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In this paper, we present a novel sensing and data fusion system to track 3-D arm motion in a telerehabilitation program. A particle filter (PF) algorithm is adopted in the system to fuse data from inertial and visual sensors in a probabilistic manner. It is able to propagate multimodal distributions of system states based on an ldquoimportance samplingrdquo technique by using sets of weighted particles. To avoid the problem of conventional PF algorithms that suffer from particle degeneracy and perform poorly in a narrow distribution situation, we adopt two strategies in our system, namely state space pruning and an arm physical geometry constraint. Experimental results show that the proposed PF framework outperforms other fusion methods and provides accurate results in comparison to the ground truth.