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 study is included in the framework of Health Smart Homes which monitor some physiological or not physiological parameters of elderly people living independently at home. In this study we will focus on the walk detection. Walk activity is one parameter to evaluate the health of patient. For example, the total time of walk during a day allows assessing quickly if the subject is mobile rather than immobile. To reach this goal we used a kinematic sensor placed on the chest recording the movements of the subject. The data are analyzed by six algorithms to detect walk phases: two based on Fourier analysis and the others using a wavelet decomposition (DWT and CWT). All algorithms are described and the performances are evaluated on real data recorded with 20 elderly people. Results show that the method using the DWT decomposition is the most efficient (78.5% in sensitivity and 67.6% in specificity).

Clinically useful and efficient assessment of balance during standing and walking is especially challenging in patients with neurological disorders. However, rehabilitation robots could facilitate assessment procedures and improve their clinical value. We present a short overview of balance assessment in clinical practice and in posturography. Based on this overview, we evaluate the potential use of robotic tools for such assessment. The novelty and assumed main benefits of using robots for assessment are their ability to assess ‘severely affected’ patients by providing assistance-as-needed, as well as to provide consistent perturbations during standing and walking while measuring the patient’s reactions. We provide a classification of robotic devices on three aspects relevant to their potential application for balance assessment: 1) how the device interacts with the body, 2) in what sense the device is mobile, and 3) on what surface the person stands or walks when using the device. As examples, nine types of robotic devices are described, classified and evaluated for their suitability for balance assessment. Two example cases of robotic assessments based on perturbations during walking are presented. We conclude that robotic devices are promising and can become useful and relevant tools for assessment of balance in patients with neurological disorders, both in research and in clinical use. Robotic assessment holds the promise to provide increasingly detailed assessment that allows to individually tailor rehabilitation training, which may eventually improve training effectiveness.

This work takes place within the framework of Smart Homes, with the goal to monitor the activities of elderly people, living independently at home, in order to continuously assess their level of activity and therefore their autonomy. A method is proposed for the selection of a range of sensors and for multiple data fusion. The system was evaluated on 7 young and 4 elderly healthy subjects who performed scenarios of daily activities (sleeping, eating, walking, and transfer) within a controlled environment. These activities were correctly classified with an overall sensitivity and specificity of 67.0% (out of 267 activities) and 52.6% (502) for the group of young people, and of 86.9% (222) and 59.3% (492) for the elderly group. The results were better with activities commonly performed in a dedicated location (i.e., taking meals in the kitchen, toileting in the bathroom). The results are acceptable with a reduced set of sensors although numerous and/or more informative sensors (i.e., video, sound detection, sensitive floors, etc.) give higher results at the cost of more cumbersome and costly systems, difficult to deploy in a private home and eventually more intrusive.

Anesthesiologists found a vibro-tactile belt to be wearable and usable and could accurately decode vibro-tactile messages in a real-time clinical environment.

This work was conducted in TIMC laboratory to develop methods able to monitor physical activities. In the framework of Health Smart Home, the purpose is to maintain and supervise elderly or fragile people at home. Activity and autonomy levels are important criteria to evaluate the health of the patient. The time spent in each postural state (lying, sitting, standing), the periods of walking and the number of postural transitions: sit-to-stand (StS), back-to-sit (BtS) give information about the patient's activity. The purpose of the current study is to detect these activities using an unique sensor made of three accelerometers, attached to the chest. First, this paper describes how each algorithm (posture, walk, postural transitions) works. Secondly, the results on real data are shown. An experiment with elderly subjects was carried out. Each subject performed daily activities (walking, sitting, lying down, ...) while wearing the sensor.