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During the rehabilitation process, the movements of the post-stroke patients need to be localized and learned so that incorrect movements can be instantly identified and modified. This is vital and necessary for patients to recover and improve their mobility toward normal life. This paper presents an adaptive Kalman filter algorithm for position estimation of patients' movements. In order to improve the performance of dynamic performance of the filter, a modified adaptive filtering algorithm is investigated. The feasibility and efficiency of the proposed adaptive algorithm is verified by simulation results.