A hybrid CNN-LSTM network for hand gesture recognition with surface EMG signals

Abstract

In this paper, hybrid network based on convolutional neural network (CNN) and long short-term memory network (LSTM) is proposed to improve hand gesture recognition accuracy. Distinguish from the large number of traditional surface electromyography (sEMG) features proposed by previous researchers, without involving a lot of manual design and professional domain knowledge, this hybrid network can automatically extract both spatial features and temporal features from the input sEMG signals. The hybrid CNN-LSTM Network has two parallel feature extraction stages: spatial features extraction using CNN and temporal features extraction using LSTM. The hybrid CNN-LSTM network combines spatial features and temporal features as Hybrid features (HybridFeat) and feeds HybridFeat into traditional classifiers, including linear discriminant analysis (LDA), support vector machine (SVM) and K nearest neighbor (KNN). The experiments showed that both in inter-session scenario and inter-subject scenario, the HybridFeat outperforms all the tested traditional features and CNNFeat. Besides, it was found that combining HybridFeat with traditional features can further improve the accuracy.