Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG
This addresses the problem of high inter-subject variability and impractical sensor arrays for affordable prosthetic control, offering an incremental improvement with scalable efficiency.
The study tackled gesture recognition for myoelectric prostheses using only two sEMG channels, achieving a multi-subject F1-score of 94.3% ± 0.3% and improving unseen subject performance from 35.1% ± 3.1% to 92.3% ± 0.9% with few-shot transfer learning.
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.