Continuous Prediction of Joint Angle of Lower Limbs from sEMG Signals

摘要

In order to realize the rehabilitation training of mirror movement in stroke patients, a new motion analysis method of EMG signal is proposed. First, surface electromyography (sEMG), hip joint and knee joint angles of 6 lower limb muscles are collected synchronously. Then, by introducing the coherence analysis and calculating the significant area index, the coupling relationship between the sEMG and the joint angle is quantitatively described, and the muscles of the most coupling relationship are set to the input channels of the model. Next, we introduce the least squares extreme learning machine algorithm based on golden section (GS-LSELM), and establish a nonlinear prediction model between sEMG and joint angle. Finally, the experimental results show that the proposed method can quickly build the model under different motion periods, and it could be used in the tracking control of the rehabilitation robot.