.54	1.57%
	Average		85.3 Kg 836.89 N	823.78 N

3 -Accuracy of the Force Sensing Shoe with respect to a digital weighing scale

Wearable Electronic Board on Belt Wearable Electronic Board Instrumented Insole Shoes and Instrumented Insole FIGURE

	Force Sensing Shoe
	Prototype Assembly

5.8 -Force Sensing Shoe prototype components FIGURE 5.9 -Force Sensing Shoe prototype assembly

TABLE 6 .

 6 1 -Mean thresholds Th L and Th M for normal stances

	Subjects	R = [r j , j = 1→ m = 20]	Th L	Th M
	S1	0.128 0.113 0.089 0.115 → 0.118 0.185 0.089
	S2	0.102 0.139 0.093 0.219 → 0.135 0.183 0.091
	S3	0.111 0.086 0.095 0.079 → 0.111 0.171 0.086
	S4	0.269 0.133 0.146 0.190 → 0.128 0.183 0.101
	S5	0.254 0.141 0.165 0.187 → 0.255 0.187 0.080
	Mean Th L , Th M		0.182 0.089
	6.8.2.2 THE ALTERED WALK

  The design of the mediator has room for a Bluetooth communication module for future use with other systems such as a Smartphone. The wireless communication interface is managed by the processing and control block using it Serial Peripheral Interface (SPI). The ZigBee module used is the "Xbee click" fromMikroelektronika (www.mikroe.com). This module is integrated in the circuit schematic design of the mediator panel illustrated in Figure C.8 of Appendix C.8.3.3.4 REAL TIME CLOCK BLOCKThe Real Time Clock (RTC) is a hardware clock module. It is used to provide the data with the correct time stamp so that the software interface on the computer can reconstruct the data. At the time of the research, this module was not used and the time stamp is currently set by the computer clock. It communicates with the processing and control block using its Inter-Integrated Circuit interface (I2C). The RTC module is the "RTC click" from Mikroelektronika (www.mikroe.com). This module is integrated in the circuit schematic design of the mediator panel illustrated in Figure C.8 of Appendix C.

	The module
	is integrated in the circuit schematic design of the mediator panel illustrated in Figure C.8
	of Appendix C.
	8.3.3.3 WIRELESS COMMUNICATION INTERFACE BLOCK

The wireless communication interface allows the mediator panel to collect additional data from other systems for more parameter extraction. In our case, the Infrared Sensing Floor receives data from our Force Sensing Shoe previously described in Chapter 5 using a wireless ZigBee module.

8.3.3.5 COMMAND AND VISUALIZATION INTERFACE BLOCK

TABLE 8 .

 8 1 -Tabular information of the recruited participants

	Subject	Age/ Gender	Height (cm)	Weight (Kg)	Foot size (EU)	Shoe size (EU)	Type of shoe	Comment
	S1	26 /M	203	150.9	47			47	Classic
	S2	19 /M	179	75.7	46			46	Running shoes
	S3	22 /M	182	104.4	45			45	Running shoes
	S4	23 /M	180.5	108.8	44			44	Converse	Flat foot
	S5	31 /M	189	97.4	43			43	Converse
	S6	24 /M	172.5	84.7	42			42	Converse
	S7	33 /M	173	96.4	41			41	Running shoes	Falt foot
	S8	23 /F	168.5	80.7	40			40	Sandal
	S9	42 /F	166	65.5	38			38	Converse
	S10	31 /F	164.8	55.7	37			37	Sandal
	S11	21 /F	158	59.2	37			37	Balerina
	S12	24 /M	181.5	81.4	45			45	Converse
			TABLE 8.2 -Participants statistics
					Age	Height (cm)	Weight (Kg)
			Min	19	158		55.7
			Max	42	203		150.9
			Average 26.58 176.48	88.4
			Std Dev	6.52	12.13	26.03
			Infrared-Sensing Plate / IR-Sensor matrix	3 cm
									3 cm
									Active Contact Area
									(ACA)
									Real Contact Area
									(RCA)

TABLE 8 .

 8 3 -RCA and ACA measurements for bare foot experiment

	Subjects/ Results	RCA(cm2)	Bare foot ACA(cm2) ACA Range Avg ACA Std ACA M. Error	% Error
	S1	259	243-306	266.4	21.3	7.4	2.86
	S2	230	207-252	232.2	17.39	2.2	0.96
	S3	238	207-270	243	18.49	5	2.1
	S4	233	225-270	237.6	13.55	4.6	1.97
	S5	206	189-234	212.4	12.15	6.4	3.11
	S6	186	171-210	193.5	13.58	7.5	4.03
	S7	200	189-225	208.8	11.85	8.8	4.4
	S8	193	180-225	202.5	14.85	9.5	4.92
	S9	168	162-180	171	7.35	3	1.79
	S10	166	153-198	175.5	13.58	9.5	5.72
	S11	154	144-171	153	8.49	-1	0.65
	S12	221	207-243	224.1	20.44	3.1	1.4
	Error Range						0.65-5.72%
	Reliability Index			99%

TABLE 8 .

 8 4 -RCA and ACA measurements for shoes experiment

Subjects/ Results Shoes RCA(cm2) ACA(cm2) ACA Range Avg ACA Std ACA M. Error

							% Error
	S1	287	288-333	300.6	14.82	13.6	4.74
	S2	266	261-306	279.9	14.36	13.9	5.23
	S3	266	261-297	276.3	12.76	10.3	3.87
	S4	218	207-270	230.4	24.07	12.4	5.69
	S5	233	234-261	244.8	10.22	11.8	5.06
	S6	233	234-252	244.8	7.1	11.8	5.06
	S7	248	234-288	265.5	19.09	17.5	7.06
	S8	225	207-252	228.6	13.55	3.6	1.6
	S9	181	180-216	192.6	11.38	11.6	6.41
	S10	166	153-189	171.9	12.33	5.9	3.55
	S11	146	144-171	153.9	8.95	7.9	5.41
	S12	236	216-288	246.6	20.44	10.6	7.29
	Error Range						1.6-7.29%
	Reliability Index			99.23%

Input raw data packets Post-Processing Two separate lists of mapped sensor values Gait and balance parameters Graphs, charts, tables, reports. Two separate lists of raw data packets from shoe and floor

	Data Collection	Pre-Processing	Parameter Extraction

  The step time (t step ) is the time frame between the first appearance of one footprint to the first appearance of the other footprint on the Infrared Sensing Walkway. During the parameters extraction, the software saves the time stamps at which the footprint appeared (t a ). The Step Time is computed by subtracting the time of appearance of the first footprint from that of the footprint of the opposite foot. The Step Time is given by Equation10.4. The Step Length and Time at shown in Figure10.2.

	t step = t a( f p i+1 ) -t a( f p i )	(10.4)
	10.3.2.5 NUMBER OF STEPS
	The number of steps (n steps ) is simply obtained by counting the number of footprints (n f p )
	performed by the person on the Infrared Sensing Walkway. Using Equation 10.5, the
	number of steps is :
	n steps = n f p -1	(10.5)
	10.3.2.6 STANCE LENGTH AND TIME

  136CHAPITRE 10. COMBINING FORCE SENSING SHOES AND INFRARED SENSING FLOOR 10.3.2.8 STRIDE LENGTH AND TIMEThe stride is the equivalent of a gait cycle. the stride length is the algebraic sum of two successive step lengths. It is similar to the step length but for the same foot (left or right). In the software application, the stride length is either determined by adding two step lengths or from the distance between the points at heel contact of two successive footprints of the same foot.The time that one stride takes to occur is called the stride time (t stride ) or the Gait Cycle Duration. It is determined by subtracting the time of appearance (t a ) of two successive footprints of the same foot. It can be computed by adding the stance time and the swing time. Equation10.8 determines the stride time or the gait cycle duration. The stride length and time are shown in Figure10.2.Gait Cycle Duration= t stride = t a( f p i )t a( f p i-1 ) = t stance + t swing (10.8) 10.3.2.9 DOUBLE SUPPORT TIMEReferring to the two phases of the gait cycle (stance and swing), there is a time when both feet are on the ground. This time is known as the Double Support time which is in other words the definition of walking. As a matter of fact, as the speed of walking increases, the double support time decreases and running begins. The total Double Support time occupies 20% of the gait cycle divided between two similar parts called initial and terminal. These being the parts when the body weight transfers for ipsilateral to contralateral and vice-versa. In our software application, the Double Support time (t DS ) is determined by subtracting the time of appearance of a footprint (t a ) from the time of disappearance (t d ) of the footprint of the opposite foot. The Double Support time may not exist if (t d < t a ). This condition may occur if the person is walking at a frequency higher than the plate sampling frequency or in other words when the person is increasing his/her pace. Nevertheless, the Double Support time can be easily determined by subtracting the swing time (t swing ) from the stance time (t stance ) according to Equation10.9. The Double Support time is shown in Figure10.2.t DS = t d( f p i )t a( f p i+1 ) =t stancet swing (10.9) 10.3.2.10 CADENCEThe cadence corresponds to the rate at which the person is stepping on the floor. It is also known as the walking frequency. It is expressed by the number of steps per minute (steps/min). Knowing the number of steps (n steps ) on the whole walkway and the time difference between the time of appearance of the last and the first footprints according to the Equation10.10.The walking speed represents the speed at which the person crosses the Infrared Sensing Walking. It is obtained by dividing the sum of the step lengths by the sum of the step times. The speed is also the product of the cadence and the stride length. The walking speed is determined using Equation10.11 as follows.

	10.3.2.11 WALKING SPEED
	S peed =	distance time	=	(cadence • stride lenght) 120	(10.11)
	10.3.2.12 FOOT PROGRESSION ANGLE
	Cadence =	n steps • (t a( f p n ) -t a( f p 1 ) ) 60	(10.10)

7. The Swing Time is shown in Figure 10.2. t swing = t a( f p i )t d( f p i-1 ) (10.7)

  For our experiments, we recruited 16 subjects (8 young students and 8 elderly subjects). The young volunteers were students from the Faculty of Engineering at the Antonine University, with mean age 24.12 ± 0.99. The elderly volunteers were recruited from St. Antonios Elderly Care, hadat-Lebanon. They have a mean age of 77.75 ± 5.41. St. Antonios elderly care is in charge of all the elders in Hadat and the surrounding area. It is a day care where elders of the region come together each day to participate in entertainment activities and then go back to their homes for the night. The recruited subjects for the study were all healthy and capable of ambulating normally without any walking aids. The exclusion criteria were any disabilities or pain (neurological or orthopedic) in the lower limbs (knee or foot) that could affect the natural free gait. Elders with history of falling were also excluded from the experiments. The recruitment of the elderly subjects was approved by the manager of the St. Antonios Elderly Care and all participants freely gave their written consent to participate after being informed about the study procedure. The elders were transported by car from St. Antonios Elderly Care to the lab at the Antonine University to participate in the experiments. Table10.1 gives an information summary (Age, Weight, Height) on the participating subjects during the experiments. The table shows the information as the mean (Avg) and the standard deviation (Std). COMBINING FORCE SENSING SHOES AND INFRARED SENSING FLOOR 2 :young ; week 3 :elders ; week 4 :young. In other words, each participant attended 2 sessions separated by a 2 weeks time period. The sessions are performed at rate of 1 or 2 sessions per day. During these sessions each participant performed 3 consecutive trials or walks on the WMFL platform. Each session lasted approximately 1 hour.

	10.4.2 EXPERIMENTAL PROTOCOL
	10.4.2.1 PARTICIPATING SUBJECTS
	TABLE 10.1 -Participating subjects information summary
	n Age (years) Weight (Kg) Height (m)
	Young 8 24.12(0.99)	84.81(4.36) 1.775(0.038)
	Elders 8 77.75(5.42)	78.75(5.41) 1.676(0.069)
	10.4.2.2 TESTING PROCEDURE

The testing procedure was carried over a period of 4 weeks during which each participating subject (young and elderly) performed a total of 2 distinct sessions. The session sequence for the participants over this time period is as follows : week 1 :elders ; week 140CHAPITRE 10.

TABLE 10 .

 10 2 -Selected gait parameters for the actimetric profile classification

	Gait Parameter	Unit	Description
	Base of Support area	cm 2	Area that includes every contact point of the body with the support
	Step length	cm	Distance between heel contact point of left and right feet
	Step time	msec	Time between heel contact point of left and right feet
	Stride length	cm	Distance between heel contact point of the same foot (left-left or right-right)
	Stride time	msec	Time between heel contact points of the same foot
	Swing time	msec	Time recorded when the foot is not in contact with the floor
	Stance time	msec	Time recorded when the foot is in contact with the floor
	Double Support time	msec	Time where both feet are in contact with the floor
	Cadence	steps/min Number of steps per minute
	Walking speed	m/s	Speed of the walk

  Based on the 95% confident interval of the ICC estimate, values less than 50% are indicative of poor reliability, values between 50% and 75% indicate a moderate reliability, values between 75% and 90% indicate a good reliability, and finally values greater than 90% are indicative of excellent reliability. The second phase consists of comparing gait parameters between groups. The objective is to identify if the difference between groups is statistically relevant or not, by taking the mean of every parameter for 142CHAPITRE 10. COMBINING FORCE SENSING SHOES AND INFRARED SENSING FLOOR

TABLE 10 .

 10 3 -Test-retest repeatability for each of the gait parameters for both young and elderly subjects

	Gait Parameter	ICC (Young)	ICC (Elders)
	Base of Support	0.999 (0.994 to 1.00)	0.999 (0.995 to 1.00)
	Step length	0.995 (0.977 to 0.999) 0.997 (0.983 to 0.999)
	Step time	0.995 (0.997 to 0.999) 0.991 (0.954 to 0.998)
	Stride length	0.994 (0.971 to 0.999) 0.995 (0.975 to 0.999)
	Stride time	0.973 (0.863 to 0.995) 0.995 (0.975 to 0.999)
	Swing time	0.997 ( 0.986 to 0.999) 0.993 ( 0.966 to 0.999)
	Stance time	0.997 (0.985 to 0.999) 0.995 (0.973 to 0.999)
	Double Support time 0.959 (0.794 to 0.992) 0.953 (0.765 to 0.991)
	Cadence	0.972 (0.862 to 0.994) 0.986 (0.929 to 0.997)
	Walking speed	0.989 (0.947 to 0.998) 0.996 (0.979 to 0.999)

TABLE 10 .

 10 4 -One-Way ANOVA for the young and elderly profiles

	Gait Parameters	Young Elders P-value
	Base of Support (cm 2 )	885.87 806.12	0.003
	Step length (cm)	65.362	52.72	<0.001
	Step time (msec)	349.06 369.31	0.051
	Stride length (cm)	131.48 108.65	<0.001
	Stride time (msec)	748.13 774.625 <0.001
	Swing time (msec)	339.63 342.31	0.663
	Stance time (msec)	341.85 333.25	0.102
	Double Support time (msec) 347.25	380.5	0.008
	Cadence (steps/min)	160.4	154.4	<0.001
	Walking speed (m/s)	1.75	1.40	<0.001

ACKNOWLEDGMENTS

tion is applied on the previous stances keeping the importance of the excess sway of the current stance (6.10). It is noticeable from the results, that the number of alerts has dropped in each scenario when using the logarithmic correlation reducing thereby the hassle on the elder. On the other hand, the determination of the risk of falling remains the same for all scenarios. As a matter of fact, the sliding window technique affects the number of alerts generated whereas the risk is determined using the number of excess sways χ j with respect to R L for the whole walk. Aging is inevitable to all living species and particularly to human race. As the world ageing population is increasing year after year, its consequences will cause a major public health problem on the developed countries. The consequences of ageing leaves the elderly in a state of "unstable incapacity" as the he/she becomes vulnerable to many diseases and incidents that affect his capacity of reaction to the environment and his autonomy of integration in the Activities of Daily Living (ADL). One of these incidents that occur to an elderly person are falls. These involuntary incidents are very critical when they happen and sometimes they are fatal. Their causes are related to the person and his environment.

The psycho-physical after-fall effects become a burden on the person himself as well as his surroundings. These could lead the person to hospitalization if no immediate action is taken. Falls are currently managed by medical institutions. However, with the growing ageing population, medical institutions will soon be short in the number of care givers and won't be able to accommodate this large number of people. Moreover, the reaction to a fall incident is very slow particularly if the elderly is alone at home. Therefore, home care is proving to be an important concern for seniors and their families. But, fall management has not been the occupation of medical institutions only. In fact, research scientists and engineers are currently addressing the problem of falls. The taking care of this problem by the scientific community requires a deep understanding of falls, their causes and their consequences. In Chapter 2, we focused on the importance of screening the measurable risk factors and the interaction among them in elaborating the individual profile. We also highlighted the importance of combining efforts between health-care professionals and scientists for complementary management of fall incidents.

From the scientific perspective, the problem of elderly falls is being tackled according to two research tracks : Fall Detection (FD) and Fall Prevention (FP). In fact, scientists and researchers are putting efforts to develop systems that are capable of detecting or predicting a fall incident (impact on the ground) in order to generate and alarm in case of a fall or an alert if a fall is about to happen (behavior analysis, activity monitoring, gait and balance analysis). In Chapter