Deep Learning-Based Hand Gesture Recognition Using Electromyography Signals

Currently, hand gesture recognition (HGR) using electromyography (EMG) signals has become a vital research direction in human-computer interaction, rehabilitation, and assistive robotics. Compared to vision-based systems, EMG offers robustness against illumination, occlusion, and privacy concerns by directly capturing neuromuscular activity. However, EMG-based gesture recognition systems faces significant challenges, including the non-stationary and noisy nature of EMG signals, inter and intra subject variability, low signal-to-noise ratio (SNR), and the difficulty of modeling complex spatio-temporal muscle activation patterns. To address these issues, we propose a deep learning-based framework that integrates comprehensive preprocessing and advanced sequence modeling. The pipeline begins with multi-channel EMG acquisition, followed by noise removal, band-pass filtering, segmentation, normalization, and short-time Fourier transform (STFT) spectrogram representation. These processed features are then fed into a hybrid CNN-LSTM-Attention architecture, where convolutional layers extract spatial dependencies across channels, recurrent layers capture temporal dynamics, and the attention mechanism highlights gesture-discriminative regions. Experimental evaluations on benchmark EMG datasets demonstrate that our framework achieves state-of-the-art performance, with Top-1 accuracy exceeding 90% and superior cross-subject generalization compared to traditional machine learning baselines. These findings confirm the effectiveness of deep learning for EMG-based gesture recognition, offering a robust solution to longstanding challenges and paving the way for practical applications in prosthetics, immersive virtual environments, and next-generation human-machine interfaces.