Continuous Kinematics Prediction of Elbow Joint Driven Based on NARX and sEMG
In order to build an accurate prediction model of the surface electromyography(sEMG)and continuous movement variables of the upper limb elbow joint,the elbow joint flexion and extension angle is recorded by the sensor and the surface lectromyography of the muscle associated with the upper limb movement is collected and filtered.The time domain features are extracted from the processing.On this basis,a non-linear autoregressive(NARX)neural network is used to predict the angle of elbow joint continuous motion,and the estimated elbow angle corresponding to the human intention can be finally identified according to the sEMG signal.Extensive experiments are conducted to verify that continuous joint angles of upper limb motion can be accurately estimated by using the proposed model.The model can be effectively used for the control of human prostheses and auxiliary devices,Moreover,the proposed method is superior to BP neural network in estimation performance.
万方数据
维普
