Overcoming the effect of muscle fatigue on gesture recognition based on sEMG via generative adversarial networks

Abstract

Surface electromyography (sEMG)-controlled bionic prostheses have been extensively investigated recently. However, the majority of investigations pertaining to gesture recognition grounded in sEMG predominantly center their focus on the human body in a static and non-fatigued condition, thus neglecting the ramifications of muscle fatigue on the precision of gesture recognition. This study explores the effect of muscle fatigue on recognition accuracy in a gesture recognition task based on sEMG. The muscle fatigue induction experiment was designed, and eight subjects were recruited to participate in the experiment to collect the sEMG signal data sets of seven gesture actions under non-fatigue and fatigue conditions. Seven gesture actions under non-fatigue and fatigue conditions were identified by four classifiers, namely K-nearest neighbor (K-NN), support vector machine (SVM), decision tree (DT), and deep convolution neural network (CNN). The experimental results show that muscle fatigue has a great impact on the accuracy of gesture recognition. Specifically, using the four classifiers K-NN, SVM, DT and CNN trained in the non-fatigue state, the test accuracy of sEMG signals in the non-fatigue state is 96.7 %, 89.0 %, 87.3 % and 97.5 % respectively, while the test accuracy in the fatigue state is reduced to 53.3 %, 55.4 %, 45.8 % and 64.8 % respectively. In this regard, we propose a new method to overcome the effects of muscle fatigue, namely, the data enhancement method based on Wasserstein General Adversary Networks-Gradient Penalty (WGAN-GP), which is used to enhance the sEMG signal under fatigue. Through the data enhancement of the fatigue sEMG signal, the experimental results show that the final test accuracy in the fatigue state is improved by more than 20 %, which can reach 72.3 %, 80.9 %, 69.9 % and 92.1 % respectively. This shows that the method proposed by us can effectively overcome the influence of muscle fatigue on the accuracy of gesture recognition, and has made a great contribution to the improvement of the robustness of gesture recognition model based on sEMG signal.