Experimental comparison of human gait tracking algorithms: Towards a context-aware mobility assistance robotic walker
Towards a mobility assistance robot for the elderly, it is essential to develop a robust and accurate gait tracking system. Various pathologies cause mobility inabilities to the aged population, leading to different gait patterns and walking speed. In this work, we present the experimental comparison of two user leg tracking systems of a robotic assistance walker, using data collected by a laser range sensor. The first one is a Kalman Filter tracking system, while the second one proposes the use of Particle Filters. The tracking systems provide the positions and velocities of the user's legs, which are used as observations into an HMM-based gait phases recognition system. The spatiotemporal results of the HMM framework are employed for computing parameters that characterize the human motion, which subsequently can be used to assess and distinguish between possible motion disabilities. For the experimental comparison, we are using real data collected from an ensemble of different elderly persons with a number of pathologies, and ground truth data from a GaitRite System. The results presented in this work, demonstrate the applicability of the tracking systems in real test cases.