Surface electromyography feature extraction via convolutional neural network

Although a large number of surface electromyography (sEMG) features have been proposed to improve hand gesture recognition accuracy, it is still hard to achieve acceptable performance in inter-session and inter-subject tests. To promote the application of sEMG-based human machine interaction, a convolutional neural network based feature extraction approach (CNNFeat) is proposed to improve hand gesture recognition accuracy. A sEMG database is recorded from eight subjects while performing ten hand gestures. Three classic classifiers, including linear discriminant analysis (LDA), support vector machine (SVM) and K nearest neighbor (KNN), are employed to compare the CNNFeat with 25 traditional features. This work concentrates on the analysis of CNNFeat through accuracy, safety index and repeatability index. The experimental results show that CNNFeat outperforms all the tested traditional features in inter-subject test and is listed as the best three features in inter-session test. Besides, it is also found that combining CNNFeat with traditional features can further improve the accuracy by 4.35%, 3.62% and 4.7% for SVM, LDA and KNN, respectively. Additionally, this work also demonstrates that CNNFeat can be potentially enhanced with more data for model training.