关键词: 表面肌电信号, 手势识别, MYO臂环, 卷积神经网络

Abstract: In order to improve the accuracy of sEMG gesture recognition algorithm and solve the limitation caused by extracting a large number of features artificially, this paper proposes a gesture recognition method based on deep neural network. Firstly, it uses an active segment segmentation method on 8 channel sEMG data which is collected by MYO armband to detect effective actions. Then, it designs a neural network which combines Convolutional Neural Network（CNN） and Long-Short Term Memory network（LSTM）. The result shows that the accuracy of gesture recognition reaches 91.6% and the proposed method is proved to be efficient and feasible.

Key words: sEMG, gesture recognition, MYO armband, convolution neural network