| Volume 26 Issue 6 - Publication Date: 1 June 2007 |
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| Integration of Vision and
Inertial Sensors for 3D
Arm Motion Tracking in
Home-based
Rehabilitation |
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Y. Tao, H. Hu, and H. Zhou Department of Computer Science, University of Essex,
Wivenhoe Park, Colchester CO4 3SQ, U.K. |
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| 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. |
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