Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Development of a human fall detection system based on depth maps

Assistive care related products are increasingly in demand with the recent developments in health sector associated technologies. There are several studies concerned in improving and eliminating barriers in providing quality health care services to all people, especially elderly who live alone and those who cannot move from their home for various reasons such as disable, overweight. Among them, human fall detection systems play an important role in our daily life, because fall is the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. The three basic approaches used to develop human fall detection systems include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. Thus, this study proposes a non-invasive human fall detection system based on the height, velocity, statistical analysis, fall risk factors and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information after considering the fall risk level of the user. Acceleration and activity detection are also employed if velocity and height fail to classify the activity. Finally position of the subject is identified for fall confirmation or statistical analysis is conducted to verify the fall event. From the experimental results, the proposed system was able to achieve an average accuracy of 98.3% with sensitivity of 100% and specificity of 97.7%. The proposed system accurately distinguished all the fall events from other activities of daily life

An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

Automatic fall detection is one of the most promising applications of wearables in the field of mobile health. The characterization of the effectiveness of wearable fall detectors is hampered by the inherent difficulty of testing these devices with real-world falls. In fact, practically all the proposals in the literature assess the detection algorithms with ‘scripted’ falls that are simulated in a controlled laboratory environment by a group of volunteers (normally young and healthy participants). Aiming at appraising the adequacy of this method, this work systematically compares the statistical characteristics of the acceleration signals from two databases with real falls and those computed from the simulated falls provided by 18 well-known repositories commonly employed by the related works. The results show noteworthy differences between the dynamics of emulated and real-life falls, which undermines the testing procedures followed to date and forces to rethink the strategies for evaluating wearable fall detectors.Funding for open access charge: Universidad de Málaga / CBUA. This research was funded by FEDER Funds (under grant UMA18-FEDERJA-022), Andalusian Regional Government (-Junta de Andalucía- grant PAIDI P18-RT-1652) and Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech

Intelligent fall detection system for eldercare

Fall among elders is a main reason to cause accidental death among the population over the age 65 in United States. The fall detection methods have been brought into scene by implemented on different fall monitoring devices. For the advantages in privacy protection and non-invasive, independent of light, I design the fall detection system based on Doppler radar sensor. This dissertation explores different Doppler radar sensor configurations and positioning in both of the lab and real senior home environment, signal processing and machine learning algorithms. Firstly, I design the system based on the data collected with three configurations: two floor radars, one ceiling and one wall radars, one ceiling and one floor radars in lab. The performance of the sensor positioning and features are evaluated with classifiers: support vector machine, nearest neighbor, naïve Bayes, hidden Markov model. In the real senior home, I investigate the system by evaluating the detection variances caused by training dataset due to the variable subjects and environment settings. Moreover, I adjust the automatic fall detection system for the actual retired community apartment. I examine different features: Mel-frequency cepstral coefficients (MFCCs), local binary patterns (LBP) and the combined version of features with RELIEF algorithm. I also improve the detection performance with both pre-screener and features selection. I fuse the radar fall detection system with motion sensors. I develop a standalone fall detection system and generate a result to display on a designed webpage

Adding intelligence to a floor based array personnel detector

According to the World Health Organization-WHO, a fall is defined, as "inadvertently coming to rest on the ground, floor or other lower level, excluding intentional change in position to rest in furniture, wall or other objects". It is known that a senior who falls is at risk for serious injury and after necessary spiraling down eventually to death. Researchers concern is to develop new technology or enhance existing one to detect falls and reduce the consequences of a fall. We enhanced smart carpet, which is a floor based personnel detector system, to detect falls using a faster but low cost processor. Our new hardware front end reads from 128 sensors (the sensors output a voltage due to a person walking or falling on the carpet). The processor is Jetson TK1, which provides more computing power than before. We generated a dataset with volunteers who walked and fell to test our algorithms. Data Obtained allowed examining data frames read from the data acquisition system. We used different algorithms and techniques, and varied the windows size of number of frames (WS>=1) and threshold (TH). We found that at (WS=8), and threshold (TH=8) using connected component labeling algorithm (CCL) produced a fall sensitivity of 87.9%. We then used the dataset obtained from applying a set of fall detection algorithms and the video recorded for the fall patterns experiments to train a set of classifiers using multiple test options using the Weka framework. We found that the widow size (WS=8) at a threshold (TH=8) using connected component algorithm generated attribute contributed to the fall sensitivity. We measured the performance of each testing options. The best feature was again the size of the connected component with WS=8, with classification accuracy of 96.94%. Other algorithms attributes did not contribute significantly to the detection of the fall

Deriving information from spatial sampling floor-based personnel detection system

Field of study: Electrical & computer engineering.Dr. Harry W. Tyrer, Thesis Supervisor.Includes vita."May 2017."Research has shown and identified a clear link between human gait characteristics and different medical conditions. Therefore, a change in certain gait parameters may be predictive of future falls and adverse events in older adults such as physical functional decline and fall risks. We describe a system that is unobtrusive and continuously monitors the gait during daily activities of elderly people. The early assessment of gait decline will benefit the senior by providing an indication of the risk of falls. We developed a low cost floor-based personnel detection system; we call a smart carpet, which consists of a sensor pad placed under a carpet; the electronics reads walking activity. The smart carpet systems is used as a component of an automated health monitoring system, which helps enable independent living for elderly people and provide a practical environment that improves quality of life, reduces healthcare costs and promotes independence. In this dissertation, we extended the functionalities of the smart carpet to improve its ability to detect falls, estimate gait parameters and compared it to GAITRite system. We counted number of people walking on the carpet in order to distinguish the plurality of people from fall event. Additionally we studied the characteristics and the behavior of the sensor's scavenged signal. Results showed that our system detects falls, using computational intelligence techniques, with 96.2% accuracy and 81% sensitivity and 97.8% specificity. The system reliably estimates the gait parameters; walking speed, stride length and stride time with percentage errors of 1.43%, -4.32%, and -5.73% respectively. Our system can count the number of people on the carpet with high accuracy, and we ran tests with up to four people. We were able to use computational features of the generated waveform, by extracting the Mel Frequency Cepstral Coefficients (MFCC), and using formal computation intelligence to distinguish different people with an average accuracy of 82%, given that the experiments were performed within the same day.Includes bibliographical references

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

An automatic wearable multi-sensor based gait analysis system for older adults.

Gait abnormalities in older adults are very common in clinical practice. They lead to serious adverse consequences such as falls and injury resulting in increased care cost. There is therefore a national imperative to address this challenge. Currently gait assessment is done using standardized clinical tools dependent on subjective evaluation. More objective gold standard methods (motion capture systems such as Qualisys and Vicon) to analyse gait rely on access to expensive complex equipment based in gait laboratories. These are not widely available for several reasons including a scarcity of equipment, need for technical staff, need for patients to attend in person, complicated time consuming procedures and overall expense. To broaden the use of accurate quantitative gait monitoring and assessment, the major goal of this thesis is to develop an affordable automatic gait analysis system that will provide comprehensive gait information and allow use in clinic or at home. It will also be able to quantify and visualize gait parameters, identify gait variables and changes, monitor abnormal gait patterns of older people in order to reduce the potential for falling and support falls risk management. A research program based on conducting experiments on volunteers is developed in collaboration with other researchers in Bournemouth University, The Royal Bournemouth Hospital and care homes. This thesis consists of five different studies toward addressing our major goal. Firstly, a study on the effects on sensor output from an Inertial Measurement Unit (IMU) attached to different anatomical foot locations. Placing an IMU over the bony prominence of the first cuboid bone is the best place as it delivers the most accurate data. Secondly, an automatic gait feature extraction method for analysing spatiotemporal gait features which shows that it is possible to extract gait features automatically outside of a gait laboratory. Thirdly, user friendly and easy to interpret visualization approaches are proposed to demonstrate real time spatiotemporal gait information. Four proposed approaches have the potential of helping professionals detect and interpret gait asymmetry. Fourthly, a validation study of spatiotemporal IMU extracted features compared with gold standard Motion Capture System and Treadmill measurements in young and older adults is conducted. The results obtained from three experimental conditions demonstrate that our IMU gait extracted features are highly valid for spatiotemporal gait variables in young and older adults. In the last study, an evaluation system using Procrustes and Euclidean distance matrix analysis is proposed to provide a comprehensive interpretation of shape and form differences between individual gaits. The results show that older gaits are distinguishable from young gaits. A pictorial and numerical system is proposed which indicates whether the assessed gait is normal or abnormal depending on their total feature values. This offers several advantages: 1) it is user friendly and is easy to set up and implement; 2) it does not require complex equipment with segmentation of body parts; 3) it is relatively inexpensive and therefore increases its affordability decreasing health inequality; and 4) its versatility increases its usability at home supporting inclusivity of patients who are home bound. A digital transformation strategy framework is proposed where stakeholders such as patients, health care professionals and industry partners can collaborate through development of new technologies, value creation, structural change, affordability and sustainability to improve the diagnosis and treatment of gait abnormalities

Implantation d’un système de vidéosurveillance intelligente pour détecter les chutes en milieu de vie

Introduction. Le vieillissement de la population est associé à un risque accru de chute menaçant le maintien des aînés à domicile et dans la communauté. Les nombreuses conséquences néfastes des chutes sur la santé de l’aîné (ex : blessures) et sur son indépendance sont réduites lorsque la prise en charge postchute est rapide. Or les proches-aidants intervenant auprès des aînés en cas de chute ne sont pas assez nombreux et sont souvent conduits à l’épuisement en raison du fardeau lié aux soins apportés à l’aîné (Ducharme, 2006; Wolff et al., 2017; World Health Organization, 2015). L’élaboration d’alternatives pour détecter et alerter lors de chutes devient incontournable pour faciliter le maintien à domicile et dans la communauté en sécurité et pour maintenir une qualité de vie (van Hoof, Kort, Rutten, & Duijnstee, 2011). De nombreuses technologies de détection des chutes ont été développées. Cependant elles ont des limites (ex : l’enregistrement de données personnelles) que le système de vidéosurveillance intelligente (VSI) développé par notre équipe tente de compenser. La VSI est composée d’une caméra reliée à un ordinateur, lui-même relié à Internet. Basée sur une analyse informatisée de l’image, la VSI détecte automatiquement la chute et envoie une alerte au répondant choisi (ex : le proche-aidant) sur son cellulaire, son ordinateur ou sa tablette. Elle préserve la vie privée par son fonctionnement en circuit fermé : en absence de chute, les images sont détruites; lors d’une chute, une image de la chute est transmise au répondant, cette image peut être brouillée à la demande de l’aîné. Si l’aîné l’autorise, il est possible d’enregistrer les 30 secondes précédant la chute pour documenter ses causes. Les travaux antérieurs montrent que la VSI a le potentiel de répondre aux besoins des usagers (Lapierre et al., 2016, 2015; Londei et al., 2009; Rougier, St-Arnaud, Rousseau, & Meunier, 2011). Cependant, il importe de valider sa technologie et d’explorer la perception des usagers dans des conditions écologiques (à domicile auprès d’aînés chuteurs) (Atoyebi, Stewart, & Sampson, 2015). But de l’étude. Basé sur le Modèle de compétence expliquant les relations personne- environnement (Rousseau, 2017), cette thèse a pour but d’explorer la faisabilité de l’implantation de la VSI pour détecter les chutes à domicile afin d’améliorer la qualité de vie de l’aîné et diminuer le fardeau du proche-aidant. Méthodologie. La thèse suit un devis de recherche de développement (Contandriopoulos, Champagne, Potvin, Denis, & Boyle, 2005) en quatre étapes. L’étape 1 consistait en deux revues de la portée (Daudt, Van Mossel, & Scott, 2013) traitant respectivement des technologies de détection des chutes et des technologies de gestion de l’errance. Plusieurs banques de données ont été explorées (ex: CINHAL, Medline, Embase). Chaque étape de sélection des études, puis d’extraction et d’analyse des données a été réalisée indépendamment par deux co-auteurs. Leurs résultats ont été comparés et les désaccords ont été résolus par consensus ou par l’intervention d’un tiers. Les données extraites ont été analysées de façon descriptive (Fortin & Gagnon, 2015). L’étape 2 était une étude de cas multiples (Yin, 2014) auprès de six aînées chuteuses vivant seules, concernant l’implantation à domicile d’une version préalable à la VSI, la vidéosurveillance programmable (VSP). La VSP a été installée durant sept nuits chez les participantes pour observer leurs déplacements lors des levés la nuit pour aller à la toilette. Des entrevues semi-structurées ont été réalisées avant puis après l’expérimentation. Les données ont été analysées qualitativement (Miles, Huberman, & Saldana, 2014; Yin, 2014). L’étape 3 était une preuve de concept en deux phases : 1) une étude de simulation en appartement-laboratoire (Contandriopoulos, Champagne, Potvin, Denis, & Boyle, 2005) et 2) un pré-test au domicile de jeunes adultes. La phase 1 impliquait la simulation de scenarios de la vie quotidienne et de scenarios de chutes afin d’estimer la sensibilité, la spécificité, le taux d’erreur et la précision de la VSI. Le pré-test consistait en l’implantation de la VSI à domicile pendant 28 jours afin d’anticiper les difficultés technologiques liées à une implantation prolongée. Pour les deux phases, un journal de bord a été complété afin de documenter le fonctionnement de la VSI puis les données ont été analysées descriptivement. L’étape 4 était une étude de cas multiples (Yin, 2014) auprès de trois dyades aînés/proches-aidants. Les aînés inclus, présentant un risque de chute élevé, vivaient seuls à domicile. La VSI était implantée pour deux mois, avec le proche-aidant comme destinataire des alertes. Une entrevue semi-structurée était réalisée, avant, à mi-parcours et après l’expérimentation. Les données ont été analysées qualitativement (Miles, Huberman, & Saldana, 2014; Yin, 2014). Résultats. Les résultats ont abouti à l’adaptation de la VSI pour explorer la faisabilité de son implantation à domicile afin de détecter les chutes graves. L’étape 1 a souligné les lacunes dans la littérature, dont certaines ont été comblées par le projet de thèse (ex : manque d’étude explorant l’implantation de systèmes ambiants dans des domiciles variés). Cette étape a aussi permis d’identifier les façons de bonifier la VSI et sa procédure d’implantation. L’étape 2 a mis en évidence des facteurs pouvant faciliter ou freiner l’implantation de systèmes de caméras à domicile. L’étape 3 a permis de valider la technologie de la VSI dans un environnement similaire à celui de l’aîné et de résoudre les problèmes techniques liés à l’implantation prolongée du système. Enfin, l’étape 4 a permis d’explorer la faisabilité de l’implantation de la VSI au domicile d’aînés chuteurs pendant une période de deux mois. Discussion. Cette recherche de développement a permis d’adapter la VSI pour son implantation grâce à plusieurs étapes de recherche (des revues de la portée, une preuve de concept, étude de cas multiple) puis de montrer la faisabilité de son implantation. Les résultats ont abouti à l’identification de facteurs influençant l’implantation de la VSI à domicile et ont permis d’émettre des recommandations à cet égard. Cette recherche est originale notamment sur trois aspects: 1) l’implication d’une équipe multidisciplinaire, 2) une conception technologique centrée sur l’usager, 3) l’implantation à domicile de la technologie. Même si des défis persistent quant à son implantation à domicile (ex. réduire l’écart de performance du système entre l’appartement-laboratoire et le domicile), cette étude encourage la poursuite du développement de la VSI. Conclusion. Cette thèse visait à répondre à la problématique des chutes des aînés à domicile grâce à l’implantation d’un système de vidéosurveillance intelligente pour alerter automatiquement le proche-aidant. Les résultats de cette recherche de développement, soulignent que la VSI serait une avenue prometteuse pour détecter les chutes graves, alerter le proche et documenter la cause des chutes. Les futures recherches sur l’implantation de technologies similaires devraient impliquer des devis de recherche quantitatifs, avec notamment des profils plus variés de proches-aidants et une implantation plus longue pour démontrer les effets de la VSI. La VSI pourrait ensuite devenir accessible aux aînés afin de soutenir leur maintien à domicile et dans la communauté et soulager le fardeau des proches- aidants.Introduction. Aging is associated with an increased risk of fall, which threatens Aging in Place. The numerous and serious consequences of falls on the older adult’s health and independence are reduced with a quick intervention. Yet the informal caregivers, who often intervene in case of a fall are not numerous enough and are often worn out because of the burden related to the care provided for the older adult (Ducharme, 2006; Wolff et al., 2017; World Health Organization, 2015). The development of alternatives to detect and alert in case of a fall becomes essential to facilitate Aging in Place in safety and to maintain a quality of life (van Hoof, Kort, Rutten, & Duijnstee, 2011). Many fall detection systems have been developed. However, they have limits (eg. the recording of personal data), that the intelligent videomonitoring system (IVS) tries to compensate. The IVS is composed of one camera linked to a computer and to the Internet. Based on the computerized analysis of the images, the IVS automatically detects falls and sends an alert to the chosen recipient (eg. the informal caregiver) on his smartphone, computer or tablet. The IVS preserves privacy with its closed circuit functioning: without a fall, the images are destroyed; in case of a fall, an image of the fall can be sent to the recipient. This image can be blurred at the request of the older adult. The 30 seconds before the fall can be recorded to document its causes, if the older adult authorizes it. Previous studies on the IVS show that the IVS has the potential to answer the users’ needs (Lapierre et al., 2016, 2015; Londei et al., 2009; Rougier, St-Arnaud, Rousseau, & Meunier, 2011). However, it is important to validate its technology and explore users’ perception in ecological conditions (at home with older adults at risk of fall) (Atoyebi, Stewart, & Sampson, 2015). Purpose. Based on the Model of Competence explaining the person-environment interactions (Rousseau, 2017), the study aims to explore the feasibility of the IVS implementation to detect falls at home in order to improve the older adult’s quality of life and decrease the caregiver’s burden. Methodology. The thesis follows a development research design (Contandriopoulos, Champagne, Potvin, Denis, & Boyle, 2005) in four steps. Step 1 was two scoping reviews (Daudt, Van Mossel, & Scott, 2013) on fall detection technology and on wandering management technology respectively. Many databases have been searched (eg. CINHAL, Medline, Embase). Each step of the study selection, data extraction and analysis have been independently realised by two co-authors. Results were compared and disagreements were solved by consensus or by a third part intervention. Extracted data were descriptively analysed (Fortin & Gagnon, 2015). Step 2 was a multiple case study (Yin, 2014) with six older adults living alone with a risk of fall, on the implementation of a previous version of the IVS, the programmable videomonitoring system. The programmable videomonitoring system was installed for seven nights at home to observe participants walk when they went to the bathroom at night. Semi- structured interviews were realised before and after the experiment. Data were qualitatively analysed (Miles, Huberman, & Saldana, 2014). Step 3 was a proof of concept in two phases: 1) a simulation study in an apartment- laboratory (Contandriopoulos, Champagne, Potvin, Denis, & Boyle, 2005) and 2) a pre-test at home with young adults. Phase 1 implied a simulation of daily living scenarios and falls scenarios to estimate the sensitivity, specificity, error rate and accuracy of the IVS. The pre- test consisted in the implementation of the IVS at home for 28 days to anticipate the technological difficulties related to extended implementation. For the two phases, a logbook was completed to document the IVS functioning, then data were descriptively analysed. Step 4 was a multiple case study (Yin, 2014) with three dyads of older adults/caregivers. The included older adults had a high risk of fall and lived alone. The IVS was implemented for a two-month period with the informal caregiver as the alerts recipient. A semi-structured interview was realised before, at mid-term, and after the experiment. Data were qualitatively analysed (Miles, Huberman, & Saldana, 2014). Results. Results encompass the adaptation of the IVS to explore the feasibility of its implementation at home to detect serious falls. Step 1 highlighted the gaps in the literature, some of which were filled by the thesis project (eg. lack of studies exploring the implementation of ambient system in various homes). This step also enabled us to identify ways to improve the IVS and its implementation process. Step 2 highlighted factors facilitating or hindering the implementation of cameras system at home. Step 3 has enabled us to validate the technology in a similar environment to the older adult’s home and to solve technical difficulties related to the prolonged implementation. Finally, step 4 enabled us to explore the feasibility of the implementation of the IVS at older adults’ home for a two-month period. Discussion. This development research enabled us to adapt the IVS for its implementation by means of four research steps (scoping reviews, proof of concept, multiple case study), and then to show the feasibility of its implementation. Results led to the identification of factors influencing the IVS at home and enabled us to make recommendations in this regard. This thesis is original on three aspects: 1) the implication of a multidisciplinary team, 2) a user-based conception, 3) the implementation of the technology at home. Despite the remaining challenges regarding the implementation (eg. the performance discrepancy between the home and the apartment-laboratory), this study encourages the further development of the VSI. Conclusion. This thesis aimed to address the problematic of falls at home thanks to the implementation of the IVS to automatically alert the informal caregiver. Results from this development research highlight that the IVS may be a promising way to detect serious falls, to alert the caregiver and document the falls causes. Future researches should be involving quantitative designs, more specifically with more various profiles of informal caregivers and a longer period of implementation, to demonstrate the IVS outcomes. The IVS could then become accessible to the older adult to support Aging in place and relieve the caregiver’s burden