A Hybrid Model Based on ResNet and GCN for sEMG-Based Gesture Recognition

Abstract

The surface electromyography (sEMG) is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion. However, limited by feature extraction and classifier selection, the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications. Moreover, due to the different characteristics of sEMG data and image data, the conventional convolutional neural network (CNN) have yet to fit sEMG signals. In this paper, a novel hybrid model combining CNN with the graph convolutional network (GCN) was constructed to improve the performance of the gesture recognition. Based on the characteristics of sEMG signal, GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal. Such strategy optimizes the structure and convolution kernel parameters of the residual network (ResNet) with the classification accuracy on the NinaPro DBl up to 90.07%. The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.

Keywords

deep learning / graph convolutional network (GCN) / gesture recognition / residual network (ResNet) / surface electromyographic (sEMG) signals