Real-time kinematic modeling and prediction of human joint motion in a networked rehabilitation system

In this paper, a networked-based rehabilitation system is introduced for lower-extremity tele-rehabilitation. In order to enable high-level motion planning of the rehabilitation robot in real-time for enhanced safety and appropriate human-robot interactions, a time series model is proposed to capture the kinematics of knee joint rotations. A major challenge in such a system is that measurement data might be delayed or lost due to wireless communication. With a delay and loss compensation mechanism, a modified recursive least square (mRLS) algorithm is applied for real-time modeling and prediction of knee joint rotations in the sagittal plane, and convergence of the proposed algorithm is studied. Simulation and experimental results are presented to verify the performance of the proposed algorithm.