Abstract--Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation.

Abstract-By 2050, about a third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international ADL or the French AGGIR scales, by automatically classifying the different Activities of Daily Living performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, Infra-Red Presence Sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs on postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors, is then used to classify each temporal frame into one of the activities of daily living that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using Support Vector Machines. We performed a one-hour experimentation with 13 young and healthy subjects to determine the models of the different activities and then we tested the classification algorithm (cross-validation) with real data.

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Rapid technological advances have prompted the development of a wide range of telemonitoring systems to enable the prevention, early diagnosis and management, of chronic conditions. Remote monitoring can reduce the amount of recurring admissions to hospital, facilitate more efficient clinical visits with objective results, and may reduce the length of a hospital stay for individuals who are living at home. Telemonitoring can also be applied on a long-term basis to elderly persons to detect gradual deterioration in their health status, which may imply a reduction in their ability to live independently. Mobility is a good indicator of health status and thus by monitoring mobility, clinicians may assess the health status of elderly persons. This article reviews the architecture of health smart home, wearable, and combination systems for the remote monitoring of the mobility of elderly persons as a mechanism of assessing the health status of elderly persons while in their own living environment.

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This article reviews the emerging concept of health "Smart" homes (HSH) and its potential through the use of telemedical information systems and communication technologies. HSH systems provide health care services for people with special needs who wish to remain independent and living in their own home. The large diversity of needs in a home-based patient population requires complex technology. Meeting these needs technically requires the use of a distributed approach and the combination of many hardware and software techniques. We also describe the wide scope of new information, communication, and data-acquisition technologies used in home health care. We offer an introduction to the HSH concept in terms of technical, economic, and human requirements. Examples of HSH projects are presented, including a short description of our own smart home and telehealthcare information system project.

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