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The International Journal of Robotics Research
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Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation

Yaqin Tao

Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, U.K., ytao{at}essex.ac.uk

Huosheng Hu

Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, U.K., zhou{at}essex.ac.uk

Huiyu Zhou

Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, U.K., hhu{at}essex.ac.uk

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 are 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 an Extended Kalman Filter (EKF) in which data from two sensors is fused in a predict-correct manner in 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.

Key Words: sensor fusion • extended Kalman filter • inertial sensor • human motion tracking • home-based rehabilitation

The International Journal of Robotics Research, Vol. 26, No. 6, 607-624 (2007)
DOI: 10.1177/0278364907079278
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