Kalman Filter and Moving Average Method based Human-Robot Interaction Torque Estimation for a Lower Limb Rehabilitation Robot

Xu Liang, Yuchen Yan, Tingting Su, Zhao Guo, Shengda Liu, Haojian Zhang, Guangping He

Abstract: Human-robot interaction torque (HT) plays an important role in the application of lower limb rehabilitation robots. To accurately and quickly obtain the HT applied by the subject during the rehabilitation training, an estimation approach of HT is proposed on the basis of the strong tracking Kalman filter (STKF) and modified moving average method in this paper. First, the dynamics and its corresponding state space model of the human-robot hybrid system are established. Second, the STKF is designed to track the sudden change of the HT rapidly. Based on the hull moving average (HMA) and the weighted moving average (WMA), the hull-weighted moving average with a variable time window (H-WMA) is introduced to improve the estimation accuracy of the HT. Finally, experiments were conducted using a lower limb rehabilitation robot. The experimental results demonstrate its accurate estimation capabilities for assessing the HT. In addition, the proposed method has an effective tracking ability even when the HT changes suddenly.