Time series prediction of knee joint movement and its application to a network-based rehabilitation system

In this paper, a network-based rehabilitation system is introduced for improved mobility and tele-rehabilitation. Time series of knee joint rotation measurement is obtained using the rehabilitation device in the system, and an autoregressive integrated (ARI) model is built to achieve knee joint angle prediction during the rehabilitation process. It is shown that the predicted knee joint angles are reliable over 10 future time steps. The ARI model and the predicted knee joint angles can provide insight to patients and therapists for deep understanding of patients' walking behaviors. Moreover, it is shown in this paper that the predicted knee joint angles can also be used to compensate for time delay and packet loss in the networked rehabilitation system to achieve accurate torque tracking. Simulation and experimental results are provided to demonstrate the performance of the proposed algorithm.