Research on Gesture Recognition of Surface EMG Based on Machine Learning

Abstract

AbstractGesture recognition pertains to recognizing meaningful expressions of motion by human, it is utmost important in medical rehabilitation, robot control as well as prosthesis design. Compared with gesture recognition based on machine vision, the gesture recognition based on wearable device, especially wearable surface electromyogram (sEMG) signal acquisition equipment, has more important theoretical and practical application prospects. However, there are still many urgent problems in sEMG signals, involving the signal acquisition and recognition accuracy of multi-channel sEMG signals, to be solved. For these problems, we designed a wearable sEMG armband with convenient acquisition and high precision to record sEMG signals and then done the gesture recognition based on deep learning method. Firstly, sEMG signals are classified, denoised and extracted features, and then extended data by sliding window. Then, Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP) were constructed to classify the 9 predefined gestures. The result showed that both methods achieve high offline recognition rate. The average gesture recognition accuracy of CNN is 99.47%; The average gesture recognition accuracy of MLP is 98.42%.KeywordsSurface EMGHand gesture recognitionCNNMLP