Electromyography (EMG) signals, as important bioelectric signals reflecting human muscle activity, have broad application prospects in fields such as human-computer interaction, prosthetic control, and motion recognition. However, electromyographic signals themselves are susceptible to noise interference such as motion artifacts and power interference, and traditional filtering methods are difficult to effectively distinguish signals from complex background noise. Meanwhile, traditional feature engineering relies on manual experience, which limits the model's generalization ability and real-time processing performance. In response to the above issues, this article proposes an end-to-end electromyographic signal processing method based on Convolutional Neural Network (CNN), which is used to simultaneously achieve signal denoising and motion pattern recognition. Firstly, the original electromyographic signal is segmented using a sliding window method and standardized; Subsequently, the processed data is input into a CNN model consisting of multiple layers of convolution and pooling structures, which automatically extracts time-domain features and completes classification. During the model training phase, the introduction of cross entropy loss function and Adam optimization algorithm improves the convergence speed and classification accuracy of the model. We used an open-source surface Electromyographic (sEMG) dataset on the PhysioNet platform for validation and compared its performance with traditional support vector machine (SVM) methods. The experimental results show that the proposed method is significantly better than traditional methods in key indicators such as accuracy and F1 score, and has better robustness and real-time performance. This study demonstrates the potential of deep learning in electromyographic signal processing, providing technical support for future intelligent prosthetic control and high-precision human-machine interaction systems.
Yicheng Yu (Thu,) studied this question.