Abstract: sEMG is often used to predict human intention behavior.It is an unstable,non-periodic and noisy bioelectrical signal,which is easily affected by power frequency interference and environmental interference,so it is difficult to predict it.This paper proposes CNNM based onVMD and PSO algorithm.The model combines LSTM,CNN andBiLSTM.Firstly,the Ninapro dataset is used to optimize the parameters of VMD through the improved PSO algorithm,and the sEMG signal is processed by VMD.The decomposed components are weighted and reconstructed according to the Hilbert energy method,which reduces the complexity of the signal and retains the key features.Then,the LSTM method is used to extract the temporal features from sEMG signals,the CNN method is used to further extract the spatial features,and the attention mechanism is used to strengthen the extraction of key information.Finally,it is input into BiLSTM for prediction and recognition.Experimental results show that the prediction accuracy of the proposed model can reach 99.9%,and the prediction accuracy of CNNM is improved by 3%～8% compared with other models.Finally,the role of each module is verified by ablation experiments.This research aims to improve the prediction and recognition accuracy of gesture actions and provide an effective solution for the control of rehabilitation training robots.

Key words: Surface electromyography, Variational mode decomposition, Convolutional neural network, Long short-term memory network, Bidirectional long-short term memory network