Modern myoelectric prosthetic hands continue to face reliability challenges due to the non-stationarities of surface electromyography (sEMG) signals, which are highly sensitive to limb positions, electrode shifts, and grasp forces. While data abundance is a common strategy to mitigate these issues, it significantly increases users’ training burden. Hand gesture recognition, which maps the spatiotemporal patterns of muscle activation to key hand gestures for daily activities, remains the standard control strategy for advanced prosthetic hands. However, while temporal information can be reliably extracted from the sEMG signals, spatial information is highly dependent on electrode placements, which vary significantly between subjects. Previous research in myoelectric hand gesture transfer learning has primarily focused on transferring either spatial information or combined spatiotemporal information, leaving the transfer of temporal information alone largely unexplored. We propose a temporal-spectral cross-subject transfer learning framework using multi-stream convolutional neural networks (CNNs), where each stream processes only a single sEMG channel. Evaluated on the Transradial Amputee sEMG Multi-Contraction Forces Dataset, our framework has achieved training accuracy of 92.73% for medium contraction force and generalization accuracy of 74.53%, outperforming several models for sEMG hand gesture recognition. It also significantly improves recognition accuracy compared to the self-training baseline with the same architecture (repeated measures t-test p ≈ 0.032 ). By excluding spatial knowledge transfer, our approach maintains high robustness even under extreme cases of channel mismatch between source and target subjects. Moreover, this study highlights the importance of CNN architecture design, and spatially agnostic feature extraction for advancing myoelectric control systems.
Le et al. (Sun,) studied this question.