Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG

• URL: http://arxiv.org/abs/2601.23011v1
• Date: Fri, 30 Jan 2026 14:21:46 GMT
• Title: Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG
• Authors: Blagoj Hristov, Zoran Hadzi-Velkov, Katerina Hadzi-Velkova Saneva, Gorjan Nadzinski, Vesna Ojleska Latkoska,
• Abstract summary: This study proposes a deep learning framework for accurate gesture recognition using only two surface electromyography (sEMG) channels.<n>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.
• Score: 0.46976113832881716
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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