Human pose recovery for rehabilitation using ambulatory sensors

In this paper, an approach for lower-leg pose recovery from ambulatory sensors is implemented and validated in a clinical setting. Inertial measurement units are attached to patients undergoing physiotherapy. The sensor data is combined with a kinematic model within an extended Kalman filter framework to perform joint angle estimation. Anthropometric joint limits and process noise adaptation are employed to improve the quality of the joint angle estimation. The proposed approach is tested on 7 patients following total hip or knee joint replacement surgery. The proposed approach achieves an average root-mean-square error of 0.12 radians at key poses.

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