Michel Vacher
email: michel.vacher@imag.fr

Saida Bouakaz
email: saida.bouakaz@univ-lyon1.fr

Marc-Eric Bobillier-Chaumon
email: marc-eric.bobillier-chaumon@univ-lyon2.fr

F Aman

Rizwan Ahmed Khan

S Bekkadja

François Portet

Erwan Guillou

S Rossato

Benjamin Lecouteux

M.-E Bobillier Chaumon

The CIRDO Corpus: Comprehensive Audio/Video Database of Domestic Falls of Elderly People

Keywords: audio and video data set, multimodal corpus, natural language and multimodal interaction, Ambient Assisted Living (AAL), distress situation

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Twenty first century is witnessing a rapid growth in population over 65 years old worldwide [START_REF] Who | World population ageing[END_REF]. This change in demography put pressure on governments which are incurring unprecedented expenditure to support ageing population since ageing is correlated with an increase in health and daily living support. For instance, in France, 12 million out of 66 million people are receiving the personalized allocation of autonomy (PAA)1 , a government financial aid for elderly person to support them in their daily life. This demographic change is very challenging to the society, governments and to technologists to come up with reliable and sustainable solutions to help ageing population to live as independently and safely as possible while relieving their family's emotional and financial burden. However this trend also provides the opportunity to come up with technologies that can help ageing population to carry on with their lives without compromising their privacy and at the same time provide assistance to caregivers i.e. nurse, partner, children etc. One of the main sources of stress in the ageing population for which technology can be of help is the fall incident. Indeed fall is a major source of injury for elderly people, between 20 and 40 percent of adults over 65 who live at home fall [START_REF] Saim | Accidental falls amongst the elderly: Health impact and effective intervention strategies[END_REF]. Fall could lead elderly persons to faint or become unconscious. Another emergency situation is the inability to call for a help in distress situation of some aged persons due to their reduced mobility. In the absences of another person in the home (such as a carer), these situations could have disastrous effect. Several studies had worked on an ICT solutions to bring autonomy and security back to the user [START_REF] Popescu | An acoustic fall detector system that uses sound height information to reduce the false alarm rate[END_REF][START_REF] Hamill | Development of an automated speech recognition interface for personal emergency response systems[END_REF][START_REF] Young | An automated, speech-based emergency response system for the older adult[END_REF][START_REF] Bloch | Evaluation under real-life conditions of a stand-alone fall detector for the elderly subjects[END_REF]. These studies have followed the paradigm shift in Human Computer Interaction design, from computercentred designs to human-centred designs [START_REF] Pantic | Human computing and machine understanding of human behavior: survey[END_REF]. Thus reliable and robust analysis of ageing population's gesture and voice has become inevitable in many application areas, such as social robots, emergency situation detection, behaviour monitoring, communication support, etc. Since many of these studies are based on statistical analysis (either for modelling or for machine learning), there is a dire need of databases of audio and video recordings to build and evaluate performance of systems that can analyse ageing population's gesture and voice. Indeed evaluation and benchmarking of systems / algorithms must be performed on standardized and accessible databases for meaningful comparison. In the absence of comparative tests on such standardized databases it is difficult to exhibits the relative strengths and weaknesses of these systems. To the best of our knowledge there exists no such database that has video with audio recordings of aged persons during fall and distress situations while it is recognised as one of the major problem in the evaluation of fall detectors [START_REF] Igual | Challenges, issues and trends in fall detection systems[END_REF]. [START_REF] Young | The CARES corpus: a database of older adult actor simulated emergency dialogue for developing a personal emergency response system[END_REF] have collected a corpus of spontaneous speech, read sentences, and emergency scenarios from adult actors aged 23-91 years but it is in English and do not contain videos recording. This could be due to the fact that it is very difficult to setup a camera and microphone to record such events. Secondly, there is an ethical issue associated with recording aged person in distress situation and later making it available to public. Very few studies were able to record real falls of elderly persons [START_REF] Kangas | Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects[END_REF][START_REF] Klenk | Comparison of acceleration signals of simulated and real-world backward falls[END_REF] but those were only concerned with accelerometer sensors. To overcome this bottleneck in the research and development of emergency situation detectors, we introduce the CIRDO database which is part of the CIRDO project [START_REF] Bouakaz | CIRDO: Smart companion for helping elderly to live at home for longer[END_REF] whose aim was to develop an audio/video emergency detection system for elderly persons.

The CIRDO database contains video with audio (French language) recordings of aged persons during falls and distress situations. CIRDO database can be used as standardized database by researchers to evaluate and compare their systems for elderly person's assistance. In this article, we provide all the details related to various steps that have been carried out to record data in such a challenging situation.

Data Acquisition

Recording sessions were based on written scenarios. These scenarios were well elaborated thanks to a field study involving 15 elderly persons [START_REF] Bobillier-Chaumon | Démarche de développement de technologies ambiantes pour le maintien à domicile des personnes dépendantes : vers une triangulation des méthodes et des approches[END_REF]. Four scenarios were related to fall (F1 to F4), one to blocked hip (B) and two were true negative (TN1 and TN2). Recording sessions were conducted in the DOMUS smart room of the LIG laboratory configured to look like a standard room (chairs, carpet, coffee table, TV. . . ).

Participants

Ideally scenarios should be played by elderly people. However, it is cumbersome to find elder person who is capable and willing to play such scenarios (refer Section 2.2.).

To record realistic data (but not necessarily played by elder person), people under 60 were recruited. Then, these volunteers were instructed to wear an equipment i.e. old age simulator (see Figure 1). Old age simulator hampered mobility, reduced vision and hearing. Overall 17 participants were recruited (9 men and 8 women) with mean age of 40 years (SD 19.5). Among them 13 people were under 60 and worn the simulator.

Experimental Protocol

Each participant was introduced to the context of the research and was invited to sign a consent form. The participants played five types of fall chosen from 28 risky situations identified as: slip, stumble, falls in a stationary position and a position of hip blocked on the sofa. Figure 2 shows two elderly participants when they fall on the carpet. These situations were selected because they were representative falls in domestic environment and could be played safely. Two other scenarios, called "true-false", were added for the evaluation and verification of automatic fall detection. The first "true-false" situation consists of a rapid action of picking up of magazines from the floor (close to a situation of fall), while the second was to try to hold a remote control from the coffee table when the person is sitting on sofa (close to a situation in which the person has a blocked hip). Scenarios included call for help sentences. These sentences were chosen based on sentences identified during the field study and on sentences extracted from the AD80 corpus [START_REF] Aman | Speech Recognition of Aged Voices in the AAL Context: Detection of Distress Sentences[END_REF] which was built with the aim of elderly voice study. The participant rehearsed the scenario several times before final acquisition of data. On average, the duration of acquisition was 2 hours and 30 minutes per person.

Equipment and Tools

The studio and the equipment used are shown in Figure 1. Moreover, the DOMUS smart home was equipped with the social inclusion product e-lio2 .

Regarding audio analysis, we conducted a multi-source capture with two wireless Sennheiser microphones. A eW-300-G2 type ME2 microphone was placed on the suspended ceiling of the studio, and a SKM-300-G2 microphone was placed on a furniture close to the participant.

The recording was performed in the technical room by a computer connected to the microphones high-frequency receivers by National Instruments PCI-6220 8-channel card.

A loudspeaker in the control room enabled experimenters to hear the progression of the scene in the studio. The National Instruments PCI-6220 card was controlled by the StreamHIS software [START_REF] Vacher | Development of Audio Sensing Technology for Ambient Assisted Living: Applications and Challenges[END_REF] developed by the GETALP team that enables data acquisition.

Regarding video analysis, we conducted the video capture with two webcam cameras (Sony PSEye: 640x480 60Hz connected by a USB 2.0). Cameras were fixed to the wall, one in front of the Sofa and other at an angle of 45 degrees. Moreover, we acquired depth image with Microsoft Kinect. Synchronization and recording of video streams from all three cameras were performed in the technical room by computer (Intel i3 3.2GHz, 4GB RAM, NVidia GeForce GTS 450 512MB). The tools that enable video acquisition was developed by the team SAARA (based on the OpenCV library). Video processing (background learning and subtraction, body parts segmentation) was performed on a PC cluster, taking advantage of algorithms parallelization on CPU and GPU. Motion tracking and identification of hazardous situations was done on a PC (Intel i7 3.5GHz, 4GB RAM, NVIDIA GTX 660 4GB GeFore).

The Acquired Corpus

The CIRDO corpus is divided into three parts, the audio corpus for sound and speech analysis, the video corpus and a third part with the annotations. The corpus is detailed in Sections 3.1. and 3.2.. The audio and video corpus was annotated for the video part, with the Advene software3 , developed at the LIRIS laboratory, and for the sound part, with Transcriber.

The CIRDO corpus is a web-based data library, hosted at LIRIS and accessible for the academic and research purpose. The license to use the CIRDO corpus is granted on a case-by-case basis. We can also grant permission to researchers to extend the corpus given they fulfil the required conditions and sign agreement.

Audio Corpus

When they played the scenarios, some participants produced sighs, grunts, coughs, cries, groans, panting or throat clearings. As our current studies are in the domain of automatic speech recognition, these sounds were not considered during the annotation process, but this can be done in the framework of future studies. In the same way, speeches mixed with sound produced by the fall were ignored. At the end, each speaker uttered between 10 and 65 short sentences or interjections ("ah", "oh", "aïe", etc.) as shown Table 3. Sentences were often close to those identified during the field studies ("je peux pas me relever -I can't get up", "elio appelle du secours -e-lio call for help", etc.), some were different ("oh bein on est bien là tiens -oh I am in a sticky situation"). In practice, participants cut some sentences (i.e., inserted a delay between "e-lio" and "appelle ma fille -call my daughter"), uttered some spontaneous sentences, interjections or non-verbal sounds (i.e., groan)

F1 F2 F3 F4 B TN1 TN2 Y01 1 2 1 1 1 1 1 8mn 40s Y02 1 1 1 1 1 1 1 4mn 35s Y03 1 1 1 3 1 1 2 6mn 30s Y04 1 1 1 2 1 1 1 5mn 54s Y05 1 1 1 3 2 1 2 8mn 50s Y06 1 1 1 1 1 1 1 5mn 07s Y07 1 1 1 1 1 1 2 5mn 17s Y08 1 1 2 1 1 1 2 7mn 04s Y09 3 1 1 1 1 1 2 6mn 48s Y10 1 1 2 3 1 1 1 5mn 50s Y11 2 1 2 3 1 1 1 7mn 31s Y12 1 1 1 2 2 1 2 8mn 01s Y13 2 2 1 1 1 1 1 5mn 54s S01 1 1 3 2 3 2 2 9mn 07s S02 1 2 1 2 1 1 2 6mn 31s S03 1 1 1 3 1 1 1 6mn 00s S04 1

Video Corpus

The content of the video corpus is displayed in Table 4 As mentioned above, the video corpus includes various types of fall according to the scenarios defined in the Protocol (refer Section 2.2.). Each scenario includes a background sequence (300-600 frames) which usually is required for human body extraction. Description of the corpus is given in Table 4 wherein F1 to F4 are the fall scenarios, B blocked hip and TN1, TN2 true negatives. The total duration of the recordings is equal to 1 hour and 55 minutes, with a total of 162 video segments.

Conclusion Drawing from Feedback from the Participants

Feedback from the participants were analyzed and allowed some conclusions [START_REF] Body-Bekkadja | Understanding the Socio-Domestic Activity: A Challenge for the Ambient Technologies Acceptance in the Case of Homecare Assistance[END_REF]. A highlighted domestic accident, outlined (for oneself and for others) by CIRDO, leads the elderly individual to relate to the falls in other ways. They suddenly attempt to further regulate and control it, sometimes by risking a profound change in behavior that may not be detected by CIRDO. More specifically, in our various studies (simulated CIRDO use), it was observed that: The elderly developed two opposing conducts in the management of their fall monitored via CIRDO. Some of them chose to boost their falling movement or overplay screams to make sure the device will recognize the danger. They were also the same elderly that, as we mentioned in Study 2, used tangible remote monitoring systems (inset). They doubted the ability of the new ambient system to detect their accident, and thus they risk a more serious injury. Others, however, in order to hide these incidents from the entourage, try to control the fall: They do not scream, and try to recover at all costs, even though it could exacerbate the deleterious effects of the fall.

In both cases, we see that the falling movement choreography (falling is unintentional by nature) will be intentionally modified by the subjects, whether to be seen to fall, and, hence, be better recognized, or otherwise to be hidden from the technology and its supervision. Thus, the presence of technological artifacts changes the dynamic of the fall, because this behaviour will be addressed, directed toward a target (technology object), and also to other involved individuals. As mentioned by Clot [START_REF] Clot | La fonction psychologique du travail[END_REF], the movements of the falls are not only directed by the conduct of the subject; they are also directed through the use of the technical object, and toward others (the representation they have of the system the possible recipients of the alert). The fall choreography is influenced by what the individual wants to show to, or hide from, the device. Therefore, the risk of non-detecting these movements is possible, because they sometimes tend to veer too far from programmed scripts. Another more symbolical consequence involves the status and the legitimacy that CIRDO will give the fall. While, in the past, the word of the victim, or of a third party, used to be enough to prove the occurrence of a fall, these days, surveillance technologies are used to validate/approve the occurrence of an incident. We could even add that, they also assess whether this fall is acceptable, and conform to pre-defined scripts. In other words, the non-detection (nonrecognition) of a fall by CIRDO may mean that: (1) The individual has poorly conducted the choreography of the falling; or (2) the movement cannot be categorized as a risk movement. This can therefore lead to a denial of risk and recognition of the elderly individual's status of "victim", thereby throwing suspicion on their statements. As a result, CIRDO may operate a transfer of the risk recognition: It is the technology that gives official status to a fall in a domestic incident. It gives credibility and legitimacy to it. Thus, technological reliability wins against the word of the elderly; and then the latter can be discredited, for ex-ample, if the user talks about incidents not recognized by technology: "If the system is not triggered, then the fall did not occur." In both cases, setting CIRDO service in the domestic social system can either redefine the falls choreography, or redefine their status. All these reasons may be caused by malfunction or rejection. Several recommendations can be addressed to the designers. In addition to reassuring, involving, and properly training to use the new system [START_REF] Hwang | The effect of user engagement on system success: A meta-analytical integration of research findings[END_REF]Thorn, 1999)(Kujala, 2003), by including demonstrations and updates in real situations adapted to their habits and lifestyle practices (to demonstrate the system works efficiently in detecting falls, and avoiding any worsening of movements), designers should create programs and falls-detection algorithms more flexible, to cover a larger spectrum of falls choreography, and not rely solely on fixed scripts in risk behavior.

In the same way, effective articulation between Audio and Video detections should be ensured to allow better fallsrecognition and validation.

As part of a participatory design through use [START_REF] He | The role of user participation in information systems development: Implications from a meta-analysis[END_REF], it would be interesting to adjust from feedbacks in the field, the location of the sensors and the level of falls scripts detection, by taking into account conductadjustments and daily risk evolutions from the moment the technological artifact is introduced. This requires a situated analysis of the actual device's uses, in order to re-design it from its usage [START_REF] Norman | User Centered System Design[END_REF]. This reinforced monitoring system will ask the elderly to make an oral (confirmation) emergency call to an outside third party. This alert is then triggered only in case of a positive response, or a lack of response from the individual, enabling them to properly control the system (at to avoid false alarms). Finally, this new device for monitoring the activity will be meaningful only if it serves and supports its users' quality of life. Therefore, one must be careful to involve and assist the various participants during all phases of CIRDO design, in an inclusive approach (as in the Living lab approach of [START_REF] Pino | Innovative technology-based healthcare and support services for older adults: How and why industrial initiatives convert to the living lab approach[END_REF], especially to anticipate reconfigurations at work in the social framework, as we shall now see.

Call for Help Recognition from Audio Analysis

The audio corpus was used for studies related to call for help recognition in Distant Speech conditions thanks to online speech analysis using SGMM acoustic modelling (Vacher et al., 2015a) [START_REF] Vacher | Recognition of Distress Calls in Distant Speech Setting: a Preliminary Experiment in a Smart Home[END_REF]. Non-speech analysis might bring information of high interest but it was not considered in our study because of the lack of training data. Currently non-speech audio analysis is a relatively unexplored field due not only to the lack of data but also to the unexpected sound classes that can be recorded at home in unconstrained conditions [START_REF] Vacher | Development of Audio Sensing Technology for Ambient Assisted Living: Applications and Challenges[END_REF]. Moreover, non-speech sounds are made of non-verbal sounds (i.e. groans, panting, throat clearings, etc.) and of daily living sounds (i.e. falling objects, door slap, etc.) which represent different semantic information and are difficult to differentiate. For these reasons, only speech analysis was considered.

Acoustic modelling

The Kaldi speech recognition tool-kit [START_REF] Povey | The Kaldi Speech Recognition Toolkit[END_REF] was chosen as ASR system. Kaldi is an open-source stateof-the-art Automatic Speech Recognition (ASR) system with a high number of tools and a strong support from the community. In the experiments, the acoustic models were context-dependent classical three-state left-right HMMs. Acoustic features were based on Mel-frequency cepstral coefficients, 13 MFCC-features coefficients were first extracted and then expanded with delta and double delta features and energy (40 features). Acoustic models were composed of 11,000 context-dependent states and 150,000 Gaussians. The state tying is performed using a decision tree based on a tree-clustering of the phones. In addition, off-line fMLLR linear transformation acoustic adaptation was performed.

The acoustic models were trained on 500 hours transcribed French speech composed of the ESTER 1&2 (broadcast news and conversational speech recorded on the radio) and REPERE (TV news and talk-shows) challenges as well as from 7 hours of transcribed French speech of the SWEET-HOME corpus [START_REF] Vacher | The Sweet-Home speech and multimodal corpus for home automation interaction[END_REF] which consists of records of 60 speakers interacting within a smart home and from 28 minutes of the Voix-détresse corpus [START_REF] Aman | Reconnaissance automatique de la parole de personnes âgées pour les services d'assistance à domicile[END_REF] which is made of records of speakers eliciting a distress emotion.

Subspace GMM Acoustic Modelling

The GMM and Subspace GMM (SGMM) both model emission probability of each HMM state with a Gaussian mixture model, but in the SGMM approach, the Gaussian means and the mixture component weights are generated from the phonetic and speaker subspaces along with a set of weight projections. The SGMM model (Povey et al., 2011a) is described in the following equations:

         p(x|j) = Mj m=1 c jm I i=1 w jmi N (x; µ jmi , Σ i ), µ jmi = M i v jm , w jmi = exp w T i vjm I i =1 exp w T i vjm . (1)
where x denotes the feature vector, j ∈ {1..J} is the HMM state, i is the Gaussian index, m is the substate and c jm is the substate weight. Each state j is associated to a vector v jm ∈ R S (S is the phonetic subspace dimension) which derives the means, µ jmi and mixture weights, w jmi and it has a shared number of Gaussians, I. The phonetic subspace M i , weight projections w T i and covariance matrices Σ i i.e; the globally shared parameters Φ i = {M i , w T i , Σ i } are common across all states. These parameters can be shared and estimated over multiple record conditions. A generic mixture of I Gaussians, denoted as Universal Background Model (UBM), models all the speech training data for the initialization of the SGMM. Our experiments aims at obtaining SGMM shared parameters using both SWEET-HOME data (7h), Voix-détresse (28mn) and clean data (ESTER+REPERE 500h). Regarding the GMM part, the three training data set are just merged in a single one. (Povey et al., 2011a) showed that the model is also effective with large amount of training data. Therefore, three UBMs were trained respectively on SWEET-HOME data, Voix-détresse and clean data. These tree UBMs contained 1K gaussians and were merged into a single one mixed down to 1K gaussian (closest Gaussians pairs were merged [START_REF] Zouari | Efficient gaussian mixture for speech recognition[END_REF]). The aim is to bias specifically the acoustic model towards distant speech home and expressive speech conditions.

Recognition of distress calls

The recognition of distress calls consists in computing the phonetic distance of an hypothesis to a list of predefined distress calls. Each ASR hypothesis H i is phonetized, every predefined voice command T j is aligned to H i using Levenshtein distance. The deletion, insertion and substitution costs were computed empirically while the cumulative distance γ(i, j) between H j and T i is given by Equation 2.

γ(i, j) = d(T i , H j )+ min{γ(i -1, j -1), γ(i -1, j), γ(i, j -1)} (2)
The decision to select or not a detected sentence is then taken according a detection threshold on the aligned symbol score (phonems) of each identified call. This approach takes into account some recognition errors like word endings or light variations. Moreover, in a lot of cases, a missdecoded word is phonetically close to the good one (due to the close pronunciation). From this the CER (Call Error Rate i.e., distress call error rate) is defined as:

CER = Number of missed calls Number of calls (3)
This measure was chosen because of the content of the corpus Cirdo-set used in this study. Indeed, this corpus is made of sentences and interjections. All sentences are calls for help, without any other kind of sentences (e.g., colloquial sentences), and therefore it is not possible to determine a false alarm rate in this framework.

Off line experiments

The methods presented in previous sections were run on the Cirdo-set audio corpus presented in Section 3.1.

The SGMM model presented in Section 5.1. was used as acoutic model. The generic language model (LM) was estimated from French newswire collected in the Gigaword corpus. It was 1-gram with 13,304 words. Moreover, to reduce the linguistic variability, a 3-gram domain language model, the specialized language model was learnt from the sentences used during the corpus collection described in Section 2.2., with 99 1-gram, 225 2-gram and 273 3-gram models. Finally, the language model was a 3-gram-type which resulted from the combination of the generic LM (with a 10% weight) and the specialized LM (with 90% weight). This combination has been shown as leading to the best WER for domain specific application [START_REF] Lecouteux | Distant Speech Recognition in a Smart Home: Comparison of Several Multisource ASRs in Realistic Conditions[END_REF]. The interest of such combination is to bias the recognition towards the domain LM but when the speaker deviates from the domain, the general LM makes it possible to avoid the recognition of sentences leading to "falsepositive" detection. Results on manually annotated data are given Table 5. The most important performance measures are the Word Error Rate (WER) of the overall decoded speech and those of the specific distress calls as well as the Call Error Rate (CER: c.f. equation 3). Considering distress calls only, the average WER is 34.0% whereas it a 39.3% when all interjections and sentences are taken into account.

On average, CER is equal to 26.8% with an important disparity between the speakers.

Unfortunately and as mentioned above, the used corpus does not allow to determine a False Alarm Rate. Previous studies based on the AD80 corpus showed recall, precision and F-measure equal to 88.4%, 86.9% and 87.2% [START_REF] Aman | Speech Recognition of Aged Voices in the AAL Context: Detection of Distress Sentences[END_REF]. Nevertheless, this corpus was recorded in very different conditions, text reading in a studio, in contrary of those of Cirdo-set.

Fall Detection from Video Analysis

The video analysis framework for fall events labeling is based on the silhouette extraction. The silhouette is extracted by removal of background pixels within the video scene using. The background subtraction is performed by Mixture of Gaussian based approach, and eigenbackground (PCA) approach [START_REF] Deeb | Real-time two-level foreground detection and person-silhouette extraction enhanced by body-parts tracking[END_REF], [START_REF] Shah | Comperehensive study and comparative analysis of different types of background subtraction algorithms[END_REF]. The extracted shape is then used to obtain discriminative features to detect anomaly in person's movement in the scene. The overview of proposed framework is shown in Figure 3.

Foreground extraction and modeling of body parts

After silhouette extraction the human body is segmented into colored connected components. Colored connected components regarded as regions are extracted by applying region growing technique. Each component with similar color are fitted into blob [START_REF] Hsieh | Segmentation of human body parts using deformable triangulation[END_REF]. Blob is represented by spatial color Gaussian mixture model, assuming that spatial color components are de-correlated. The probability of an observation X t of that pixel at time t to belong to the background is given by equation 4.

P (X t ) = L k=1 ω k,t .η(X t , µ k,t , Σ k,t ) (4)
where L is the number of gaussian, η is Gaussian probability density function, Σ k,t is an estimates of weight and ω k,t is covariance matrix of the k-th Gaussian in the mixture at time t. In the last, an energy based function is formulated over the unknown labels of every pixel in the form of a first order Markov random field (MRF) energy function:

E(f ) = p∈P D p (f p ) + λ p,q∈Ne V p,q (f p , f q ) (5)
Here, N e is neighboring pixels and the data energy Σ p∈P D p (f p ) evaluates the likelihood of each pixel to take a label. Finally, energy function is minimized with a graph cut algorithm via a swap approach (Miguel Angel [START_REF] Bautista | Hupba8k: Dataset and ecoc-graph-cut based segmentation of human limbs[END_REF]. Background model is updated selectively with an online EM algorithm [START_REF] Moon | The expectation-maximization algorithm[END_REF].

Tracking of body parts

Our method begins with an iterative process and considers that person is in standing position. Body is decomposed in three parts i.e. head, torso and lower part. Then, dynamic processing technique is used to obtain different level of body details (i.e. number of blobs), if required. At first, a blob (k-th blob) is built for each connected component with a feature vector containing: color, center's coordinates C k , a predicted change of color P k and velocity V k . Set of current colored connected components are extracted from the smoothed foreground by region growing. By considering tracking of blobs as a matching process, we introduce a novel cost function to obtain distance between two blobs. The cost function is given by:

Cost(j, k) = α 1 ( P k -P t j /V k )+α 2 ( C j -C M k / C k ) (6
) where C t j and P t j respectively, the color and the center's coordinates of the j-th connected component extracted at time t.

Pose recognition and labeling of events

The labeling of events is based on the scenario define above. To identify poses, the extracted silhouette from the body parts was used. To recognize and label different poses from the video data, an histogram based approach was used as they runs in real time and improves its accuracy with the passage of run time The recognition wss based [START_REF] Barnachon | Human actions recognition from streamed motion capture[END_REF]. The histograms were then compared with the Bhattacharyya distance and warped by a dynamic time warping process to achieve their optimal alignment [START_REF] Barnachon | Ongoing Human Action Recognition with Motion Capture[END_REF]. Bhattacharyya distance is calculated using:

d(H 1 , H 2 ) = 1 -Σ I H 1 (I).H 2 (I) Σ I H 1 (I).Σ I H 2 (I) (7)
Where H 1 and H 2 are integral histograms.

In this study we focused on the analysis of posture to detect event of distress. Our framework for fall detection could further be enhanced by considering other factors as well, i.e. facial expression analysis. Analysis of facial expressions to detect pain can be very advantageous in case of prolonged immobility of elderly person [START_REF] Khan | Pain detection through shape and appearance features[END_REF].

Conclusion

This paper investigated smart home technology and recorded comprehensive audio/video corpus of domestic falls. For elderly, the fall is one of the most feared and recurring problems. Surveillance technologies tries to provide solution to this issue by alerting contact person in case of fall. There is lot of work which focuses on this problem but to the best of our knowledge there is no common database for comparing results. To address this issue we have made available a database composed of audio/video records of falls and prolonged immobility. In this paper, studies using this database for the automatic identification of these events were also presented.

The CIRDO corpus was recorded in a smart environment reproducing a typical living room containing an e-lio communication device equipped with microphone and camera. The experiment was guided by an ethnographic study which detailed the various events related to distress situations faced by elderly people. Various scenarios were established from this study that describes pattern of postures in case of fall so that acted falls were as close to real situation as possible. The database can thus be useful to researchers of the community studying video or audio emergency situations as well as to model the relationship between audio/video events and learn fall model using machine learning.

Figure 1 :

 1 Figure 1: (a) Experimental room of the LIG with its equipment and (b) screen shot of a young participant wearing a simulator during the training phase.

Figure 2 :

 2 Figure 2: Excerpt of 2 video records showing each the fall of an elderly participant.

Figure 3 :

 3 Figure 3: Event detection, video pipeline.

Figure 4 :

 4 Figure 4: Event detection, results

Table 1 :

 1 Characteristics of the participants of the records

	Reference	Age	Sex	Reference	Age	Sex
	Y01	30	M	Y07	52	M
	Y02	24	F	Y08	28	M
	S01	83	F	S04	66	M
	Y03	29	M	Y09	52	F
	S02	64	F	Y10	23	M
	S03	61	M	Y11	40	F
	Y04	44	M	Y12	40	F
	Y05	16	M	Y13	25	F
	Y06	16	M

(Sxx: senior people -Yxx: younger people)

  Table 2 gives some examples.

	Distress Sentence	AD80 Sentence
	Aïe aïe aïe	Aidez-moi
	Oh là	Au secours
	Merde	e-lio, appelle du secours
	Je suis tombé	e-lio appelle ma fille
	Je peux pas me relever	Appelle quelqu'un e-lio
	Qu'est-ce qu'il m'arrive	e-lio, appelle quelqu'un
	Aïe ! J'ai mal	e-lio appelle ma fille
	Oh là ! Je saigne ! Je me suis blessé	e-lio appelle le SAMU

Table 2

 2

	: Some examples sentences [in French] identified
	for inclusion in the scenarios
	Before each recording session, the experimenter explained
	the scenario to the participant. Scenario has following sub-
	components: physical location, time of the day, activity
	(what the person intends to do and what eventually hap-
	pens), gesture (way of falling), and the spoken sentences.

Table 3 :

 3 Composition of the audio corpus Cirdo-set

			Nb. of interjections
		Spk.	or short sentences	Size
			All	Call for help	(s)
		Y01	22	14	37.59
		Y02	16	15	27.51
		Y03	24	21	35.59
		Y04	25	15	43.97
		Y05	32	21	38.16
		Y06	19	15	48.25
		Y07	12	12	18.75
		Y08	15	12	23.58
		Y09	23	21	39.86
		Y10	20	19	29.83
		Y11	29	27	43.96
		Y12	24	21	33.54
		Y13	17	14	25.32
		S01	65	53	92.07
		S02	23	19	31.21
		S03	23	21	26.02
		S04	24	21	50.33
		ALL	413	341	645.54
	No	Occurrence number of each scenario	Time

Table 4 :

 4 Video Corpus for the two group of participants

Table 5 :

 5 Word and Call Error Rate for each participant

		WER (%)			WER (%)
	Spk.	All	Call for	CER	Spk.	All	Call	CER
			help	(%)			for help	(%)
	Y01	45.0	39.1	27.8	Y07	21.3	17.0	16.7
	Y02	41.4	44.4	40.0	S04	30.8	25.0	25.0
	S01	51.9	49.6	34.0	Y08	45.9	43.6	23.8
	Y03	19.1	15.4	14.3	Y09	67.0	54.8	50.0
	S02	39.2	34.3	26.3	Y10	21.5	19.5	5.3
	S03	21.2	20.3	28.6	Y11	14.9	11.76	7.4
	Y04	61.8	50.8	20.0	Y12	21.4	22.4	19.0
	Y05	49.4	41.2	33.3	Y13	57.7	44.9	71.4
	Y06	24.5	22.4	14.3	All	39.3	34.0	26.8

This allowance from the state is given to people over 60 years old in loss of autonomy

http://www.technosens.fr

http://liris.cnrs.fr/advene/

Acknowledgements

This work was supported by the French funding agencies ANR and CNSA through CIRDOproject (ANR-2010-TECS-012). The authors would like to thanks the persons who agreed to participate in the survey or in the recordings.


I Verrier

C Veillas

T Lépine

Low coherence interferometry for central thickness measurement of rigid and soft contact lenses

In this paper we propose contact lens central thickness measurement with a low coherence interferometry technique using either a SLED source or a broadband continuum generated in air-silica Microstructured Optical Fiber (MOF) pumped with a picosecond microchip laser. Each of these sources associated with the interferometer provides, at the same time, good measurement resolution and quick signal recording without moving any optical elements and without need of a Fourier Transform operation. Signal improvement is performed afterwards by a numerical treatment for optimal correlation peaks detection leading to central thickness value of several contact lenses.

Introduction

Daily or monthly disposable contact lenses are more and more appreciated by their users particularly because of their comfort, their obvious convenience and their clinical performance. Thus, the request from the contact lenses wearers is growing and requires an increase in their fabrication. So a fast and precise control of their biological and geometrical characteristics becomes necessary.

The central thickness measurement of the contact lens during its fabrication is essential because of its influence on the final thickness and then its ability to correct in a proper way a refractive default. Usually, the central thickness of random samples from the fabrication batch is measured with a resolution around 0.01 mm by destructive methods. Recently, apparatus have been available on the market allowing the test of 100% of the fabricated products [START_REF] Marcus | Apparatus for measuring Material Thickness Profiles[END_REF]. These apparatus are based on non-contact optical low coherence interferometric techniques with superluminescent diodes (SLED) at the wavelength of 1300 nm [START_REF] Marcus | Method for measuring material thickness profiles[END_REF][START_REF] Blalock | Practical applications in film and optics measurements for dual light source interferometry[END_REF]. The principle of the measurement consists in comparing a reference pulse with the reflected light from the different layers of the sample under test. The reference pulse has to be synchronized with each of the pulses reflected by the different boundaries of the sample by adjusting for each measurement the delay of the reference pulse with those issued from the sample. To do that, the reference path is modulated by moving the delay line. This technique authorizes a great precision of the order of 1 µm without destruction of the sample.

As written above, the classical low coherence interferometric methods as TD-OCT (Time Domain -Optical Coherence Tomography) require the displacement of an optical element in the experimental set-up to analyze each depth position and then need several acquisitions in order to reconstruct the whole signal [START_REF] Bouma | Handbook of optical coherence tomography[END_REF][START_REF] Schmitt | Optical Coherence Tomography (OCT): A review[END_REF]. Signal recording could be quite long and to avoid this drawback some researchers have developed a similar approach in the frequency domain, named FD-OCT (Frequency Domain -Optical Coherence Tomography), where a spectrometer is set at the output of the interferometer to achieve the spectral analysis of the intercorrelation signal [START_REF] Fercher | Optical coherence tomography principles and applications[END_REF][START_REF] Leitbeg | Performance of Fourier domain vs. time domain optical coherence tomography[END_REF]; the signal is then reconstructed by a numerical Fourier Transform operation [START_REF] Leitbeg | Spectral measurement of absorption by spectroscopic frequency-domain optical coherence tomography[END_REF][START_REF] Park | Optical Frequency domain reflectometry based on realtime Fourier Transformation[END_REF]. Despite the fast recording, the difficulty of the FD-OCT remains the sampling for the numerical reconstruction of the signal on its whole dynamics range [START_REF] Dorrer | Spectral resolution and sampling issues in Fourier-Transform spectral interferometry[END_REF] and the calibration of the detector [START_REF] Dorrer | Influence of the calibration of the detector on spectral interferometry[END_REF].

The method proposed here to measure the contact lenses central thickness is also based on low coherence interferometry without needing to move any optical elements and provides directly the intercorrelation signal in a single acquisition [START_REF] Ben Houcine | Temporal analysis of optical complex structures: application to tomography through scattering media[END_REF][START_REF] Ben Houcine | Imaging through scattering medium using SISAM correlator[END_REF]. The measurement is straightforward without Fourier Transform operation. However, a numerical treatment could improve the results afterwards.

To demonstrate the ability of such an experiment, two low coherence light sources are used with the interferometer and the results with several contact lenses are compared. Applications of this work could also be considered for other types of samples.

The first part of this paper is devoted to the description of the used set-up and of the obtained signals; the second part examines how optical source influences the correlation signal and explains the calibration method; the last part gathers the experimental results with the two light sources (SLED and supercontinuum) for several samples (two soft lenses and one rigid lens).

Measurement principle

The light of a low coherence source (SLED or supercontinuum) is injected in a Mach-Zehnder interferometer, which is followed by a SISAM (Spectromètre Interférentiel à Sélection par l'Amplitude de Modulation) optical correlator (Fig. 1). The SISAM is constituted of two diffraction gratings, a beam-splitter, an imaging lens and a 2D-CCD detector. In the Mach-Zehnder interferometer, the reference arm contains a delay line that is adjusted once at the beginning and that keeps the same position during the acquisition; the sample is set in the measurement arm at the focal point of a microscope objective.

Low coherence sources

The light source used here is either a SLED emitting at the central wavelength λ 0 = 822.4 nm over a spectral bandwidth of ∆λ = 39 nm (full width at 1/e 2 ) connected to a monomode fiber or a source emitting a supercontinuum light over the visible spectrum up to the near infrared region (flat spectrum around λ 0 = 1064 nm of full width ∆λ = 1580 nm between the minima) generated in an air-silica Microstructured Optical Fiber (MOF) pumped by a picosecond microchip laser [START_REF] Tombelaine | Ultra wide band supercontinuum generation in air-silica holey fibers by SHG-induced modulation instabilities[END_REF].

The wave amplitude after collimation by the lens at the interferometer input is expressed as:

S c = S s r ( ) Γ ν ( ) (1)
S s r ( ) is the spatial distribution amplitude where r 2 = x 2 + y 2 is the radial component and Γ ν ( ) is the temporal frequencies distribution.

The beam is supposed to be spatially Gaussian for the SLED and with a first order Bessel distribution for the supercontinuum. Indeed, the fiber propagates only the fundamental mode for the wavelengths associated to the SLED and the MOF inside which is generated the supercontinuum propagates only the LP 11 mode in the visible range. Then, for each source, the spatial distribution is:

SLED: S s r ( ) = S 0 exp -r 2 a 0 2       a 0 ≈ 1mm Supercontinuum: S s r ( ) = S 0 J 1 r a 1       a 1 = 2mm (2)
a 0 and a 1 stand, respectively, for the beam radius of the fundamental mode and for the dimension of the LP 11 mode after propagation up to the collimating lens. The temporal frequencies spectrum Γ ν ( ) = TF γ t () ( ) of each light source is such as its distribution is Gaussian for the SLED and flat for the supercontinuum: (3)

SLED: Γ ν ( ) 2 = Γ 0 2 exp -4 ν -ν 0 ( ) 2 ∆ν 2           with ν 0 = 3.

Mach-Zehnder interferometer

In the reference arm, the wave propagates in air up to the first diffraction grating and remains nearly collimated. Its phase ϕ R ν ( ) is proportional to the traveling path l R :

ϕ R ν ( ) = 2πν c l R (4)
In the measurement arm, the wave propagates in air up to the 10x microscope objective, is focalized and reflected back by each interface of the object. Then, the different waves still travel in air up to the second diffraction grating. The phase ϕ i ν ( ) of each reflected wave is expressed as:

ϕ i ν ( ) = 2πν c l T + 2 n k e k k =1 i>1 i-1 ∑           (5)
where l T , n k , e k are respectively the total path in air, the refractive index and the thickness of the object slice k. The amplitude distributions S R and S i traveling up to each grating are then given by:

S R r,ν ( ) = α R S s r ( ) Γ ν ( ) exp -j 2πν c l R       S i r,ν ( ) = α T S s r ( ) Γ ν ( ) r i t k 2 k =1 i>1 i-1 ∏           exp -j 2πν c l T + 2 n k e k k =1 i>1 i-1 ∑                     (6)
The coefficients α R and α T correspond to the reflected and transmitted waves amplitudes by the different beam-splitters for each arm of the interferometer. r i is the reflection amplitude coefficient of the last interface i. For each slice k, the transmission amplitude coefficient is t k .

Assuming that the object is constituted of only one slice (2 interfaces), then the whole amplitude wave in the measurement arm just behind the second grating is:

S T r,ν ( ) = S 1 r,ν ( ) + S 2 r,ν ( ) = S i r,ν ( ) i=1 2 ∑ = α T S s r ( ) Γ ν ( ) r 1 exp -j 2πν c l T       + r 2 t 1 2 exp -j 2πν c l T + 2n 1 e 1 ( )             (7)
In the case of same media in front of and behind the object, then the reflection amplitude coefficients r 1 and r 2 are equal (r 1 =r 2 =r) leading to the reflection intensity coefficient R=rr* and to the transmission intensity coefficient T=1-R.

Each interface induces a pulse that propagates up to the second diffraction grating.

SISAM correlator

The fields of the different waves propagated in the Mach-Zehnder interferometer undergo a phase shift by the two SISAM gratings in an antisymmetrical way in each arm. The induced phase shifts are respectively ψ R r,ν ( ) and ψ T r,ν ( ) for the reference and the measurement arms. These waves are then added by the beam-splitter onto the CCD detector. The resulting amplitude S r,ν ( ) is the sum of the waves phase-shifted by the object and the gratings:

S r,ν ( ) = S R r,ν ( ) exp jψ R r,ν ( ) [ ] + S T r,ν ( ) exp jψ T r,ν ( ) [ ] (8)
The integral of the power spectral density C SS r,ν ( ) of the resulting wave S r,ν ( ) , on the whole source frequency bandwidth, yields to the light distribution E C r ( ) at the output of the SISAM:

E C r ( ) = C SS r,ν ( ) dν ν ∫ = S r,ν ( ) S * r,ν ( ) ν ∫ dν ( 9
)
C SS r,ν
( ) is the autocorrelation function of the resulting wave S r,ν ( ) :

C SS r,ν ( ) = S R r,ν ( ) 2 + S T r,ν ( ) 2 + S R r,ν ( ) S T * r,ν ( ) exp -j∆ψ ( ) + S R * r,ν ( ) S T r,ν ( ) exp j∆ψ ( ) (10)
The phase difference ∆ψ = ψ T -ψ R due to the diffraction gratings is developed at the first order around the central frequency ν 0 and is expressed as:

∆ψ = ψ T -ψ R = 2π ⌣ Q + Qν ( ) X with ⌣ Q = 2N 0 cos ′ θ 0 cos ′ θ p -′ θ 0 ( ) and Q = 2 -N 0 ν 0 cos ′ θ 0 cos ′ θ p -′ θ 0 ( ) + 1 c sin ′ θ p -′ θ 0 ( )       (11)
X is the horizontal coordinate (perpendicular to the grating lines) bound to the detector. The mathematical definition of X and calculation steps of Eq. ( 11) have been already developed [15]. The lens at the output of the SISAM images the two gratings planes onto the 2D-CCD camera with a magnification equal to 1.

N 0 = 150 l/mm is the lines number per millimeter of each grating, ′ θ 0 the diffraction angle for the source central wavelength λ 0 and ′ θ p the diffraction angle corresponding to the flat tint (beams are parallel and superposed) at the wavelength λ p . The incidence angle upon the gratings is θ 0 = 82.5°. In order to add the beams after the gratings, the beam-splitter is set at 45° from each diffracted beam at the wavelength λ p (flat tint) (Fig. 2). As a matter of fact, the SISAM plays the role of correlator between the measurement and reference fields by their direct temporal intercorrelation in real time without moving the delay line. The phase shift induced by the two diffraction gratings on each wave transverse section permits then to avoid the modulation of the reference optical path contrary to TD-OCT and does not need any numerical Fourier Transform operation, as it is the case for FD-OCT.

The light distribution on the detector is finally written as:

E c X ( ) = E 0 1+ K ′ K cos 4πν 0 n 1 e 1 c       F π∆ν 2n 1 e 1 c       +cos 4πν 0 c X sin ′ θ p -′ θ 0 ( ) + 2πν 0 ∆l c       F π∆ν ∆l c + QX             +T cos 4πν 0 c X sin ′ θ p -′ θ 0 ( ) + 2πν 0 ∆l + 2n 1 e 1 c       F π∆ν ∆l + 2n 1 e 1 c + QX                                                       (12)
with:

K = 2α R α T R α R 2 +α T 2 R 1+ T 2 ( ) ′ K = α T α R T R ∆l = l T -l R E 0 = S 0 2 Γ 0 2 ∆ν α R 2 +α T 2 R 1+ T 2 ( ) [ ] J 1 r a 1       π 2 exp - r 2 a 0 2              and F x ( ) = sin c x ( ) = sin x x continuum exp - x 2       2         SLED       
Equation ( 12) exhibits 3 terms:

• A continuous background E 0 1+ K ′ K cos 4πν 0 n 1 e 1 c       F π∆ν 2n 1 e 1 c             ; • A first vertical correlation peak centered at X 0 = - ∆l cQ of full width: ∆X = 2 ∆νQ
between the minima for the supercontinuum and ∆X = 2 π 2 ∆νQ at 1/e 2 for the SLED;

• A second vertical correlation peak centered at X 1 = - ∆l + 2n 1 e 1 cQ of same full width.

The two correlation peaks have nearly the same width depending on the source spectral width ∆ν and on the Q factor induced by the SISAM: the greater the source spectral bandwidth ∆ν and the factor Q are, the thinner the peaks are. The Q factor, when developed at the first order, varies in the same way as N 0 , θ 0 and (λ 0 ) -1 .

It can be deduced that the correlation peaks are thinner when the source spectral bandwidth ∆ν, the incidence angle θ 0 on the grating and the grating dispersion are increasing (N 0 high) and when the central wavelength λ 0 is decreasing. However, the light distribution at the set-up output has been calculated with an approximation of the phases induced by the SISAM gratings since the phase difference development of Eq. ( 11) is limited to the first order; the dispersion terms of higher orders lead to phase distortion. Furthermore, the microscope objective in front of the sample induces additional dispersion that contributes to correlation peaks broadening in particular for very broadband spectrum such as the supercontinuum source.

The peaks are centered at the group delays of each of the reflected waves by the object and are weighted by the SISAM scale factor Q that imposes then the measurement range and the resolution. Moreover these peaks are spatially modulated at 2ν 0 c sin ′ θ p -′ θ 0 ( ) frequency leading to a greater oscillations number in the peaks when the temporal central frequency ν 0 and the diffraction angles difference θ p '-θ 0 ' are growing. The peak location is chosen at the maximum of the oscillations envelop.

Correlation signals, calibration and limits of the set-up

Correlation signals, numerical treatment and calibration

As explained in the section 2, the signal captured by the 2D-CCD detector (Sony XC-EI50CE) does not only depend on the SISAM configuration but also on the selected optical source (SLED or supercontinuum). At first, to show how the source influences the correlation signal, a mirror is used as object and set at the focal plane of the microscope objective. The delay line is kept at a fixed position so that the two arms of the interferometer are nearly equal. Then, the 2D-CCD camera records a correlation signal for the SLED (Fig. 3 Each of the two signals exhibits, inside its envelope, only one vertical correlation peak due to the interferences between the reference pulse and the single reflected pulse by the mirror. The spatial envelop of the light distribution differs for the SLED and the supercontinuum (Eq. ( 12)): the SLED one is a Gaussian function and is maximum at the centre of the camera whereas the supercontinuum one varies as a first order Bessel function and reveals two sides lobes along the y-axis with a minimum at its centre.

In order to improve the correlation signal, a numerical treatment has been developed with Matlab in the following manner. The signals corresponding to the measurement and reference arms are also recorded separately and subtracted from the correlation signal in order to suppress the continuous background (Figs. 4(a) and4(d)). A region of interest of the subtracted signal is then selected and vertical Prewitt filter (method for edge detection) is applied to this region for extraction of the strong contrast areas (Figs. 4(b) and 4(e)). A sum over the columns and suppression of linear tendency part by part allows enhancing the signal. Then, each peak is automatically detected by search of the maxima inside the signal envelop (Figs. 4(c) and 4(f)) in the following way: all the values exceeding a threshold are taken into account. The threshold is chosen to be as three times the sliding standard deviation calculated with the values immediately before the considered point.

The peak maximum and the standard deviation expressed in grey level sum are 2300 and 60 for the SLED (Fig. 4(c)) whereas these values are 5900 and 50 for the supercontinuum (Fig. 4(f)). It leads to SNR equal to 16 dB for the SLED and 21 dB for the supercontinuum.

For each light source, the set-up is now calibrated by moving the delay line with the mirror used as object. For each position LR i of the delay line, the 2D-CCD camera records the correlation signal including the vertical correlation peak that stands at different areas on the image recorded by the CCD detector. The peak is localized (pixel number) as explained in the previous paragraph using a program developed with Matlab. Figure 5 shows the corresponding signals for three positions LR i of the delay line and for the two sources, leading in the following study (section 4), to the measurement of the central thickness of contact lenses. The peaks detection gives access to the relationship between the pixel number (corresponding to the peak position) and the delay line position (corresponding to the object position) as represented in Fig. 6(a) for the SLED and in Fig. 6(b) for the supercontinuum. These calibration curves lead to a conversion coefficient of 1.798 µm/pixel for the supercontinuum and of 1.924 µm/pixel for the SLED.

Resolution, accuracy and measurement range

The calibration of the set-up when using the SLED or the supercontinuum source enables us now to compare, in term of optical paths, the peaks width that induces the resolution of the measurement. These theoretical values of the peaks widths between two minima for the supercontinuum are deduced from Eqs. 11 and 12 and given by:

∆X = 2λ 0 2 c∆λQ = λ 0 2 ∆λ - N 0 λ 0 cos ′ θ 0 cos ′ θ p -′ θ 0 ( ) + sin ′ θ p -′ θ 0 ( )       -1 (13)
For the SLED, Eq. ( 13) has to be multiplied by the coefficient 2 π when the peak width is estimated at 1/e 2 . The numerical values of λ o and ∆λ are firstly depending on the two sources but also on the CCD detector which bandwidth can reduce the wavelength spectrum. In our case, the CCD spectral bandwidth is [300 -1000 nm] which includes the wavelength spectrum of the SLED but not that of the supercontinuum. Taking into account the spectral limitation of the CCD detector leads to the values of λ o = 650 nm and ∆λ = 700 nm for the supercontinuum whereas for the SLED, these values λ o = 822.4 nm and ∆λ = 39 nm are staying the same as those of the source. The wavelength values corresponding to the flat tint are λ p = 632.5 nm (alignment with an HeNe) for the SLED and λ p = λ o = 650 nm for the supercontinuum. Then, with the help of the above numerical values, the theoretical widths of the correlation peaks are calculated: ∆X = 59µm = 7 pixels (1 pixel = 8.6 µm on the detector) for the SLED and ∆X = 3µm = 0.35 pixel for the supercontinuum. Here, it can be noticed that, for the SLED, the correlation signal is well sampled, as the peak width is greater than two pixels. It is not the case for the supercontinuum, whose correlation signal retrieval could be critical as its width is smaller than one pixel. However, the sampling will not be a limitation, even for the particular case of the supercontinuum, because actually its correlation peak broadens as seen in the following paragraph.

The peak widths theoretical values are now compared with the experimental ones. The full width at 1/e 2 of the correlation peak for the SLED is measured to be 8 pixels (Fig. 4(c)) corresponding to an optical path difference of ∆δ = 15 µm (see calibration in Fig. 6), whereas for the supercontinuum, the peak full width is of 44 pixels or ∆δ = 79 µm (Fig. 4(f)). Thus, the experimental peak width value is close to the theoretical one for the SLED contrary to the values for the supercontinuum. Indeed, when the optical frequency bandwidth becomes very large (case of the supercontinuum), the correlation peak undergoes broadening due to dispersion effect. This is not taken into account in Eq. 13 because the dispersion terms of high orders induced by the gratings, that contribute to phase distortion, have been omitted as well as the dispersion induced by the microscope objective. As a result, the experimental correlation peak for the SLED is clearly getting thinner than that of the supercontinuum.

These experimental values of the correlation peak widths do not affect the accuracy and then the measurements results even in the case of the supercontinuum because the peak is always detected by the same criteria (search of the maximum of the oscillations envelop by the Matlab program). But the peak widths lead to the determination of the set-up resolution as established in the following paragraph.

a) Resolution

Let us consider now the case of different optical paths to be determined in the measurement arm, several intercorrelation peaks will then appear. To differentiate among them, the peaks of spatial width ∆X must be well separated in order to be detected by the camera. Limitation to resolve the peaks is given at first sight by the Rayleigh criteria: the peaks separation must be greater than their half width (at 1/e 2 for the SLED or between the minima for the supercontinuum). This condition implies that the path difference (δ = n 1 e 1 ) is large enough and greater than δ min = ∆δ/2 = 8 µm for the SLED and δ min = ∆δ/2 = 40 µm for the supercontinuum. The resolution of the set-up is then better for the SLED than for the supercontinuum.

b) Measurement range

Another point to be considered in the set-up is the maximum optical path difference that can be measured. The measurement range gives this upper limit of the optical path difference that should not exceed the maximum value corresponding to the camera field of 768 pixels. Indeed, in our experiment, the horizontal width of the beam is not a limiting factor to the measurement range. Then the optical path difference must not be greater than δ max = 1.48 mm (768 pixels) for the SLED and δ max = 1.4 mm (768 pixels) for the supercontinuum (see the corresponding conversion coefficients subsection 3.1). The measurement range is nearly the same for both sources.

c) Accuracy

The last limit presented here is the measurement accuracy that is fixed by the ability of the Matlab program to localize one correlation peak. The peak width does not influence the accuracy of the measurement much whereas the signal to noise ratio (SNR) is a limiting factor. Indeed, the position of the correlation peak is determined with a better accuracy if SNR is as high as possible. For the supercontinuum, the signal to noise ratio could be enhanced by suppression of the area in between the two lobes of the envelop signal (Fig. 4(e)). This central area does not bring any information about the signal and reduces the signal to noise ratio when the sum is achieved on the signal columns.

To quantify the set-up accuracy, a set of ten correlation signals has been recorded under the same experimental conditions. The numerical process is applied to each of the ten signals in order to compare the position of the correlation peak. At worst, the maximum of the correlation peak is localized at one pixel around + 1 pixel. Thus, the set-up accuracy is considered to be of 3 pixels corresponding to an optical path of 5 µm for the supercontinuum and 6 µm for the SLED (see the corresponding conversion coefficients subsection 3.1). In the section 4, the accuracy is considered to be the same (6 µm) for the SLED and the supercontinuum.

The limits of the set-up being known, measurements with different samples can be now realized in order to validate the set-up.

Fig. 1 .

 1 Fig. 1. Set-up.

646 10 14 ∆ν 1 / e 2

 1412 Hz

Fig. 2 .

 2 Fig. 2. Beams addition.

  (a)) or the supercontinuum (Fig. 3(b)) light sources.

Fig. 3 .

 3 Fig. 3. Correlation images with (a) the SLED source and (b) the supercontinuum.

Fig. 4 .

 4 Fig. 4. Determination of the correlation peaks. (a) Subtracted image with the SLED. (b) Vertical filter and region of interest with the SLED. (c) Columns sum with the SLED. (d) Subtracted image with the supercontinuum. (e) Vertical filter and region of interest with the supercontinuum. (f) Columns sum with the supercontinuum.

Fig. 5 .

 5 Fig. 5. Positions of the correlation peaks for calibration. (a) With the SLED. (b) With the supercontinuum.

Fig. 6

 6 Fig. 6 Calibration curves for the two sources (a) SLED. (b) supercontinuum.

Acknowledgments

The supercontinuum source was provided by Leukos in the frame of a collaboration with Xlim institute from Limoges (France) and the authors would like to express their thanks to V. Couderc, G. Huss et P. Leproux. The authors are also grateful to C. Fallet, student at the Institut d'Optique, for the elaboration of the computing program.

Experimental results

Three contact lenses are tested in air: two soft lenses and a rigid one. Soft contact lenses refractive index and center thickness significantly depend on their water content. So for all the experiments, the lenses are taken out of their solution and immediately set into a box with their apex in front of the objective. The measurement takes a few seconds so that the lenses have no time to suffer from dehydration and are considered as wet.

Soft contact lens (-3.75D)

The first measured sample is a soft contact lens of optical power -3.75D made in Balafilcon A material. The mean values of the central thickness and refractive index given by the manufacturer are roughly e 1 = 90 µm and n 1 = 1.4.

As explained in subsection 3.1, the CCD detector records three images (one for the correlation, one for the measurement signal and one for the reference signal). The interferences signal is extracted and shows two correlation peaks issued from the two contact lens interfaces.

Figure 7(a) shows the correlation signal after subtraction and numerical treatment when using the SLED source. It permits to obtain the following results: the positions of the two peaks induced by each faces of the lens are localized on the pixels which numbers are X 0 = 318 and X 1 = 387. The optical path difference δ = n 1 e 1 is corresponding to 69 pixels leading to the lens central thickness. So, δ = 131 + 6 µm and e 1 = 131/1.4 = 95 + 4 µm.

For the supercontinuum source (Fig. 7(b)), the two correlation peaks, corresponding to the two lens interfaces, are separated by 72 pixels meaning that e 1 = 93 + 4 µm. For both of the sources (SLED and supercontinuum), the experimental values of the central lens thickness agree with the one given by the manufacturer (roughly 90 µm).

Soft contact lens (-4.75D)

The second sample is a soft contact lens fabricated in the same material (Balafilcon A) as the first lens but with an optical power of -4.75D. Measurements are realized in the same way as for the first sample. The mean values of the central thickness and refractive index are also roughly e 1 = 90 µm and n 1 = 1.4.

Figure 8(a) gives the numerical treatment of the correlation signal when using the SLED source and leads to the following results: the two correlation peaks induced by each of the lens interfaces are localized on the pixels which numbers are X 0 = 312 and X 1 = 378 and the central thickness is e 1 = 91 + 4 µm. It has to be noticed that the small peak (around the pixel n°110) detected by the program is not significant and must not be taken into account. For the measurement with the supercontinuum source (Fig. 8(b)), the gap between the two correlation peaks corresponding to the lens interfaces is of 78 pixels leading to e 1 = 101 + 4 µm and is greater than the expected value (nearly 90 µm) whereas the measurement with the SLED source agrees with the value given by the manufacturer. This could be explained by the fact that, in the case of the supercontinuum, the reference beam is not strictly superimposed on the measurement one in the vertical direction.

Rigid contact lens (-4.5D)

The last studied sample is a rigid contact lens of optical power -4.5D and fabricated in SiloxanyIMA-fluoroMA-MMA material. The mean values of its central thickness and refractive index are roughly e 1 = 140 µm and n 1 = 1.48.

Figure 9(a) shows the signal when using the SLED source and the two correlation peaks corresponding to each lens interfaces are localized on the pixels which numbers are X 0 = 234 and X 1 = 339 corresponding to the lens central thickness of e 1 = 137 + 4 µm. For the measurement with the supercontinuum source (Fig. 9(b)), the gap between the two correlation peaks is of 114 pixels and leads to e 1 = 138 + 4 µm in agreement with the value given by the manufacturer as the experimental value obtained with the SLED.

Table 1 gathers the different experimental values of the contact lens central thicknesses with each of the two optical sources. The obtained results show that the experimental values of contact lens central thicknesses are those expected. The measurement accuracy is nearly the same with the SLED source or the supercontinuum source (<6 µm).

The temporal frequency distribution of both sources does not much affect these measurements despite the dispersion phenomenon that is more important for the supercontinuum. However, measurement of thinner samples could be limited by the resolution of the system.

The spatial distribution plays almost no role in the measurements except for the choice of the region of interest in the numerical treatment. However this choice is not very critical if the signal to noise ratio is good, that is to say, if the contrast of the correlation signal is sufficiently high.

Conclusion

In this work, the ability to measure contact lenses central thickness with a good accuracy is demonstrated. Two soft contact lenses and one rigid contact lens of close optical power have been used as samples with two different optical sources. The resolution of the measurements is more efficient for the SLED configuration than for the supercontinuum but the results are nearly the same for each of the optical sources in terms of accuracy (around few µm). However, the resolution of the measurement can be enhanced in the case of the supercontinuum by compensating the dispersion induced by the microscope objective. This will be done in future work by inserting a similar objective in the reference arm. The numerical treatment itself is well adapted as, for the two sources configurations (SLED or Supercontinuum), the correlation peaks are well contrasted. Although, in the particular case of the supercontinuum, the numerical treatment could even take more advantage of a better choice of the region of interest by suppression of the beam central part where the light intensity is minimum.

In the final analysis, the indisputable advantage of this measurement technique is the very fast correlation signal recording since there is neither wavelength tuning nor delay line modulation.