An approach to continuous hand movement recognition using SEMG based on features fusion

摘要

Abstract With the continuous development of EMG acquisition technology and artificial intelligence technology, EMG signal analysis has been extensively studied in human–computer interaction, rehabilitation training, prosthetic control and remote device control. As hand movements become more and more complex, hand movement recognition based on surface electromyography (sEMG) has become a hotspot. In this paper, by using multi-features fusion-based Long Short-Term Memory convolutional neural network (MFFCNN-LSTM), a continuous hand movement recognition method based on time-domain and time–frequency-spectrum features of forearm sEMG signal is proposed. Ten basic hand movements including rest action are identified. Firstly, the hand movement data is cut from NinaPro db8 dataset to extract effective sEMG signal fragments. Secondly, the empirical Fourier decomposition method is used to denoise the sEMG signals. Thirdly, the time-domain and time–frequency-spectrum features of sEMG signals from different channels are extracted, and sent to two parallel CNN networks to extract the high-dimension features, respectively. Fourthly, the high-dimension features are fused as the input of LSTM, a fully connected layer and a softmax layer to recognize the continuous hand movements. Finally, MFFCNN-LSTM is compared with the support vector machine, CNN and LSTM on the same computer. The experimental results show that the recognition accuracy, sensitivity and specificity of MFFCNN-LSTM on NinaPro db8 dataset are 98.5, 95.25 and 95.5, respectively. It has higher recognition accuracy on the five public datasets than other methods.