Mesure de la fragilité et détection de chutes pour le maintien à domicile des personnes âgées

Population ageing is a major issue for society in the next years, especially because of theincrease of dependent people. The limits in specialized institutes capacity and the wish of theelderly to stay at home as long as possible explain a growing need for new specific at home services.Technologies can help securing the person at home by detecting falls. They can also help in theevaluation of the frailty for preventing future accidents. This work concerns the development oflow cost ambient systems for helping the stay at home of elderly. Depth cameras allow analysingin real time the displacement of the person. We show that it is possible to recognize the activityof the person and to measure gait parameters from the analysis of simple feature extracted fromdepth images. Activity recognition is based on Hidden Markov Models and allows detecting atrisk behaviours and falls. When the person is walking, the analysis of the trajectory of her centreof mass allows measuring gait parameters that can be used for frailty evaluation. This work isbased on laboratory experimentations for the acquisition of data used for models training andfor the evaluation of the results. We show that some of the developed Hidden Markov Models arerobust enough for classifying the activities. We also evaluate de precision of the gait parametersmeasurement in comparison to the measures provided by an actimetric carpet. We believe thatsuch a system could be installed in the home of the elderly because it relies on a local processingof the depth images. It would be able to provide daily information on the person activity and onthe evolution of her gait parameters that are useful for securing her and evaluating her frailty.