11 research outputs found
DĂ©tection de la prĂ©sence humaine et Ă©valuation de la qualitĂ© du sommeil en Ă©tablissement dâhĂ©bergement pour personnes ĂągĂ©es dĂ©pendantes (EHPAD)
In France, in Europe and worldwide, the aging population is a reality. Some of these elderly people lose their autonomy as they are no longer able to manage alone the tasks of daily life. The societal issue is therefore to ensure a level of well-being and safety of these persons, consistent with changes in living standards, customs and modern habits. The research areas related to the problems of elderly people at home are showing great dynamism, while the nursing home, which remains the solution for cases of high dependence, is somewhat neglected. Nevertheless, staff shortages combined with rising costs and residentsâ demands offer an opportunity for innovative ICT-based solutions. The work presented here was performed, in the context of a CIFRE doctoral thesis, within the Legrand research team and at the physics and electronics department of Mines-Telecom SudParis at Evry. The subject and project aim was twofold: firstly, designing a new sensor which will be incorporated in the electrical installation of the patientâs living space, and secondly, a multi-sensor merger to monitor the activity of the resident in order to enable real-time reporting of situations requiring the caregiverâs intervention or to detect slow drifts whose interpretation will be the responsibility of the medical staff. The work carried out for the purpose of this thesis has been included partially in the FUI 14 project whose propose is precisely the âsupervision of residents in the nursing homeâ. The present paper is structured in such a way as to introduce the background of the work and the approach taken to perform it. The context and needs identified for monitoring of nursing home residents are also introduced. We begin by describing existing monitoring systems and the technical methods used to detect emergency situations. We end the first part (chapter 1) of this paper by specifying the major problem encountered when testing existing monitoring systems based on ambient sensors: namely how to detect the presence of an immobile and silent person in the room. Using an existing pyro-electric infrared sensors network installation in a nursing home, the next section proposes an original solution for detecting human presence in a room and also for differentiating between the presence of one and the presence of more than one person (chapter 2). Chapter 3 presents a new sensor integrated into the electrical installation of the patientâs living space. Here, we introduce a thermopile based thermal sensor in order to detect the presence of a person in his/her living space. In this work we restrict the use of this sensor to detecting the presence of the person in bed (chapter 4). The estimation of sleep quality which represents the original dimension of our work is presented in chapter 5. Differentiation between different phases of sleep is based on unsupervised classification approaches. Our project opens up encouraging prospects for the use of this type of sensor for relatively fine characterization of different kinds of sleepEn France, en Europe et dans le monde entier, le vieillissement de la population est une rĂ©alitĂ©. Une partie de cette population ĂągĂ©e est dite dĂ©pendante car elle nâest plus en mesure dâassumer seule les tĂąches de la vie quotidienne. Lâenjeu sociĂ©tal est alors de garantir un niveau de bien-ĂȘtre et de sĂ©curitĂ© Ă ces personnes, compatible avec lâĂ©volution du niveau de vie et des usages et habitudes âmodernesâ. TrĂšs logiquement, les domaines de recherche liĂ©s Ă la problĂ©matique des personnes ĂągĂ©es Ă domicile font preuve dâun grand dynamisme, alors que la maison de retraite, qui reste la solution pour la grande dĂ©pendance, a Ă©tĂ© un peu dĂ©laissĂ©e. NĂ©anmoins, la pĂ©nurie de personnel conjuguĂ©e Ă lâaugmentation des coĂ»ts et des exigences des rĂ©sidents offre une opportunitĂ© Ă des solutions innovantes basĂ©es sur les TIC. Les travaux de cette thĂšse de doctorat sous convention CIFRE se sont dĂ©roulĂ©s dans ce contexte au sein de lâĂ©quipe de recherche de Legrand et du dĂ©partement dâElectronique et Physique de TĂ©lĂ©com SudParis Ă Evry. Le sujet concerne la conception dâun nouveau capteur (non-portĂ©) intĂ©grant lâinstallation Ă©lectrique du lieu de vie du patient ainsi que la fusion avec dâautres capteurs de lâinfrastructure afin de suivre lâactivitĂ© du rĂ©sident et, le cas Ă©chĂ©ant, soit signaler en temps rĂ©el des situations nĂ©cessitant le recours dâun aidant, soit identifier des dĂ©rives lentes dont lâinterprĂ©tation sera du ressort du personnel mĂ©dical. Les travaux de la thĂšse ont Ă©tĂ© en partie intĂ©grĂ©s au projet FUI14 « E-monitorâĂąge » dont lâobjectif est prĂ©cisĂ©ment la « supervision » des rĂ©sidents. Ce mĂ©moire est structurĂ© de maniĂšre Ă prĂ©senter lâhistorique de ces travaux et la dĂ©marche opĂ©rĂ©e pour leur rĂ©alisation. Nous introduisons le contexte et les besoins identifiĂ©s pour le suivi des personnes ĂągĂ©es dans les maisons de retraites. Nous faisons un point sur les systĂšmes de supervision/monitoring existants et nous prĂ©sentons les mĂ©thodes et les techniques de dĂ©tection de situations dâurgence. Nous terminons cette partie du mĂ©moire (chapitre 1) par la spĂ©cification du problĂšme majeur rencontrĂ© par ces systĂšmes de supervision qui est celui de la dĂ©tection de prĂ©sence dâune personne. En sâappuyant sur la technologie des capteurs pyro-Ă©lectriques, la partie suivante propose une solution originale de traitement de signal pour la dĂ©tection dâune prĂ©sence humaine dans une chambre voire la dĂ©tection de prĂ©sence de plusieurs personnes Ă la fois (chapitre 2). Le chapitre 3 introduit ensuite un capteur thermique Ă base de thermopiles afin de dĂ©tecter la prĂ©sence dâune personne dans son lit, ce que ne permet pas la technologie pyro-Ă©lectrique qui ne dĂ©tecte pas un corps chaud immobile. Dans cette partie nous limitons lâutilisation de ce capteur Ă la dĂ©tection de la prĂ©sence de la personne dans son lit (chapitre 4) voire Ă lâestimation de la qualitĂ© de son sommeil qui constitue dâune part lâoriginalitĂ© de nos travaux sâappuyant sur des approches de classification non-supervisĂ©e, et qui ouvre des perspectives encourageantes quant Ă lâutilisation de ce capteur pour caractĂ©riser relativement finement le type de sommeil dâautre part (chapitre 5
Corpus multimodal enregistré par des personnes ùgées à domicile et l'élaboration d'un IHM adapté
International audienceL'Ă©valuation de la fragilitĂ© physique chez les personnes ĂągĂ©es est primordiale pour Ă©valuer leur capacitĂ© Ă vivre en autonomie. Le systĂšme de monitoring prĂ©sentĂ© dans ce papier s'appuie sur cinq types de capteurs. Ces capteurs appartiennent Ă deux classes d'appareillage : installation et mobiles . Parmi ces capteurs: 1) capteurs infrarouges supervise les diffĂ©rents dĂ©placements du sujet, 2) la balance estime l'Ă©quilibre, 3)le dynamomĂštre mesure la prĂ©hensionpalmaire 4) les semelles connectĂ©es mesure de nombre de pas 5) le radar de marche mesure la distance parcourue Ă domicile et la vitesse de marcheĂ©galement. Le logiciel est dĂ©veloppĂ© sous Android pour rĂ©cupĂ©rer, envoyer et stocker des donnĂ©es sur un serveur distant. L'acceptabilitĂ© est Ă©valuĂ©e en premiĂšre phase en revanche l'acceptation est considĂ©rĂ©e en deuxiĂšme phase. Au cours de la premiĂšre phase, cinq personnes ĂągĂ©es ont Ă©tĂ© sĂ©lectionnĂ©es puis invitĂ©es Ă passer une journĂ©e au Living Lab afin dâadapter et amĂ©liorer le systĂšme notamment l'IHM. La deuxiĂšme phase consiste Ă installer le systĂšme Ă domicile. Parmi les rĂ©sultats de cette seconde phase figure la base de donnĂ©es multimodale. Les mĂ©decins ont accĂšs Ă la base de donnĂ©es via un site Web afin dâinterprĂ©ter les effets du traitement quâils ont prescri
Detecting human presence and evaluation of sleep quality in accomodation establishment for the dependent elderly (nursing homes)
En France, en Europe et dans le monde entier, le vieillissement de la population est une rĂ©alitĂ©. Une partie de cette population ĂągĂ©e est dite dĂ©pendante car elle nâest plus en mesure dâassumer seule les tĂąches de la vie quotidienne. Lâenjeu sociĂ©tal est alors de garantir un niveau de bien-ĂȘtre et de sĂ©curitĂ© Ă ces personnes, compatible avec lâĂ©volution du niveau de vie et des usages et habitudes âmodernesâ. TrĂšs logiquement, les domaines de recherche liĂ©s Ă la problĂ©matique des personnes ĂągĂ©es Ă domicile font preuve dâun grand dynamisme, alors que la maison de retraite, qui reste la solution pour la grande dĂ©pendance, a Ă©tĂ© un peu dĂ©laissĂ©e. NĂ©anmoins, la pĂ©nurie de personnel conjuguĂ©e Ă lâaugmentation des coĂ»ts et des exigences des rĂ©sidents offre une opportunitĂ© Ă des solutions innovantes basĂ©es sur les TIC. Les travaux de cette thĂšse de doctorat sous convention CIFRE se sont dĂ©roulĂ©s dans ce contexte au sein de lâĂ©quipe de recherche de Legrand et du dĂ©partement dâElectronique et Physique de TĂ©lĂ©com SudParis Ă Evry. Le sujet concerne la conception dâun nouveau capteur (non-portĂ©) intĂ©grant lâinstallation Ă©lectrique du lieu de vie du patient ainsi que la fusion avec dâautres capteurs de lâinfrastructure afin de suivre lâactivitĂ© du rĂ©sident et, le cas Ă©chĂ©ant, soit signaler en temps rĂ©el des situations nĂ©cessitant le recours dâun aidant, soit identifier des dĂ©rives lentes dont lâinterprĂ©tation sera du ressort du personnel mĂ©dical. Les travaux de la thĂšse ont Ă©tĂ© en partie intĂ©grĂ©s au projet FUI14 « E-monitorâĂąge » dont lâobjectif est prĂ©cisĂ©ment la « supervision » des rĂ©sidents. Ce mĂ©moire est structurĂ© de maniĂšre Ă prĂ©senter lâhistorique de ces travaux et la dĂ©marche opĂ©rĂ©e pour leur rĂ©alisation. Nous introduisons le contexte et les besoins identifiĂ©s pour le suivi des personnes ĂągĂ©es dans les maisons de retraites. Nous faisons un point sur les systĂšmes de supervision/monitoring existants et nous prĂ©sentons les mĂ©thodes et les techniques de dĂ©tection de situations dâurgence. Nous terminons cette partie du mĂ©moire (chapitre 1) par la spĂ©cification du problĂšme majeur rencontrĂ© par ces systĂšmes de supervision qui est celui de la dĂ©tection de prĂ©sence dâune personne. En sâappuyant sur la technologie des capteurs pyro-Ă©lectriques, la partie suivante propose une solution originale de traitement de signal pour la dĂ©tection dâune prĂ©sence humaine dans une chambre voire la dĂ©tection de prĂ©sence de plusieurs personnes Ă la fois (chapitre 2). Le chapitre 3 introduit ensuite un capteur thermique Ă base de thermopiles afin de dĂ©tecter la prĂ©sence dâune personne dans son lit, ce que ne permet pas la technologie pyro-Ă©lectrique qui ne dĂ©tecte pas un corps chaud immobile. Dans cette partie nous limitons lâutilisation de ce capteur Ă la dĂ©tection de la prĂ©sence de la personne dans son lit (chapitre 4) voire Ă lâestimation de la qualitĂ© de son sommeil qui constitue dâune part lâoriginalitĂ© de nos travaux sâappuyant sur des approches de classification non-supervisĂ©e, et qui ouvre des perspectives encourageantes quant Ă lâutilisation de ce capteur pour caractĂ©riser relativement finement le type de sommeil dâautre part (chapitre 5)In France, in Europe and worldwide, the aging population is a reality. Some of these elderly people lose their autonomy as they are no longer able to manage alone the tasks of daily life. The societal issue is therefore to ensure a level of well-being and safety of these persons, consistent with changes in living standards, customs and modern habits. The research areas related to the problems of elderly people at home are showing great dynamism, while the nursing home, which remains the solution for cases of high dependence, is somewhat neglected. Nevertheless, staff shortages combined with rising costs and residentsâ demands offer an opportunity for innovative ICT-based solutions. The work presented here was performed, in the context of a CIFRE doctoral thesis, within the Legrand research team and at the physics and electronics department of Mines-Telecom SudParis at Evry. The subject and project aim was twofold: firstly, designing a new sensor which will be incorporated in the electrical installation of the patientâs living space, and secondly, a multi-sensor merger to monitor the activity of the resident in order to enable real-time reporting of situations requiring the caregiverâs intervention or to detect slow drifts whose interpretation will be the responsibility of the medical staff. The work carried out for the purpose of this thesis has been included partially in the FUI 14 project whose propose is precisely the âsupervision of residents in the nursing homeâ. The present paper is structured in such a way as to introduce the background of the work and the approach taken to perform it. The context and needs identified for monitoring of nursing home residents are also introduced. We begin by describing existing monitoring systems and the technical methods used to detect emergency situations. We end the first part (chapter 1) of this paper by specifying the major problem encountered when testing existing monitoring systems based on ambient sensors: namely how to detect the presence of an immobile and silent person in the room. Using an existing pyro-electric infrared sensors network installation in a nursing home, the next section proposes an original solution for detecting human presence in a room and also for differentiating between the presence of one and the presence of more than one person (chapter 2). Chapter 3 presents a new sensor integrated into the electrical installation of the patientâs living space. Here, we introduce a thermopile based thermal sensor in order to detect the presence of a person in his/her living space. In this work we restrict the use of this sensor to detecting the presence of the person in bed (chapter 4). The estimation of sleep quality which represents the original dimension of our work is presented in chapter 5. Differentiation between different phases of sleep is based on unsupervised classification approaches. Our project opens up encouraging prospects for the use of this type of sensor for relatively fine characterization of different kinds of slee
Multisensor data fusion algorithm for sleep quality estimation using multiple measurements
International audienceObjectives/Introduction: Sleep monitoring represents an interest field in medical word. We spend a part of our life in bed, then an analyzing of our sleep represents a part of our medical consultation. Therefore, the supervision of the evolution of our sleep quality is primordial thing. A lot of systems have been implemented, tested and commercialized. Each system has a specified properties and characteristics. In our living lab, We study the correlation between the sleep quality and balance stability measured at wake up. In this study we use the withing system which is based on movement counted when the person was in the bed at night in order to measure sleep quality and for the balance stability is measured by scale which uses the movement counted when the person is on the scale. The balance stability is used to enhance the sleep quality score therefore the data fusion algorithms are tested on recorded database. The database is recorded by students in internship and phd students. In this work, the data base is described, the correlation study is presented and the fusion data algorithms are detailed. Estimation methods of the weight for each modality (scale and withing system) are prospected
Suitable segmentation method of a thermal signal representing sleep movement in the bed
International audienceSleep quality and quantity is an important factor in the prevention of fraitly and serious deseases such as depression and diabetes. Therefore the devices that allow sleep monitoring are studied for well being. Body movement during sleep can be related to particular desesases such a sleep apnea and restless legs syndrome. In this paper we present a study on movements detection during sleep and the estimation of inactivity duration. Contactless thermal sensor (thermopile) is use to detect thermal variation caused by body movement in bed. A new adaptive segmentation method including symbolic representation (SAX) is introduced and implemented to compare identified sleep phases by our system to a reference hipnogram. The proposed sensor and his processing algorithm has been evaluated on 11 subjects. This evaluation shows good correlation between automatic segmentation and the hypnogram reference labels
Thermal-Signature-Based Sleep Analysis Sensor
This paper addresses the development of a new technique in the sleep analysis domain. Sleep is defined as a periodic physiological state during which vigilance is suspended and reactivity to external stimulations diminished. We sleep on average between six and nine hours per night and our sleep is composed of four to six cycles of about 90 min each. Each of these cycles is composed of a succession of several stages of sleep that vary in depth. Analysis of sleep is usually done via polysomnography. This examination consists of recording, among other things, electrical cerebral activity by electroencephalography (EEG), ocular movements by electrooculography (EOG), and chin muscle tone by electromyography (EMG). Recordings are made mostly in a hospital, more specifically in a service for monitoring the pathologies related to sleep. The readings are then interpreted manually by an expert to generate a hypnogram, a curve showing the succession of sleep stages during the night in 30s epochs. The proposed method is based on the follow-up of the thermal signature that makes it possible to classify the activity into three classes: âawakening,â âcalm sleep,â and ârestless sleepâ. The contribution of this non-invasive method is part of the screening of sleep disorders, to be validated by a more complete analysis of the sleep. The measure provided by this new system, based on temperature monitoring (patient and ambient), aims to be integrated into the tele-medicine platform developed within the framework of the Smart-EEG project by the SYELâSYstĂšmes ELectroniques team. Analysis of the data collected during the first surveys carried out with this method showed a correlation between thermal signature and activity during sleep. The advantage of this method lies in its simplicity and the possibility of carrying out measurements of activity during sleep and without direct contact with the patient at home or hospitals
Multimodal Corpus Recorded by Elderly People at home with elaboration of an adapted IHM
International audienceAssessment of physical frailty in community dwelling older people is essential to evaluate their capacity to live independently. A solution in context of digitized healthcare in silver technology is the home monitoring of the evolution of their state of health based on physical fragility assessment. The monitoring system used in our work is based on five types of sensors: 1) infrared sensors, 2) balance quality system, 3) dynamometer, 4) insoles in shoes, 5) radar system. A software was developped on android OS in order to collect, send and store data onto a medical server. Acceptability and acceptance of these sensors were studied in two phases. First, a group of older adult were invited to co-participate and familiarize the IHM by using the plateform in our LivingLab Activageing. Second phase consists of sensors installation at home. This latter affords a a multimodal database that can infer information such as the health status of the end users. This work was included in a longitudinal study in order to provide health-care professional indicators to assess the effect of their recovery actions which were prescribed for older adults recruited
Thermal signal analysis in smart home environment for detecting a human presence
International audienceSmart Home environments have become an important research topic in recent years. This paper deals with human presence detection using an ubiquitous sensor. Various solutions and supervision systems require to extract information regarding people present in the monitored environment. So both academic and industrial labs are interesting by this subject. More so than most other object-detection and sensing tasks, human-sensing is a challenging endeavor for a variety of reasons. To detect the person entering in a supervised environment several technical solutions based on large physical phenomena are available. Each researchers team use specific sensors with adapted techniques. Sensors choice is linked to supervision system ai
Design and first evaluation of a sleep characterization monitoring system using remote contactless sensor
International audienceThis paper presents the design and a first evaluation of a new monitoring system based on contactless sensors to estimate sleep quality. This sensor produces thermal signals which have been used, at first, to detect a human presence in the bed and then to estimate sleep quality. To distinguish between different sleep phases, we have used methods of signal processing in order to extract the necessary features for learning an adapted statistical model. The existing monitoring systems use sensors attached to the bed or worn by the person. We propose in this paper a system based on a passive thermal sensor which has the advantage of being fixed on the wall, thus it is easier to use and more reliable. We explain different signal processing steps and describe sleep stage recognition algorithms. We propose an adaptation of the SAX method for the thermal signal. Finally, we evaluate our system in comparison with a polysomnographic recording system in the Hospital (CHU) of Limoges
Multimodal localization in the context of a medical telemonitoring system
International audienceThis paper addresses a localization system which is based on a combination of information from two modalities: a Smart Home Person Tracking (SHPT) composed of infrared sensors and an Audio Person Tracking (APT) which uses microphones able to estimate azimuth of acoustic sources. This combination improves precision of localization compared to a standalone or separated module. The localization software facilitates the integration of both SHPT and APT systems, to display the position in real time, to record data and detect some distress situations (some kind of fall). Results on implementation show good adaptation for Smart Home environments and a robust detectio