WASSL-EGR: Wavelet Attention and State-Space Learning for Robust EMG-Based Gesture Recognition
Abstract
Surface electromyography (sEMG) signals enable intuitive human-machine interaction by capturing muscle activation patterns associated with hand gestures. However, the accurate recognition of hand gestures using sEMG signals still remains challenging due to the complex nature of the signal variations. This work proposes a wavelet-based deep learning framework for EMG gesture recognition using time-frequency representations. The segmented sEMG signal is converted into the Continuous Wavelet Transform (CWT) spectrogram to obtain the multi-scale muscle activation. Two lightweight deep learning frameworks are developed to effectively learn gesture representations from these wavelet features. The Wavelet Attention Convolutional Neural Network (WA-CNN) integrates scale and channel attention to emphasize informative frequency bands and EMG electrodes. The Wavelet State-Space Model (WSSM) incorporates efficient temporal modeling of gesture dynamics. Experimental results on the Ninapro DB2 dataset demonstrate that the proposed models outperform conventional machine learning and deep learning baselines. In particular, WSSM achieves 95% accuracy while maintaining low model complexity suitable for real-time wearable EMG-based interaction systems.