Affiliations: [a] Department of Medicine, University of Friburg, Fribourg, Switzerland. E-mail: [email protected] | [b] Inria, Villers-lès-Nancy, 54600, France. E-mail: [email protected] | [c] Université de Lorraine, LORIA, UMR 7503, Vandœuvre-lès-Nancy, 54506, France
Abstract: This work concerns the development of low-cost ambient systems for helping elderly to stay at home. Depth cameras allow a real-time analysis of the displacement of the person. We show that it is possible to recognize the activity of the person and to measure gait parameters from the analysis of simple features extracted from depth images. Activity recognition is based on Hidden Markov Models and performs fall detection. When a person is walking, the analysis of the trajectory of her centre of mass allows to measure gait parameters that can then be used for frailty evaluation. We show that the proposed models are robust enough for activity classification, and that gait parameters measurement is accurate. We believe that such a system could be installed in the home of the elderly, while respecting privacy, since it relies on a local processing of depth images. Our system would be able to provide daily information on the person’s activity, the evolution of her gait parameters, and her habits, information that is useful for securing her and evaluating her frailty.
Keywords: Frailty risk assessment, gait analysis, activities recognition, depth camera, elderly people support