Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910121767993344 |
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| author | Hristov, Blagoj Hadzi-Velkov, Zoran Saneva, Katerina Hadzi-Velkova Nadzinski, Gorjan Latkoska, Vesna Ojleska |
| author_facet | Hristov, Blagoj Hadzi-Velkov, Zoran Saneva, Katerina Hadzi-Velkova Nadzinski, Gorjan Latkoska, Vesna Ojleska |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_23011 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG Hristov, Blagoj Hadzi-Velkov, Zoran Saneva, Katerina Hadzi-Velkova Nadzinski, Gorjan Latkoska, Vesna Ojleska Machine Learning Artificial Intelligence Signal Processing 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. |
| title | Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG |
| topic | Machine Learning Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2601.23011 |