SEMG Motion Intention Recognition Based on Wavelet Time-Frequency Spectrum and ConvLSTM

Abstract

The electromyographic signal (EMG) is a kind of bioelectrical signal, which can predict human motion intention through signal analysis. Multiple classification models are used to predict the motion intention. It has been found that the classification accuracy is closely related to the feature information extracted from the signal. Traditionally, features are designed manually through prior knowledge. In this paper, a kind of EMG signal classification method based on convolutional neural network and convolutional long-Term memory network (CNN-ConvLSTM) is proposed. ConvLSTM, with its global feature extraction capability, is designed to extract the sEMG features of each channel, and due to its strong local feature extraction capability, CNN was designed to further extract the fused feature information and realize end-To-end classification. The experimental results show that this algorithm has better classification performance than the existing classification methods. The electromyographic signal (EMG) is a kind of bioelectrical signal to recognize human movement intention.