Background/Objectives: Surface Electromyography (sEMG) presents tremendous potential as a non-invasive interface for the detection of motor intent, yet the low signal-to-noise ratio, subject variability, and the need to capture patterns at both long and short timescales make the recognition of hand gestures challenging. Methods: In this paper, the HAMSNet model is presented, which is designed for the recognition of ten different hand gestures using the sEMG signal. Sliding window segmentation is employed to segment the signal into fixed-length time windows, and channel-wise z-score normalization is applied to reduce amplitude variations. To capture the signal at different timescales, the model utilizes the Hydra 1D convolutional neural network (1D CNN), which extracts both short-range and long-range features. Furthermore, the learned features are refined using the multi-head self-attention technique, which highlights the more discriminative time regions. Finally, the Squeeze-and-Excitation (SE) technique is employed to refine the obtained features by channel-wise recalibration. Results: The model is trained in end-to-end fashion, and the results are validated using the 80/20 split method, where the model achieves 0.9894 accuracy, Macro F1 of 0.9894, and an ROC-AUC score of 0.99977. Additionally, the model achieves an MSE score of 0.001969. Furthermore, the model also achieves high accuracy under the leave-one-subject-out cross-validation (LOSO-CV) protocol, providing encouraging evidence of subject-independent performance within the evaluated dataset. Conclusions: The obtained HAMSNet model’s results are compared with the existing results from the literature on the same dataset. The comparisons show that the HAMSNet outperforms the existing methods. An ablation study is conducted to validate the contribution of each component to the proposed model and an explainability analysis is conducted to indicate the interpretability of the model’s decisions.
Sobahi et al. (Fri,) studied this question.