A channel-fused gated temporal convolutional network for EMG-based gesture recognition

Background: EMG gesture recognition can be widely applied in many fields, such as prosthetic control and human-computer interaction, to enhance the standard of human life. Purpose: This study aims to develop a deep learning -based model to identify multiple complex gestures from raw EMG signals. Methods: We propose a new channel -fused gated temporal convolutional network. First, a channel fusion and gating mechanism is designed to improve temporal convolutional networks, allowing the model to obtain higher -level features. Second, we improve the channel fusion module by the short-term average energy to fuse the EMG signals of multi -channels more accurately. Results: The model is evaluated on a public dataset of EMG gestures, NinaPro DB5. Results demonstrated that the unbalanced accuracy rate of 53 gesture actions reaches 92.71%, and the balanced accuracy rate 74.79%. Further, ablation experiments validates the effectiveness of each model module. Conclusions: This study demonstrates our proposed approach can improve gesture recognition accuracy for complex gestures and has great potential for practical applications.