🤖 AI Summary
This study addresses the practical limitations of clinical myoelectric prosthetic control, which are hindered by substantial inter-subject variability and the impracticality of high-density electrode arrays. The authors propose an end-to-end deep learning framework that operates on ultra-low-density (dual-channel) surface electromyography (sEMG) signals. By employing a convolutional sparse autoencoder to directly extract temporal features and integrating few-shot transfer learning with incremental learning strategies, the method drastically reduces user-specific calibration requirements while enabling dynamic expansion of gesture classes. Evaluated on a six-class gesture task, the approach achieves a multi-user F1-score of 94.3% ± 0.3%. For new users, performance improves from 35.1% ± 3.1% to 92.3% ± 0.9% with only minimal calibration data. Furthermore, when extended to ten gesture classes, the system maintains a robust F1-score of 90.0% ± 0.2%.
📝 Abstract
Reliable control of myoelectric prostheses is often hindered by high inter-subject variability and the clinical impracticality of high-density sensor arrays. This study proposes a deep learning framework for accurate gesture recognition using only two surface electromyography (sEMG) channels. The method employs a Convolutional Sparse Autoencoder (CSAE) to extract temporal feature representations directly from raw signals, eliminating the need for heuristic feature engineering. On a 6-class gesture set, our model achieved a multi-subject F1-score of 94.3% $\pm$ 0.3%. To address subject-specific differences, we present a few-shot transfer learning protocol that improved performance on unseen subjects from a baseline of 35.1% $\pm$ 3.1% to 92.3% $\pm$ 0.9% with minimal calibration data. Furthermore, the system supports functional extensibility through an incremental learning strategy, allowing for expansion to a 10-class set with a 90.0% $\pm$ 0.2% F1-score without full model retraining. By combining high precision with minimal computational and sensor overhead, this framework provides a scalable and efficient approach for the next generation of affordable and adaptive prosthetic systems.