关键词: 手势识别, 双流卷积神经网络, 离散小波变换, 表面肌电信号, 肌电控制系统

Abstract: Oriented to the high-performing myoelectric control systems,a surface Electromyography (sEMG) based gesture recognition approach based on dual-stream convolutional neural network was proposed.The discrete wavelet transform coefficients together with raw sEMG signals were input into two branches of a dual-stream convolutional neural network respectively for high-level feature learning.The high-level features learned by two branches were fused together via a high-level feature fusion module.The proposed framework was evaluated on three large-scale benchmark databases containing sEMG signals collected from 50 to 52 hand gestures.Experimental results showed that the majority voting accuracy of the proposed framework reached 97.9%,81.3%,and 82.4% on three benchmark databases respectively when recognizing all hand gestures.Moreover,the gesture recognition accuracy of the proposed framework based on sliding windows significantly outperformed the state-of-the-art deep neural networks.

Key words: gesture recognition, dual-stream convolutional neural network, discrete wavelet transform, surface electromyography, myoelectric control systems