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Angle Estimation for Lower Limb Joint Movement Based on VMD-NARX Algorithm

Based on the surface electromyography (sEMG), this study aimed at the control of the fusion between the lower extremity exoskeleton and the human body. In this paper, a variational mode decomposition (vmd) algorithm based on neural network is proposed. Firstly, the sEMG signals collected in the process of lower limb movement are denoised by band-pass filter, and then denoised by variational mode decomposition. Finally, the lower limb angle prediction model based on NARX neural network is established to realize the continuous prediction of lower limb joint angle. The electromyographic signals of 10 muscle groups of the lower limbs of 5 subjects were collected, and the joint angles of walking, sitting up and crossing obstacles were estimated. The experimental results show that VMD takes 102 ms for feature extraction of 600 points, the speed of this method is higher than LMS random forest (LMS-RF), and the RMSE of NARX prediction model based on variational mode decomposition is lower than 2, which proves that this method is an effective method for establishing high-precision angle prediction model.