Integration of vision and inertial sensors for 3D arm motion tracking in home-based rehabilitation

The integration of visual and inertial sensors for human motion tracking has attracted significant attention recently, due to its robust performance and wide potential application. This paper introduces a real-time hybrid solution to articulated 3D arm motion tracking for home-based rehabilitation by combining visual and inertial sensors. Data fusion is a key issue in this hybrid system and two different data fusion methods tire proposed. The first is a deterministic method based on arm structure and geometry information, which is suitable,for simple rehabilitation motions. The second is, a probabilistic method based on all Extended Kalman Filter (EKF) in which data from two sensors is fused in a predict-correct manner ill order to deal with sensor noise and model inaccuracy Experimental results are presented and compared with commercial marker-based systems, CODA and Qualysis. They show good performance for the proposed solution.