Human activity analysis: a personal robot integrating a framework for robust person detection and tracking and physical based motion analysis

Abstract

AbstractThe analysis of certain parameters related to cognitive and to motor humans’ activities in everyday life conditions can allow to detect potential behavioral troubles, make diagnoses and assess patients’ progress after a therapy. Within this context, personal robots can provide an autonomous movable platform for embedded sensors allowing to detect and track humans while ensuring an optimal observability of the person’s activity in complex and cluttered environments. This paper presents a framework combining a multimodal human detector based on sensors embedded in a mobile robot and a decisional engine exploiting the fuzzy logic mechanisms to make the robot track humans, maximizing observability and facing losses of detection. The robustness of this framework is evaluated experimentally in home spaces through different scenarios. Such a mobile system provides an effective marker-less motion capture means for sensing human activity in non-invasive fashion. We present a physical model based method exploiting the features of the system and of the embedded Kinect. Its performances are evaluated first comparing the results to those obtained with a precise 3D motion capture marker based system and to data obtained from a dynamic posturography platform. Then an experiment in real life conditions is performed to assess the system sensitivity to some gait disturbances.