Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures

This paper presents a cognitive vision approach to recognize a set of interesting activities of daily living (ADLs) for elderly at home. The proposed approach is composed of a video analysis component and an activity recognition component. A video analysis component contains person detection, person tracking and human posture recognition. A human posture recognition is composed of a set of postures models and a dedicated human posture recognition algorithm. Activity recognition component contains a set of video event models and a dedicated video event recognition algorithm. In this study, we collaborate with medical experts (gerontologists from Nice hospital) to define and model a set of scenarios related to the interesting activities of elderly. Some of these activities require to detect a fine description of human body such as postures. For this purpose, we propose ten 3D key human postures usefull to recognize a set of interesting human activities regardless of the environment. Using these 3D key human postures, we have modeled thirty four video events, simple ones such as "a person is standing" and composite ones such as "a person is feeling faint". We have also adapted a video event recognition algorithm to detect in real time some activities of interest by adding posture. The novelty of our approach is the proposed 3D key postures and the set of activity models of elderly person living alone in her/his own home. To validate our proposed models, we have performed a set of experiments in the Gerhome laboratory which is a realistic site reproducing the environment of a typical apartment. For these experiments, we have acquired and processed ten video sequences with one actor. The duration of each video sequence is about ten minutes and each video contains about 4800 frames.