Pedro Campos
email: pcampos@uma.pt

Hildegardo Noronha
email: hildnoronha@gmail.com

On the usage of different work analysis methods for collaborative review of large scale 3D CAD models

Human work interaction design is an emerging discipline that aims to encourage empirical studies and conceptualizations of the interaction among humans, their variegated social contexts and the technology they use both within and across these contexts. In this paper we describe and elaborate around the usage of different work analysis methods in a complex, real world work domain: collaborative review of large-scale 3D engineering models. The analysis is based on (i) input from experts in the oil platform engineering field, (ii) previous and related work and (iii) application of different methods considering the recent advances in technology. We conclude that hierarchical task analysis was not effective in obtaining a clear, common vision about the work domain. Storyboarding was the most useful technique as it allowed discovering novelty factors that differentiate the solution and improve the usability of the product, thereby supporting the human work at offshore engineering design and review sessions.

Introduction

Human work interaction design [START_REF] Katre | Human Work Interaction Design: Usability in Social, Cultural and Organizational Contexts Second[END_REF] is an emerging research field within HCI that is focused on the user's experience of tasks (procedures) and the artifact environment (constraints in the work domain). That analysis and interpretation of human work is eventually manifested in the design of novel, technology-based products, systems and applications [START_REF] Katre | Human Work Interaction Design: Usability in Social, Cultural and Organizational Contexts Second[END_REF]. In this paper, we report on a seven-month research study around the requirements elicitation, scenario design and storyboarding processes for creating a new Virtual Reality (VR) distributed application to support a complex work domain: the collaborative review of large scale 3D engineering models, in the context of the oil and gas industry at a very large organization [START_REF] Santos | A collaborative VR visualization environment for offshore engineering projects[END_REF].

The current way of designing industrial plants relies on the communication among experts in several areas of the field, and on tools that allow the specification and simulation of the site. VR resources are used to visualize and interact with complex 3D environments in real time. Several engineering simulations employ VR to foresee the results of complex industrial operations.

In this paper, we analyze the user tasks at stake during collaborative sessions of 3D CAD models design and review in the specific context of the oil and gas industry. By studying the users' work and needs, the related existing work and the possibilities that recent advances in multimodal technologies, we expect to shed new light into how an integrated environment should be conceived and designed in order to positively influence the collaboration levels between dispersed teams of engineers that need to review oil platform problems and to design solutions for those problems.

Our main contribution to the Human Work Interaction Design (HWID) field is the comparison of the effectiveness of different work analysis and design methods towards establishing a common vision regarding a new product for the oil and gas industry engineering models' review, in a collaborative manner. This is especially important for gaining new insight about the relative advantages between the different methods in a highly complex, real world work domain.

Background and Related Work

Human Work Interaction Design (HWID) is an emerging approach that promotes a better understanding of the relationship between work-domain based empirical studies and the iterative design of prototypes and new technologies [START_REF] Katre | Human Work Interaction Design: Usability in Social, Cultural and Organizational Contexts Second[END_REF]. HWID's goal is to encourage empirical studies and conceptualizations of the interaction among humans, their variegated social contexts and the technology they use both within and across these contexts.

To achieve this, HWID promotes the use of knowledge, concepts, methods and techniques that enable user studies to procure a better apprehension of the complex interplay between individual, social and organizational contexts and thereby a better understanding of how and why people work in the ways they do. Therefore, one of the main characteristics of HWID as an interaction design approach is to focus the analysis on the how's and why's of people's work. HWID also tries to promote a better understanding of the relationship between work-domain based empirical studies and iterative design of prototypes and new technologies [12]. HWID's roots lie in Cognitive Work Analysis (CWA) [START_REF] Katre | Human Work Interaction Design: Usability in Social, Cultural and Organizational Contexts Second[END_REF][START_REF] Vicente | Cognitive Work Analysis: Towards Safe, Productive, and Healthy Computer-Based Work[END_REF]. Cognitive Work Analysis (CWA) is a multidisciplinary framework for the analysis, design, and evaluation of human work developed by Rasmussen, and colleagues [START_REF] Rasmussen | Cognitive systems engineering[END_REF]. Its purpose is to guide the design of technology for use in the work place. CWA helps an analyst identify the activities and agents that are needed for a system to effectively fulfill its functional purpose. CWA can also be regarded as a formative process that focuses on an ever-increasing number of dynamic constraints that systems present nowadays, rather than prescriptive methods of working.

Storyboarding [START_REF] Khai | Storyboarding: an empirical determination of best practices and effective guidelines[END_REF] is a common technique in HCI and design for demonstrating system interfaces and contexts of use. Despite its recognized benefits, novice designers still encounter challenges in the creation of storyboards. Many researchers have studied the benefits and disadvantages of storyboards, including Truong and colleagues [START_REF] Khai | Storyboarding: an empirical determination of best practices and effective guidelines[END_REF], who presented two formative studies designed to uncover the important elements of storyboards.

Activity-based analysis [START_REF] Uden | Designing mobile interfaces using activity theory[END_REF], in particular activity theory methods, incorporates the notions of intentionality, history, mediation, motivation, understanding, culture and community into design. In particular, it provides a framework in which the critical issue of context can be taken into account.

In hierarchical analysis [START_REF] Annett | Task analysis and training design[END_REF], another work analysis method, the instructional designer breaks down a task from top to bottom, thereby, showing a hierarchical relationship amongst the tasks, and then the instruction is sequenced bottom up. Task analysis often results in a hierarchical representation of what steps it takes to perform a task for which there is a goal and for which there is some lowest-level "action" that is performed [START_REF] Annett | Task analysis and training design[END_REF].

Usage of Different Work Analysis Methods

In the offshore engineering field, the project of deep-water production systems, including oil platforms, ships and all the subsea equipment that plays a part in the production process, is currently designed by means of complex computer modeling systems. The design of a new production unit is a lengthy and expensive process, which can last many years and consume hundreds of millions of dollars, depending on the complexity of the unit and the maturity of the technology required to make the project technically and economically feasible. Offshore engineering projects involve not only geographically distributed teams but also teams of specialists in different areas using different software tools, both commercial and internally developed. While the interoperability of those tools is still an issue, it is a mandatory requirement for any collaborative solution.

One of the objectives we had was to establish a sound requirements document stating clearly the desired project's objectives, requirements and specifications, as well as outlining scenarios for the solutions proposed and their evaluation procedures.

We used the resources available at Tecgraf-PUC Rio (the research arm of Brazil's largest oil industry company, Petrobrás) [START_REF] Santos | A collaborative VR visualization environment for offshore engineering projects[END_REF] to conduct user observations and informal interviews with the engineers involved in collaborative engineering design and review activities. The final result we obtain is very important, since it allows us to understand: (i) the application domain, (ii) the problem of designing and reviewing CAD models, and (iii) the needs and constraints of the system's stakeholders. Additionally, and perhaps most importantly, we elaborate on the relative advantages and disadvantages we faced when applying different work analysis methods: activity-based analysis [START_REF] Uden | Designing mobile interfaces using activity theory[END_REF], hierarchical analysis [START_REF] Annett | Task analysis and training design[END_REF] and storyboarding as a way to understand the value of possible solutions [START_REF] Khai | Storyboarding: an empirical determination of best practices and effective guidelines[END_REF].

Activity-based analysis

Activity 1: Designing and reviewing engineering models. Design review is the process of checking the correctness and consistency of an engineering project while making the necessary adjustments [START_REF] Santos | A collaborative VR visualization environment for offshore engineering projects[END_REF]. In the session, users can manipulate objects, highlight and create annotations, do measurements, check the proper ergonomic design. The ability to move, rotate and scale objects is important for various purposes, such as joining models, viewing hidden areas, planning the placement of new devices, and simulating a maintenance or intervention operation in a process plant. Moreover, integration with an engineering database from the CAD system is useful to create annotations emphasizing critical parts (Figure 1). Comments attached to objects can also be used as recommendations for project management. Figure 1 shows a measurement taken for planning the movement of a large tank in a production unit. Users create annotations to guide the maintenance procedure and animate the entire operation. Finally it is possible also to confirm if the space distribution of the engineering devices conforms to the ergonomic needs for operation and maintenance. Activity 2: Riser analysis workflow. An important step in deep-water oil exploitation is the elevation of the oil from depths over one thousand meters to the surface. Oil platforms use ascending pipes, called risers, which are tubular structures that convey oil and/or gas from the wellhead on the sea floor to the platform's separator system tanks [START_REF] Santos | A collaborative VR visualization environment for offshore engineering projects[END_REF]. To certificate the operation of the risers for their entire lifecycle (30 years or so), simulations of the stress applied to the riser system are conducted based on meteo-oceanographic data about wind, tide and water currents. Simulations are made under extreme environment conditions to test stress resistance. It is important to perform fatigue analysis studies to evaluate the most critical regions of the risers affected by cyclical stress in order to guarantee their integrity during their lifetime.

Conclusions after the activity analysis. The problem of providing engineering teams with effective tools for collaborative work is becoming increasingly important, not only because teams are increasingly working distributed throughout the world, but also because current tools still lack support for collaborative engineering design and review, either in co-located or distributed settings. The oil industry is especially well positioned as a potential demonstrator for research developments in this field, since it's one of the largest user bases of high-end hardware and software.

Collaborative Virtual Environments (CVEs) place the emphasis on providing a common virtual space of interaction to distributed teams, a space where they can meet as if they were face to face, while sharing and manipulating the relevant work artifacts, in real time. In the case of this application domain, the oil industry, there is a relevant issue that motivates this project: the working force is aging, and the industry is not attracting younger generations of workers. Therefore, the trend of conceiving "digital oil fields" capable of being controlled remotely is becoming strategic for oil companies.

The essential scenario for solving this problem is a 3D interactive environment that represents the oil platform and associated subsea equipment, in a multi-touch virtual control room approach. The oil industry application domain, as referred in the previous section, fits well into the design and evaluation of novel environments, because of several factors. First of all, the very nature of the work performed by the engineers themselves, which is often carried out in collaborative, geographically apart settings. Secondly, because they can provide real world data in the form of large-scale CAD models, thus acting as reliable demonstrators of the project's results. And finally, because there are -to our knowledge -very few research efforts specifically targeted at this application domain.

The final product the team performing this work analysis identified is essentially a large-scale virtual environment based on multi-touch and remote collaboration features for increased awareness and increased sense of presence among teams of oil industry engineers. The final prototype should consist of a set of computer clusters for multi-projection environments running the software designed, developed and evaluated throughout the project.

The main idea underlying this vision is that if we provide engineers with multitouch collaborative tables and walls, we can achieve a state-of-the-art environment with an interesting application to the oil industry: a system that finally allows these users to find, navigate and visualize their complex CAD models' data in a much more satisfying and effective way. This goal can be measured in two dimensions: the usability dimension, making use of well-know Human-Computer Interaction (HCI) evaluation methods and the collaborative dimension, using traditional measures for determining the levels of remote collaboration between geographically dispersed teams.

3.2

Hierarchical task analysis

In this work analysis, the hierarchical task breakdown was actually the first step the team took. The analysis was based on input from experts in the oil platform engineering field, several brainstorming sessions, semi-structured interviews and other meetings. The result (after many iterations) is shown in Figure 3. One of the positive aspects was the fact that the team was able to identify the most important tasks and the most intense tasks from a cognitive perspective. The downside was that the team remained without a clear picture about what should be designed and implemented, what was more important and what was less relevant. Other methods, as we will see in the next section, proved far more efficient in gaining a common vision.

Storyboard-based analysis

Since the team had difficulties in synthesizing the work of offshore engineering design and review teams, we decided to employ storyboards [START_REF] Khai | Storyboarding: an empirical determination of best practices and effective guidelines[END_REF] to facilitate the ideation process as well as to better explore the possible design concepts. Figure 4 illustrates a particular one (textual descriptions omitted for brevity).

Storyboarding was particularly beneficial to this project's HCI design. It allowed matching the possibilities offered by recent advances in mobile, multitouch technologies with the cognitive tasks at stake. After long discussions, the team chose to employ the power of tablets to create different navigation modes in a collaborative prototype where CAD models are simultaneously shared and visualized through Wi-Fi, while the user can also videoconference with other remotely located engineers. One of the navigation modes is depicted in Figure 4. The storyboard illustrates the use of sensors to control the camera's orientation. Translations are performed through multi-touch gestures. The user is free to work whenever he wishes to, which is a significant step further regarding the current system being used by the company (based on traditional desktop-based PCs). Therefore, we can conclude that storyboarding was an effective technique to identify novelty factors that could enhance the usability of the proposed product.

Final Solution

The system is built upon the concepts of intuitive visualization and cooperation. To achieve our objectives while using those concepts we built several test navigation modes. Two of them ended up being selected for further improvement. Despite an apparent similarity on interaction styles, the two modes are quite different in both the technical and user interaction components: The first version uses built-in inertial sensors to position the virtual camera in a first person view manner, just as if the user was holding a real video camera and filming around. To allow the user to move around, it uses an in-screen touch-based joystick. The second version uses the tablet's camera to track its position and orientation, relative to a tracker. It works as if the user was filming the object on top of a table allowing all the natural movements he would do.

The second version can't use the built-in inertial sensors exclusively as none of them gives translation (the position can be doubly integrated from acceleration but the errors and drift are too significant to be useful). GPS, another possible alternative, can only work outdoors and doesn't have enough accuracy for this problem. The solution is to use the camera to track an object and from that deduce the tablet's position and orientation. The camera tracking is a suitable solution as it provides a surprisingly accurate translation, rotation and, indirectly, zooming without the need of another artificial input (like the virtual joystick on the first-person view navigation mode). The first-person version can be shared between multiple tablets allowing one user to guide or show some feature on the model to the other users. Figure 5 illustrates this. The microphones and the front cameras are shared between multiple tablets, creating a videoconference that improves the cooperativeness of the system. A minor feature (freezing the camera) was also implemented. It allows users to freeze the current view and move around the tablet without fearing that the movements performed will change the camera's position and without stressing body positions. This feature can be used when showing certain features on the models or even when working on the models themselves on future work.

Comparison between the different methods

Hierarchical task analysis was the first method employed in this product's HCI design. Perhaps because of that it was the method that required more effort in order to produce a reasonable set of artifacts describing the engineers' work. Task analysis taken from a hierarchical perspective had its advantages. First, it allowed the entire team to understand the priorities in the design that should be taken into account. It also had the advantage of promoting discussion around a single diagram, which made it easier to reason about human work without losing the "big picture". However, this method was the least efficient of all. The team remained without a completely clear picture about what should be designed and implemented, what was more important and what was less relevant.

Secondly, the method for trying out a more efficient work analysis was activitybased analysis. We tried to write a complete, detailed description of the collaborative engineering activities that are performed by offshore engineering teams, working both in the oil platform as well as in the central company's offices.

The activity-based analysis effort was overall positive. By forcing a detailed description of the activities at stake, the team spent a lot of time and effort, but at least was able to reach a better work analysis. It allowed identifying a final, concrete product, essentially a large-scale virtual environment based on multi-touch and remote collaboration features for increased awareness and increased sense of presence among teams of oil industry engineers. The disadvantage we encountered was the fact that activity-based analysis did not allow the identification of novel ideas, and the brainstorming processes that usually lead to better UI designs was not well undertaken.

As mentioned before, storyboarding was the most useful technique as it allowed discovering novelty factors that differentiate the solution and improve the usability of the product, thereby supporting the human work at offshore engineering design and review sessions.

Conclusions

Supporting the needs of offshore engineering teams is an important industrial problem that should be addressed taking into account the rapid evolution in user interaction styles available. The potential for innovative solutions that is brought by tablet-based computing is enormous. In this paper, we described the industrial creation and evaluation experience of a new mobile system for collaboratively navigating and reviewing 3D engineering models, applied to the oil industry. We highlight that storyboards and scenarios were an effective way to elicit requirements together with oil industry experts, as opposed to high-level task analysis.

Our main contribution to the Human Work Interaction Design (HWID) field is the comparison of the effectiveness of different work analysis and design methods towards establishing a common vision regarding a new product for the oil and gas industry engineering models' review, in a collaborative manner. This is especially important for gaining new insight about the relative advantages between the different methods in a highly complex, real world work domain.

Fig. 1 .

 1 Fig. 1. Annotating and measuring activities involved during design and review.

Fig. 2 .

 2 Fig. 2. The second activity identified: riser analysis.

Fig. 3 .

 3 Fig. 3. Task case map for our work domain analysis.

Fig. 4 .

 4 Fig. 4. A particular part of the storyboards created, illustrating one of the navigation modes.

Fig. 5 .

 5 Fig. 5. Two engineers engaged in a co-located collaborative design and review session.

Acknowledgments

This work was supported by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), under project PTDC/EIA-EIA/116070/2009.


Tetsuro Chino
email: tetsuro.chino@toshiba.co.jp

Kentaro Torii

Naoshi Uchihira

Yuji Hirabayashi

Work and Speech Interactions among Staff at an Elderly Care Facility

Keywords: cooperative work, speech interaction, care

diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Japan's increasingly aging population is an important issue, one urgent aspect of which is caring for elderly persons with disabilities. Nowadays, care services are often provided by the devoted efforts of care staff at long-term care facilities. Care services are characterized by what we call "action-oriented intellectual services." Care staff makes many decisions regarding medical care which require specialized knowledge. They also assist elderly persons with disabilities in many activities of daily living. Care service staff members have many types of professions and specialties. Also, care services must operate at all hours, throughout the year, and successfully doing so requires a high degree of information sharing among staff, for example, keeping records of care performed. [START_REF] Lemonidou | Allocation of nursing time[END_REF] pointed out that medical staff spend much time on indirect care, including record keeping and information sharing. Conventional IT systems are fundamentally designed for desk work, however, and do not support the needs for hands-free and eyes-free operations suited to action-oriented intellectual services.

[2] and [START_REF] Torii | Service Space Communication by Voice Tweets in Nursing, in proc[END_REF] proposed a "voice tweet system" to overcome these problems. In that system, "voice tweets" spoken by a staff member are tagged with the staff member's location and motion, spoken keywords, and associations with background knowledge, and based on these tags, the tweets are automatically delivered to an appropriate staffer. This system provides semi-hands-free and eyes-free communication among medical and care staff. Such a system requires knowing what kind of speech communication supports cooperative work in medical and care domains. We therefore performed field studies at an elderly care home in Japan to analyze cooperative work and speech interaction among care staff.

We report on a series of field studies and the insights extracted from analysis of the collected data. The remainder of this paper is organized as follows. Section 2 describes previous works. Section 3 describes the field and the tasks examined. Section 4 describes the study methodology, and Section 5 presents an analysis of the collected data. Finally, Section 6 gives our conclusions and proposed areas for future study.

2

Related Works [START_REF] Lee | Loosely Formed Patient Care Teams: Communication Challenges and Technology Design[END_REF] analyzed dynamically formed teams in a hospital emergency room, and identified three key factors in the design of team communication technology: (1) maintaining awareness within the team, (2) making informative interruptions, and (3) supporting role-based calling. However, this study was limited to a few directly connected rooms on a single floor. Further investigation of more distributed locations, such as in larger hospitals or care facilities with multiple stories, may therefore be necessary.

[5], [START_REF] Tang | A Mobile Voice Communication System in Medical Setting: Love it or Hate It?[END_REF], and [START_REF] Tang | InfoFlow Framework for Evaluating Information Flow and New Health Care Technologies[END_REF] reported on the use of a commercial voice communication system named "Vocera" in actual hospitals. They pointed out (1) that selecting communication artifacts is necessary, (2) that person-locating features can reduce human movement, and (3) that remote voice communication has positive effects on the prioritization and scheduling of tasks. They also described risks and problems associated with voice communication devices. [START_REF] Nambu | Risk Sharing Communication in Medical Settings: Analysis of Nurses' Conversation[END_REF] reported on an analysis of nurse-patient conversations in a Japanese hospital. They found that most utterances effective for risk sharing are spoken by nurses who are highly skilled at risk recognition and communication. In the "voice tweet system" described above, sharing this kind of informative utterances as "voice tweets" can have positive educational effects for the whole team.

Study Location

Care Facility

We performed field studies at a Japanese elderly care home over 8 days between September 2011 and February 2012. Figure 1 shows an overview of the study location. The building has four stories, with 40 private resident rooms, common living and dining rooms, 1 care station, 1 bath area, and other rooms and spaces. Corridors, elevators, and stairways connect the rooms.

Residents with Disabilities

About 30 residents were living in this building during our field study. For convenience, we classify residents into the following groups:

─ Residents with slight disability are independently mobile with the aid of a device such as a cane or walker. ─ Residents with moderate disability require a wheelchair and staff assistance to move around. ─ Residents with severe disability are bedridden, and can only be moved by multiple staff using specially designed devices such as a stretcher.

Target Tasks

We observed and collected data on 3 tasks.

─ The bathing assistance task involves bathing residents on the first floor of the building, and is performed by a special day shift team. ─ The nighttime staff performs night shift tasks, which involve monitoring resident sleep, performing evening and morning care, checking vital signs on a schedule, responding to nurse and sensor calls, and attending to other needs as they arise. ─ At scheduled shift changes, leaders of the incoming and outgoing shift personnel perform the handover task.

Methodology

Speech Data Collection

We collected over 142 h of "subject sounds" via microphones attached to staff members' clothing. We furthermore collected another 142 h of "intercom sounds" via intercom lines, and more than 98 h of "environmental sounds" via microphones set up at various locations. We used IC recorders to record environmental sounds in 44.1 KHz 16-bit linear PCM stereo, and recorded subject sounds and intercom sounds in 44.1 KHz 16-bit linear PCM mono. Figure 2 shows the equipment used.

Extraction and Transcription of Utterances

About half of the audio data was selected and automatically split into sound fragments. We set the threshold parameter for length of silence between fragments to 500 ms, and human transcribers flagged fragments that were human utterances. Finally, the human utterances were transcribed with time stamps and speaker identifiers. This resulted in more than 42,000 transcribed utterances.

Other Collected Data

We collected more than 64,000 staff location samples using a Bluetooth-based indoor location tracking system that we developed. Human observers traced staff movements onto paper, and investigated their actions. The observers recorded the time, place, participants, media, and a brief memo on the content of observed conversations involving staffers. Observers also videotaped subjects to get precise movement data with timestamps.

Work and Speech Interaction Analysis

Work Analysis of the Bathing Assistance Task

We chose the bathing assistance task for analyzing work and speech interactions, because this was the most complicated collaborative activity among our target tasks. The following is an overview of the bathing assistance task.

1. An "inner support" staff in the dressing room determines the next resident to be bathed according to a schedule. Staff performs the bathing task according to the disability level of the resident as follows:

! For residents with slight disability, the inner-support staff calls the resident's room via intercom and asks the resident to come to the bath area.

! For residents with moderate disability, one or two "outer support" staff members go to the resident's room, check vital signs, and then ask the resident to come to the bath area by wheelchair with their assistance. If the resident agrees, then the staff members assist the resident from the bed to the wheel chair, and bring the resident to the bath area on the first floor.

! For residents with severe disability, two or three outer support staff members go to the resident's room with a stretcher designed for bathing bedridden residents, and check vital signs. If the resident agrees and conditions are met, then the staff members assist the resident from the bed to the stretcher, and bring the resident to the bath area on the first floor.

2. After arriving at the bath area, the resident undresses with inner or outer staff assistance, according to need and availability.

3. Assisting staff also check the resident's body and apply ointments or medicines if necessary.

4. The inner support staff helps the resident bathe in a specially designed barrierfree bathroom or a mechanized bathroom, as needed.

5. After bathing, mainly inner support staff helps the resident get dressed.

6. Outer support staff assists the resident from the first-floor dressing room to the second-floor common living and dining rooms.

7. In the living and dining room, other staff members provide support by drying the resident's hair, bringing food, or helping the resident rest in the living room.

8. Finally, staff helps the resident return to his or her room.

Since multiple residents bathe at the same time, some procedures are performed simultaneously. Many factors can force changes in procedure. For example, residents may not be in their rooms, or their mental or physical condition may preclude bathing.

It is worth noting that the tasks of getting dressed and undressed provide residents with valuable opportunities to train and maintain their physical and intellectual abilities, so the staffers provide minimal support and focus on safety. The time needed for getting dressed and undressed differs from resident to resident and can reach 30 min in the longest case, but efficiency is not the primary consideration in providing highquality services at elderly care facilities. Flexible collaboration is therefore necessary to ensure these tasks are performed smoothly.

Utterances Spoken during Bathing Assistance

We choose one bathing assistance task involving all female residents for our speech interaction analysis. The task was performed over one morning, and represents about 10% of the speech data described in section 4.1.

According to records provided by the elderly care home, 6 staff members had bathing assistance duties that morning. Among them, we focused on two outer support staff members, who we refer to as A and B. We selected them because they moved throughout the study location and interacted with many residents in many different situations at multiple times and places. Table 1 shows a summary of utterances by A and B and other staff and residents. From the data, 10,095 utterances were extracted. The outer support staff spoke relatively more than others, perhaps because of numerous stock phrases used with residents. The lengths of each utterance were similar for all speakers, perhaps due in part to the influence of our automatic splitting process.

Table 2 shows the spatial conditions of each conversation participant. Almost all utterances (99.4%) were face-to-face communications. The proportion of remote communication was only 1.0% for staffer A, and 0% for staffer B. Inner support staff calls slightly disabled residents in this task, so there is little opportunity for remote communication by outer-support staff. Introducing the voice tweet system at this location would have a significant effect, because currently outer support staff does not have access to remote communication media, which may force unnecessary movement.

Frequency and Intended Recipients of Utterances

We analyzed utterance frequency and the intended recipients of each utterance to know how often and to whom the care staff talks during this task. Figure 3 shows a time plot of utterances spoken by outer support staffer A (the upper half of the graph) and staffer B (the lower half of the graph) during the 2.5 h of the bathing assistance task. Dots at the upper, middle, and lower positions denote utterances spoken to residents, other staff, and oneself (monologues), respectively. Both A and B are almost always speaking during the task, and the intended recipients quickly changes. In one instance, the collected data show an outer support staff's simultaneous conversation with a resident and other staff in the dressing room. Figure 4 shows the distribution of intended recipients for utterances by staffers A and B. For both, about 75% of utterances are spoken to residents, about 17% to other staff, and the remaining utterances are monologues. The frequency of utterances to residents may be evidence of the importance of direct face-to-face verbal communication between staff and long-term residents.

Names in Utterances

Accurate long-term care records are important for maintaining and improving the quality of care services. Meaningful long-term care records require several key types of information, such as who performed the care task, what kind of care was performed, for whom the care was performed, when the care was performed, and where it was performed. Mobile device-based support systems like the voice tweet system can automatically record data on who (staff), when (time), and where (location). But data about for whom care is performed (resident) may be difficult to acquire, because devices such as physical tags attached to residents may be considered dehumanizing. We therefore analyzed the appearance of personal names in the utterances spoken by outer support staffers A and B. Personal names appeared 703 times, 378 times for staffer A and 325 times for staffer B. Table 3 summarizes the distribution of personal names in each utterance. About 16.9% of utterances have at least one name that could be a clue as to who is providing care or to whom care is being provided. Such information could be used by an advanced voice tweet system to semi-automate long-term care record maintenance in future care staff support systems.

Clustering Sequences of Utterances into Passages

To get more information about who provides care and for whom, from utterances for a semi-automatic long-term care record system, we attempted to cluster sequences of utterances into what we call "passages." A threshold parameter of the time gap between sequential utterances allows merging series of utterances spoken by each staffer into passages. Figure 5 shows distributions of passages with specified numbers of names, for 13 different settings (from 0.1 to 60 s) of the threshold for the time gap between passages.

Given an appropriate parameter value (3 s in this case, at the vertical dotted arrow in Figure 5.), the percentage of passages with only one person-name (shown as the solid line in the Figure 5.) can be dramatically improved from 16.1% (the unmerged utterances case in Table 3) to 32.6%, and the average time length of the passage will be 16.7 s. This rate is sufficiently high and this length is sufficiently short to be utilized for precise semi-automatic long-term care records in future care staff support systems.

Possible Extensions and Applications

Background knowledge on each person who works or lives in the facility (e.g. name, role, gender, shift/work schedule, profession, behavioral preference or tendency, etc.) can be utilized to disambiguate the referent of each referring expression (e.g., personal names or pronouns) appearing in each utterance or passage. Based on this information, together with automatically acquirable information (e.g., who, where, and when) from a mobile device-based platform, the future stuff support system can generate and suggest estimated candidates or outlines of long-term care records to the responsible staff member, and ask for confirmation. This mechanism may contribute to lower costs and reduced load of care staff for the indirect care, as well as to improved quality of long-term care records.

Conclusions and Future Work

We reported on a series of field studies at an elderly care facility. For eight days we observed three tasks: bathing assistance, night shift operations, and shift handover. We collected approximately 400 h of recorded speech, 42,000 transcriptions, data from a location tracking system, and handwritten notes by human observers. We also analyzed speech interaction in a bathing assistance task. We found that (1) staff members are almost always speaking during the task, (2) remote communication is rare, (3) about 75% of staff utterances are spoken to residents, (4) utterance targets are frequently switched, and (5) about 17% of utterances contain at least one personal name. We also attempted clustering utterances into passages. The rate of passages with only one personal name can be raised to 33%. These results could be utilized in semi-automatic long-term care record taking.

Future targets for study include [START_REF] Lemonidou | Allocation of nursing time[END_REF] an integrated analysis of not only the collected speech data, but also the location, action, and observed descriptions of staff actions, and (2) comparison with data from other hospitals to clarify differences and common aspects, helping to create general models applicable to the broader healthcare domain.

Fig. 1 .

 1 Fig. 1. Overview of the elderly care facility

Fig. 2 .

 2 Fig. 2. Sound data collection equipment

  (a) Subject sounds (b) Intercom sounds (c) Environmental sounds

Fig. 3 .

 3 Fig. 3. Time plot and intended recipients of utterances

Fig. 4 .

 4 Fig. 4. Distribution of intended recipients of utterances of staffers A and B (outer support staff)

Fig. 5 .

 5 Fig. 5. Relation between the average length of passages (horizontal axis), and the distribution of passages with a specified number of personal names (vertical axis), for multiple thresholds

Table 1 .

 1 Summary of the extracted utterances

	Speaker	Number	Percentage	Min.	Ave.	Max.	St dev.
		Extracted utterances		Length of utterances (s)
	A	2,293	22.7%	0.074	1.683	5.697	1.214
	B	1,692	16.8%	0.121	1.724	5.059	1.125
	Others	6,110	60.5%	0.055	1.635	18.053	1.441
	Total	10,095	100.0%	-	-	-	-

Table 1 .

 1 Number

and length of extracted utterances of staffers A and B of the outer support staff and otehers (other persons, including other staff and residents).

Table 2 .

 2 Summary of special conditions of each conversation participant

	Speaker	Number	Percentage	Number	Percentage	Number	Percentage
		Face-to-face				Total
	A	2,269	99.0%	24	1.0%	2,293	100.0%
	B	1,692	100.0%	0	0.0%	1,692	100.0%
	Total	3,961	99.4%	24	0.6%	3,985	100.0%

Table 2 .

 2 Spatial conditions of conversational participants in utterances of staffers A and B (outer support staff).

	Remote

Table 3 .

 3 Distribution of personal names in each utterance

	Table 3.
	Distribution of number of presonal names in utterances of A (outer support staff A) and B (outer support staff B).
	Speaker	Number	Percentage	Number	Percentage	Number	Percentage	Number	Percentage
		No (0)	Only 1	2 or more		Total
	A	1,938	84.5%	335	14.6%	20	0.9%	2,293	100.0%
	B	1,377	81.4%	305	18.0%	10	0.6%	1,692	100.0%
	Total	3,315	83.2%	640	16.1%	30	0.8%	3,985	100.0%

Acknowledgements

This project was joint research between the Toshiba Corporation, the Shimizu Corporation, and the Japan Advanced Institute of Science and Technology, and was supported by the Service Science, Solutions and Foundation Integrated Research Project Program of the Japan Science and Technology Agency under the Ministry of Education, Culture, Sports, Science and Technology, Japan.


Anant Bhaskar Garg
email: anantgg@yahoo.com

K K Govil

Empirical Evaluation of Complex System Interfaces for Power Plant Control Room using Human Work Interaction Design Framework

Keywords: Cognitive Work Analysis, Embodied cognition, Human Work Interaction Design, Power Plant Control Room, Ecological Interface Design

This paper first discusses two cognitive science paradigms and then present third approach related to interaction with the world as known as embodied cognition. The focus is to analyze work settings with the help of cognitive work analysis and human work interaction design approaches. The case of power plant control room is discussed and analyzed in the context of human system interactions, work and task analysis. This approach helps in reducing cognitive workload on operator which can result in reduction in errors in managing the control room of the power plant.

Introduction

In the year 1956, two different conferences and seminars gave birth to two new research areas known as Artificial Intelligence and Cognitive Science. They stress that brain functions like a computer that specifies symbols can be processed and manipulated and known as cognitivism. This view that thought is manipulation of abstract symbols, algorithmic computation in the same way as computer does. All meaning arose via correspondences between symbols (words, mental representations) and things in the external world. The mind was seen as a mirror of nature, and human thought as abstract and disembodied. Therefore, in principle, a computer could replace a brain, and in particular computers can do anything that brains can do. But this give rise to symbol grounding problem and unable to explain how we get meaning of objects.

The two main approaches (symbolic and connectionist) study cognition of an individual being but as we live in the world, environment and interaction with others play an important role. This thinking leads to theories of embodied and situated cognition, where cognition is seen as taking place not only in the brain, but also in interaction with the world supported by the body [START_REF] Clark | Being There: Putting Brain, Body and World Together Again[END_REF], [START_REF] Varela | The Embodied Mind[END_REF]. Moreover, this may suggest that neural processes not only generate sensory experiences, but also help in exploratory interaction with the world that guides action or behavior. This paradigm finds applications in various research projects notably robotics, autonomous agents, and interactive interfaces [START_REF] Brooks | Intelligence Without Representation[END_REF], [START_REF] Dautenhahn | Embodiment and Interaction in Socially Intelligent Life-Like Agents[END_REF]. This requires awareness of the relationship with the world especially work, physical movement, action, and other affective behavior reactions. Everyday human activities include all of above states that we can understand through work analysis, and embodied cognition -both are prominent paradigm in cognitive science. Embodied cognition, CWA, and Cognitive System Engineering (CSE) [START_REF] Fidel | From information behavior research to the design of information systems: the Cognitive Work Analysis framework[END_REF] provide a new framework for Human Work Interaction Design (HWID) research. A more detailed HWID framework is given by [START_REF] Clemmensen | A Human Work Interaction Design (HWID) Case Study in E-Government and Public Information Systems[END_REF] (as shown in figure 1). To represent organizational settings that describe work processes we need to understand role of information and functional demands of the dynamic work environment. Mapping of information to features must show underline semantic structure of the work domain. This require nesting of information which implies meaning inherit in the work processes. For this, we require knowledge and understanding of objects.

Objects are those with which we deal in everyday world. How we acquire the meaning and refer to the symbol or sign. Informatics and cognitive science deal with problems representation, semantics of computation and their application to create technology.

HWID Framework for Control Room

All this depends on perception of objects and conception of object that has been embedded in commonsense discourse. Mental phenomena, spoken & written language, and systems of signs & codes are intentional. According to Brian Smith, how we represent objects of the world and real-world situation in the computer is the vital point in understanding human functioning. So he says that experience we have with constructing computational (intentional) systems may open a window onto something to which we would not have any access [START_REF] Smith | On the Origin of Objects[END_REF]. Therefore, present research will suggest ways to design and develop systems, which are more suitable for varied situations and users.

Above research work is related to field of Man -Machine Interface (MMI) / Human -Computer Interaction (HCI), HWID in particular with the installation and operation of machines in the power plant. Here design of interfaces and interactions with control panel effect working of human operators. Through better-designed interfaces we can increase effectiveness, safety, and decision-making. Thus understanding the work situations and processes are important for improving design of control room and interfaces. It is important that how we could design machines so that they help human in every aspect and they will be their most efficient and effective assistants.

Work Study

To study human interaction with environment for studying power plant control room systems we require the understanding of what we mean by 'information' and flow of information within particular situation.

Information is experienced in perception, retained by memory, and manipulated via symbols. Thus, 'information' is central for the exploration of technical, social, and aesthetic bases of human interaction with the world. This provides the framework for our understanding of the underlying patterns of 'information' transmission, storage, and retrieval. Conceptual knowledge of concepts such as ontologies, usability issues, and knowledge management help us to create better systems. Conceptual structures based model assist learner in retrieving, evaluating, and comprehending information through the use of semantic associations. This helps in knowing, how we discover and learn regularities in the environment from the consequences of our actions. How information, communication and technologies (ICT) can best be used for effective and efficient transmission of information to all irrespective of sociocultural background and abilities? The above analysis is based on some goals and performance measures and their understanding. How interface design can be improved through approaches such as ecological interface design [START_REF] Burns | Ecological Interface Design[END_REF]? What are testing methods & situations that show the effectiveness of the interfaces?

Further, this problem is not simply programming but it involves cognition, our spatio-temporal movements, and our awareness of the environment in which we work. Therefore this problem includes representation of the world, processes involved, and comes under multidisciplinary Cognitive Work Analysis (CWA) and Human Work Interaction Design (HWID) frameworks [START_REF] Clemmensen | A Human Work Interaction Design (HWID) Case Study in E-Government and Public Information Systems[END_REF]. The problem requires understanding of the relation between human and machines that we can study in a complex socio-technical system such as control room of power, oil, and gas industries or particularly energy sector. Here our aim is to analyze the whole system not just human operators or machines in isolation but complete work settings so that individuals can act more effectively and preserve system safety and efficiency. We are analyzing work domain of the hydropower plant control room as shown in figure 2

Human System Interfaces based on Ecological Interface Design

To study work environment and particular situation we are using CWA and HWID frameworks and focusing on guidelines related to Human System Interfaces (HSI). Embodied view provides a sharp contrast from the standard information-processing viewpoint, in which cognition is seen as a problem of recovering details of the pre-given outer world [START_REF] Varela | The Embodied Mind[END_REF]. In this light, the mind is no longer seen as passively reflecting the outside world, but rather as an active constructor of its own reality. The mind thereby becomes a distributed entity, an emergent characteristic of the whole sensory-central-motor neural system, existing in the elaborate network of interconnections that extend throughout the body. The philosophy of embodiment also stresses temporal, physical, and socio-cultural-technical situatedness. And, quite significantly, the embodied view in cognitive science allows for direct social and cultural interaction, which is crucial for understanding work situations.

In any power plant control room there are two objectives: first is to present to the operator components of plant and second is to minimize in-formation or Cognitive overload on operator. To exhibit this, traditional display is shown in figure 2 and ecological display in figure 3 based on ecological interface design (EID) [START_REF] Memisevic | Work domain analysis and ecological interface design for hydropower system monitoring and control[END_REF]. EID presents intangible abstract concepts visible and meaningful to operators [START_REF] Burns | Ecological Interface Design[END_REF]. For analyzing work situations in control room following questions are important:

• Objects representation and their meaning • What makes us to acquire knowledge about the objects in the world, their specific relations, and properties • How we extract information, its meaning • Ontology of objects Fig. 3. A traditional display for the steam system [START_REF] Burns | Evaluation of Ecological Interface Design for Nuclear Process Control: Situation Awareness Effects[END_REF] Fig. 4. An ecological display for the turbine system [START_REF] Burns | Evaluation of Ecological Interface Design for Nuclear Process Control: Situation Awareness Effects[END_REF] Ecological display clearly represents reactor pressure, enthalpy, temperature, pressure, and mass flow balance for better decision making. Through this work in progress, we would like to improve information displays, layout of system information, processes, and alarm system of a complex system such as power plant. Besides this study intend to find link between electricity distribution and control room status. These are larger unit of analysis and involve social-technical and cultural issues.

Research Analysis and Discussion

Hutchins tried to show that the cognitive science approach could be, with little modification, applied to a unit of analysis, which is larger than an individual. The unit of analysis here is a larger cognitive sociotechnical system, such as an automated industrial setting (electricity distribution system [START_REF] Hay | Process of Simulating Novel Control Room Scenarios for Future Active Networks[END_REF], [START_REF] Memisevic | Investigating Human-System Interaction with an Integrated Hydropower and Market System Simulator[END_REF] power plant control room) [START_REF] Burns | Evaluation of Ecological Interface Design for Nuclear Process Control: Situation Awareness Effects[END_REF], [START_REF] Braseth | Lessons Learned From Halden Project Research On Human System Interfaces[END_REF] and airline cockpit [START_REF] Hutchins | Cognition in the Wild[END_REF]. This approach is related to cognitive work analysis of studying processes in work environment. In both approaches, the question remains the same: what are the structures and processing of representations, which are internal to the system? To explain information-processing properties of individuals or larger systems, it is difficult to infer what is inside their mind [15] [16]. But for larger sociotechnical systems, it is possible to directly observe the different representations that are inside the system, despite being outside the brain of individual. The above objectives can be attained through analyzing the various contributions of the environment in which work activities take place. Such as representational media (e.g. instruments, displays, manuals, navigation charts), the interactions of individuals with each other and their interactive use of artifacts (computer systems). Simultaneously, how information necessary to cooperate is propagated through the system by representational states and across machines is analyzed.

CWA is a work-centered conceptual framework developed by Rasmussen, Pejtersen, and Goodstein. Through CWA designing of constraints based interfaces is possible and such interfaces support operators in dealing with uncertain events and proved to be useful not just usable interfaces [START_REF] Vicente | Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work[END_REF].Through CWA & HWID we analyzes the work people do, the tasks they perform, the decisions they make, their information behavior, and the context in which they perform their work [START_REF] Sanderson | Cognitive Work Analysis across the system life cycle: Achievement, challeneges, and prospects in aviation[END_REF]. CWA is a holistic approach and examines several dimensions: the environmental, organizational, social, activity, and individual. Therefore, we require a multi-disciplinary approach to apply concepts of CWA to study complex system [START_REF] Fidel | From information behavior research to the design of information systems: the Cognitive Work Analysis framework[END_REF].

In present research, we apply HWID framework for power plant control room panel interfaces to design the information system interaction and activities. As we live in the world, environment and interaction with others plays an important role. Thus both the paradigms find applications in various research projects and areas notably are studies of work domain analysis, robotics, autonomous agents, and interactive interfaces. These research directions will also be central for future developments of man-machine interaction, studying and enhancing the limits of information processing capacities of human and machine. These understandings result in better cooperation between human and computer that will enhance our capacities as supported by technology. Present research work explores the complex work environment to study cognition, cognitive tasks, and mental representations that will result in computational model of control room interactions and work setting for information handling based on CWA [START_REF] Jenkins | Cognitive Work Analysis: Coping with Complexity[END_REF] & HWID (as in fig. 5). Further application is using Abstraction Hierarchy method for the redesigning of the Control System Man-Machine Interface (MMI) of the Medium Tension Distribution Network of the Public Power Corporation of Greece [START_REF] Drivalou | Tracing Interface Design solutions for an Electricity Distribution Network Control System Using the Abstraction Hierarchy[END_REF].

Conclusion

This research provides understanding of cognition underlying human interactions with machines to improve design of both machines and work situations. We need interfaces and work situation, which reduces error and cognitive overload on human. Thus use of cognitive system engineering in the energy sector will helps us to analyze, model, design and evaluate effective human integration in complex socio-technical systems. The role of human operators in power plant control rooms is not only to control plant but also to meet market conditions and goals. With the advent of new Information Technology infrastructure we need to assess information requirements to manage control room that suits both human, machines, and economies.

Fig. 1 .

 1 Fig. 1. A Framework of HWID[START_REF] Clemmensen | A Human Work Interaction Design (HWID) Case Study in E-Government and Public Information Systems[END_REF]

Fig. 2 .

 2 Fig. 2. Control Room Panel of a Hydropower Plant

Fig. 5 .

 5 Fig. 5. Framework for Work -Activity Measure Analysis

Acknowledgements. First author would like to thank Prof. Torkil for helpful in writing and completing the paper. Also thankful to Dr. S. J. Chopra, Chancellor, and Dr. Parag Diwan, Vice-Chancellor, UPES for their encouragement and support.


Ricardo Proença
email: ricproenca@gmail.com

Arminda Guerra

Natural Interactions: An application for Gestural Hands Recognition

Keywords: Human-Computer Interaction, gesture recognition, computer vision, disabled person, assistive technology 4

This paper presents a system for the development of new humanmachine interfaces focused on static gestures recognition of human hands. The proposal is to aid access to certain objects to the occupant of an intelligent wheelchair in order to facilitate their daily life. The proposed methodology relies on the use of simple computational processes and low-cost hardware. Its development involves a comprehensive approach to the problems of computer vision, based on the steps of the video image capture, image segmentation, feature extraction, pattern recognition and classification. The importance of this work relates to the need to build new models of interaction that allow, in a natural and intuitive way, to simplify the daily life of a disable person.

Introduction

In face-to-face communication verbal and non-verbal modalities are used to complement each other. When we intend to transpose natural communication into communication with the machine, several difficulties occur, for example, those that concern user experience and, in particular, those that do not permit social inclusion. We know that some people do not have the ability to handle some devices, but they possess enough ability to interact with a system of recognition of gestures [START_REF] Pausch | Giving CANDY to Children: User-Tailored Gesture Input Driving an Articulator-Based Speech Synthesizer[END_REF]. It becomes evident the necessity to build models of Human-computer interaction through gestures that allow, in a natural and intuitive way, to these people, to easily interact with the machines.

The image acquisition and processing technologies evolution permit to build a system that enable computers to understand the sign language. Different types of algorithms for gesture recognition are being studied, but the challenge lies in the fact that this interaction becomes independent of devices that assist the identification of the human hand, such as gloves or sensors for motion capture.

The proposed work is the development of a system for static gesture recognition with one hand only, located in front of a webcam, in a simple and uniform background scenario, without the support of assistive materials. This interface could be applied to an intelligent wheelchair in order to facilitate disabled peoples' quality of life.

Related Work

Several gestural proposals for various areas, such as entertainment, medicine, marketing, smart home control, elderly and disable care [START_REF] Lande | Multimodal System using Human Computer Interface[END_REF], [START_REF] Chhabria | Multimodal Interface for Disabled Persons[END_REF] as well as intelligent wheelchairs [START_REF] Zhang | A novel intelligent wheelchair control system based on hand gesture recognition[END_REF], [START_REF] Kobayashi | Robotic wheelchair based on observations of people using integrated sensors[END_REF], [START_REF] Simpson | The smart Wheelchair component system[END_REF], [START_REF] Jia | Head Gesture Recognition for Hands-free Control of an Intelligent Wheelchair[END_REF], [START_REF] Kumar | Hand Gesture Recognition Wheelchair Control by Digital Image Processing Technique[END_REF] were found in literature review. However, the majority of studies are exclusively related to the chair's movement and we did not find the complete mobility concerns. We also noticed the existence of wheelchair-mounted robotic arm applications [START_REF] Hillman | The Weston Wheelchair Mounted Assistive Robot -The Design Story[END_REF], [START_REF] Tsui | Development and Evaluation of a Flexible Interface for a Wheelchair Mounted Robotic Arm[END_REF], [START_REF] Palankar | Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments[END_REF], [START_REF] Touati | Smart Powered Wheelchair Platform Design and control for People with Severe Disabilities[END_REF] that uses different interaction devices (such as joystick), but none so intuitive that could be considered natural (to humans). In fact, this relation is not fully effective to personal conflict resolution without considering mobility. Some people, for example, do not hold the necessary dexterity to handle these devices, but they have enough ability to interact with a system for gesture recognition [START_REF] Pausch | Giving CANDY to Children: User-Tailored Gesture Input Driving an Articulator-Based Speech Synthesizer[END_REF] that certainly minimizes many of the difficulties and inhibitions.

3

The Context: Assistive Technology

We are daily faced with several examples of people who suffer road accidents. The consequences fully affect the mobility of the individual either physically, psychologically and socially. This situation promotes numerous side effects, some of which demand alternative means of mobility such as wheelchairs.

The increasing number of people with mobility disabilities requires assistive technologies to provide their quality of life improvement. However, there are many obstacles to use technology solutions because they require expensive resources or further studies in order to improve every citizen access. It is our understanding, according the research we did, that image acquisition and processing technologies' evolution, allows building a computational system that makes the machine to recognize human gestures. However, the construction of this system requires the use of various programming algorithms quite complexes. We list general procedures that entail: (1) image capture, (2) human hand segmentation, (3) feature extraction, (4) classification of gestures.

We considered the gestural algorithms development, based on low cost webcams, a great stimulus in Human-Machine Interaction.

Given the results we obtained in the course of this investigation, we propose a system to help the occupant of a wheelchair to interact with the world, especially, to have access to certain objects in order to facilitate their daily life.

In terms of hardware, the prototype consists of an intelligent wheelchair, a laptop with a built-in webcam and a robotic arm. As to software, this system consists in static gesture recognition, performed in real time, through a hand located in front of a webcam.

Fig. 2. Proposed system

Our concept of mobility will be based on the transposition of each executed gesture to a robotic arm action. This transposition is set from 6 gestures that we present in fig. 3 With this method, we intend to show that it is possible to construct a human-machine interaction system, facilitating the life of the person with inhibitions, with an acceptable level of environmental restrictions.

Again, we can prove that the fact that a person has to use a glove, or any other device that aids the identification of the human hand, is inhibiting the free, systematic and autonomous individual action.

The scope of this work is limited to the recognition of gestures; we want to identify the static gesture shown by the occupant of the wheelchair, between a set of predefined gestures system. The prototype that we outlined has not been built so far.

Finally, we launched the challenge to awake attentions and possible interests for the implementation of this type of equipment that will allow, in our view, more disinhibiting action of the individual, because it determines the elements that are not always available to anyone of us, even in conditions of normal mobility.

Natural Interactions

Natural interaction is considered through the way people naturally communicate through gestures, expressions, movements, and see the world by looking around and manipulating physical artifacts; the key assumption is that people should be allowed to interact with technology as they are used to interact with the real world in everyday life.

Our focus is on gestural communication. The gestures can naturally increase the interaction between the user and the computer, replacing devices such as the mouse, keyboard, joystick or buttons in machines control. However, a great incitement for gestural interfaces development comes from the growth of applications for virtual environments [START_REF] Krueger | Artificial Reality II[END_REF], such as surgery simulation. In the majority of these applications, the gestures are seen as triggers to virtual objects control, simulating the 2D, 3D or abstractions of real objects, such as robotic arms.

5.1

Gestures

There are many definitions and debates about the meaning of gesture. Pavlovic [START_REF] Pavlovic | Visual Interpretation of Hand Gestures for Human Computer Interaction: A Review[END_REF] presents a definition of hand gesture that combines the development of posture and position in time, with the intention of communication or manipulation.

In Human-computer interaction field, gestures imply the use of the hands to instruct a machine. Its meaning depends on the system that integrates and evaluates them. Point or rotate actions are representative actions of this type of gestures.

We can differentiate the gestures in two types: dynamic and static. The dynamic gestures, necessarily, involve time evolution [START_REF] Birk | Real-Time Recognition of Hand Alphabet Gestures Using Principal Component Analysis[END_REF] and require movement to transmit the intended message. The gesture to say "goodbye" is an example of a dynamic gesture performed with an open hand and is only meaningful if done with movement.

Furthermore, static gestures do not include movement to transmit the message. The same static open hand, with no motion made, forward the information to "stop".

In Computer Vision, we can state that static gesture is a configuration of the hand pose represented as a single image. A dynamic gesture implies movement, represented by a sequence of images.

However, in this work, we will limit ourselves to the static symbolic gestures performed by one hand.

Static Gesture Recognition

Static gesture is represented by an image; the recognition is based on feature extraction discriminants of the image. We can, for instance, to extract features from the directions of the fingers or the contours of the hand. The position of the palm of the hand and of the fingers has been widely used for gesture recognition [START_REF] Maclean | Fast Hand Gesture Recognition for Real-Time Teleconferencing Applications[END_REF], [START_REF] Kim | Interaction with Hand Gesture for a Back-Projection Wall[END_REF]. We can also treat other information about the hands as the angles [START_REF] Sato | Real-Time Input of 3D Pose and Gestures of a User's Hand and Its Applications for HCI[END_REF], roughness [START_REF] Jang | Two-staged hand-posture recognition method for softremocon system[END_REF] or sampling points on the boundary region of the hand [START_REF] Hamada | Hand Shape Estimation Using Sequence of Multi-Ocular Images Based on Transition Network[END_REF], [START_REF] Hamada | Hand shape estimation using image transition network[END_REF].

Another category of resources abundantly used implies the notion of moment. It is a weighted average of the pixels intensities of an image, usually chosen to have some interpretation. We can find statistical moments [START_REF] Lee | Recognition of hand gesture to human-computer interaction[END_REF], Zernike moments [START_REF] Sribooruang | Hand posture classification using wavelet moment invariant[END_REF] and Hu moments [START_REF] Hu | Visual Pattern Recognition by Moment Invariants[END_REF], which are more used.

Fourier analysis can also be applied for representing the limits of the hand [START_REF] Licsár | Dynamic training of hand gesture recognition system[END_REF]. The feature extraction is a complex computational problem because it requires perfect background characteristics and high computational resources. Disorganized background images with changes in lighting conditions become very difficult to handle.

Allied to this, the used algorithms require loading the whole image into memory, which complicates the computer resources management.

After extracting the features, then we can classify the gestures. The more simplistic way of static hand recognition is to count the number of fingers [START_REF] Kim | Interaction with Hand Gesture for a Back-Projection Wall[END_REF]. Nevertheless, recognition can be seen as a matching process, applying cluster algorithms [START_REF] Licsár | Dynamic training of hand gesture recognition system[END_REF], [START_REF] Zhou | Static hand gesture recognition based on local orientation histogram feature distribution model[END_REF]. In this case, recognition consists of finding the best match between images that represent static gestures. We may also do the same correspondence with Chamfer distance algorithm [START_REF] Athitsos | An appearance-based framework for 3d hand shape classification and camera viewpoint estimation[END_REF]. In addition, the Neural Networks are widely used in the hand postures detection and classification [START_REF] Marcel | Hand gesture recognition using inpu/output hidden markov models[END_REF], using two or three layers of neurons. More recently, techniques of face detection have been applied to this problem. The Viola and Jones system that leverages the features Haar [START_REF] Jones | Face recognition using boosted local features[END_REF] is a consensus for subject researchers.

Interface Design: Process Description

Based on [START_REF] Gonzalez | Digital Image Processing[END_REF], the proposed method for symbolic gesture recognition follows a logical and complementary tasks' sequence in terms of digital image processing. The fundamental steps for the proposed work are presented on fig. 4.

Image Acquisition

The process starts with the gesture acquisition by the webcam and presented through video images (fig. 5).

Segmentation

Segmentation is the main part of the entire process and it assumes a sequence of tasks (Fig. 6). This stage makes the human hand recognition by skin color detection. The procedure starts with the conversion captured for the normalized RGB color space. Knowing that the majority of human skin color tends to cluster in the red channel, we extract this channel and then, we subtract the background of the extracted red channel image. Background subtraction allows us to obtain a greyscale image with color intensity changes between the background and the hand, as shown in (fig. 7). Then, we design the image histogram and compute the optimal thresholding value. We used the Method of the Valley that consists in checking how many regions there are (peaks and valleys) in the histogram and use the valley information to set the value of the threshold. Fig. 8 shows the histogram and optimal threshold value identified.

Feature Finding

The previous step provides an image where we can find the edge pixels that separate the hand from the background, but without information about the edges as entities in themselves, thus, the next step is to be able to gather the whole pixels on the edge contours surrounding the stain of segmented hand. Fig. 10 shows that trough the extracted contour we can calculate several structures such as convex hull, contour edges, fingers and surrounding rectangles.

Feature extraction

The feature extraction lets obtaining a set of feature vectors, also called descriptors that can distinguish, accurately, each hand gesture. Our approach proposes the use of geometric features with Hu invariant moments [START_REF] Hu | Visual Pattern Recognition by Moment Invariants[END_REF]. The presented features are the most discriminatory from the six gestures chosen: contour size; contour circularity; contour eccentricity; convex hull size; bounding box proportion; bounding box area; rotated bounding box area; main inertia axe; Hu Moments. Some problems during the gesture recognition may occur if there is a large rotational movement, because the shape and proportions of features that represent the gesture may change.

We noticed that these features are easy to extract, are independent and are sufficient to distinguish the amount of gestures chosen by us, however, these are not suitable for all types of gestures.

Classification

For pattern recognition and classification, we choose the Support Vector Machines (SVM) machine learning technique that has been recognized in recent years. This technique is used in various pattern recognition tasks, always with results superior to those achieved by similar techniques in many applications. Its use comes from some of its main characteristics [START_REF] Smola | Geometry and invariance in kernel based methods[END_REF]:

• Good generalizability;

• Robustness in larger dimensions;

• Well-defined theory in Mathematics and Statistics.

Tests

The system performs well as long as the skin-like colors in the background do not exist and if it is tested indoor. Results pointed for a good removal of luminosity influence during the segmentation process, so it becomes less dependent on the lighting conditions, which has always been a critical obstacle to the image recognition. The system operates in real time with around 30 frames per second, requiring an effort to minimize time processing of the tasks. We saved 100 images representing each gesture to our database. For testing we have also saved 100 images for each gesture of 5 different people.

Table 1 summarizes the results of the hand classification tests.

Tasks and Activities to Build the Prototype

The main goal of the proposed work was the development of a system for recognizing static gestures of human hands of different people, situated in front of a webcam. The choice of appropriate technology for this type of system could limit the development and visualization of the results.

This section presents and clarifies the work context for the development of the proposed system. Considering the literature review process carried out, we discovered interesting and innovative approaches for the application's development based on the gestural recognition. However, we found an absence of this type of systems for assistive technologies regarding wheelchair intelligent robotic arms.

Based on the lack of information, we create our design process for the proposed system. The decision about the software and hardware to be used on the development of our system was the main concern. Conversely, we needed to consider the basic principles defined previously: the system should operate in real time with minimal restrictions and the system should have a low cost demand in order to be applied in everyday life by a significant number of users who needed it.

Based on these decisions fig. 11 presents the first phase of the design process: The relationship between quality and performance for the real-time system was taken into account.

Fig. 12 shows the implementation task process referring the decision taken during each task: image acquisition, color space conversion, histogram processing, hand segmentation, feature finding, feature extraction, classification of gestures and system tests. Some potential users of the system, around twelve, were, informally, interviewed, at the beginning of this project and later on a prototype phase. They considered that the proposed solution will be important since it preview affordance for a highest number of people who have social, financial or accessibility restrictions.

Conclusions

It has been shown that it is possible to interact with a machine naturally and intuitively through hand gestures without requiring support material such as gloves or markers. And that there is a possibility to develop Computer Vision interactive systems based and implemented on conventional computers using inexpensive devices, accessible to all, facilitating, especially disable mobility people with an acceptable level of environmental restrictions.

The proposed system produces satisfactory results with fixed constant lighting conditions, with a non-skin color scenario. However, the method used for skin segmentation exposes some flaws, mainly caused due to lighting variations causing glare, reflections and shadows. These shortcomings are minimized applying morphological filters and smoothing filters.

Conversely, the systems works with quite different hand gestures, but it shows some weakness when using more identical gestures such as sign language. Thus, the scope of this work is limited to the recognition of gestures; we wanted to identify the static gesture shown by the occupant of the wheelchair. The prototype that we outline has not been built so far.

In future work, the study of methods that may respond better to lighting variations, to different scenarios and how to be more effective in identifying the skin, even from other races would be helpful. The gesture recognition through dynamic movement analysis can also be a target for further investigation. However, we launched the challenge to awake attentions and possible interests for the implementation of this type of equipment that will be, in our point of view, more disinhibiting action of the individual, because it determines the element that are not always available to anyone.

Fig. 1 .

 1 Fig. 1. Problematic situation

  , fulfilling each a specific function in the context of facilitating the mobility of the person: (a) pause; (b) stretch mechanical arm with speed 1 (c) stretch mechanical arm with speed 2 (d) open claw, (e) close claw; (f) move arm to the starting position.

Fig. 3 .

 3 Fig. 3. Hand recognition gestures

Fig. 4 .

 4 Fig. 4. Fundamental steps for the proposed work

Fig. 5 .

 5 Fig. 5. Captured frame

Fig. 6 .

 6 Fig. 6. Key steps of segmentation

Fig. 7 .

 7 Fig. 7. -a) Normalized RGB image b) Red channel extracted c) Background subtracted

Fig. 8 .

 8 Fig. 8. Image Histogram with optimum threshold identified

Fig. 9 .

 9 Fig. 9. Binary image

Fig. 10 .

 10 Fig. 10. Calculated structures to feature extraction

Fig. 11 .

 11 Fig. 11. The design process -first phase

Fig. 12 .

 12 Fig. 12. Implementation task process

Table 1 .

 1 Gesture classification results

	Gestures	Frames Correct Incorrect % Correct % Incorrect
	A, B, C, D, E, F	3000	2974	26	99.1%	0.9%