MSCB-DualAttn Network for sEMG-Based Gesture Recognition in Transradial Amputees with Varying Residual Limb Lengths

Transradial amputation severely impairs hand function, and myoelectric control offers a promising solution. However, due to the heterogeneity of neuromuscular compensation, existing methods often struggle to generalize across populations with varying residual limb lengths. In this study, we collected an EMG dataset from 41 participants, including healthy individuals and amputees with long, medium, and short residual limbs, performing 15 gestures, and proposed a Multi-Scale Convolutional Block with Dual Attention Network (MSCB-DualAttn) that integrates parallel multi-scale convolutions (kernel sizes 3, 5, and 7) with channel and temporal attention mechanisms. The model achieved recognition accuracies of 93.79%, 86.13%, and 78.10% in the healthy, long-stump, and middle-stump groups, respectively, and 62.69% in the short-stump group, representing a 13.83 percentage point improvement over the baseline model. Ablation studies and interpretability analyses confirmed the complementary roles of the multi-scale and attention modules, while the performance degradation in the short-stump group suggested a potential limitation of existing models when neuromuscular control shifts from fine synergy to coarse compensation. This study proposes a model framework that enhances gesture recognition accuracy in amputees and highlights the potential of physiology-driven architectures for myoelectric control.