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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.04181 |
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| _version_ | 1866909983825723392 |
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| author | Touko, Nia Ellis, Matthew O A Capone, Cristiano Burrello, Alessio Donati, Elisa Manneschi, Luca |
| author_facet | Touko, Nia Ellis, Matthew O A Capone, Cristiano Burrello, Alessio Donati, Elisa Manneschi, Luca |
| contents | Reliable long-term decoding of surface electromyography (EMG) is hindered by signal drift caused by electrode shifts, muscle fatigue, and posture changes. While state-of-the-art models achieve high intra-session accuracy, their performance often degrades sharply. Existing solutions typically demand large datasets or high-compute pipelines that are impractical for energy-efficient wearables. We propose a lightweight framework for Test-Time Adaptation (TTA) using a Temporal Convolutional Network (TCN) backbone. We introduce three deployment-ready strategies: (i) causal adaptive batch normalization for real-time statistical alignment; (ii) a Gaussian Mixture Model (GMM) alignment with experience replay to prevent forgetting; and (iii) meta-learning for rapid, few-shot calibration. Evaluated on the NinaPro DB6 multi-session dataset, our framework significantly bridges the inter-session accuracy gap with minimal overhead. Our results show that experience-replay updates yield superior stability under limited data, while meta-learning achieves competitive performance in one- and two-shot regimes using only a fraction of the data required by current benchmarks. This work establishes a path toward robust, "plug-and-play" myoelectric control for long-term prosthetic use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04181 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition Touko, Nia Ellis, Matthew O A Capone, Cristiano Burrello, Alessio Donati, Elisa Manneschi, Luca Machine Learning Human-Computer Interaction Reliable long-term decoding of surface electromyography (EMG) is hindered by signal drift caused by electrode shifts, muscle fatigue, and posture changes. While state-of-the-art models achieve high intra-session accuracy, their performance often degrades sharply. Existing solutions typically demand large datasets or high-compute pipelines that are impractical for energy-efficient wearables. We propose a lightweight framework for Test-Time Adaptation (TTA) using a Temporal Convolutional Network (TCN) backbone. We introduce three deployment-ready strategies: (i) causal adaptive batch normalization for real-time statistical alignment; (ii) a Gaussian Mixture Model (GMM) alignment with experience replay to prevent forgetting; and (iii) meta-learning for rapid, few-shot calibration. Evaluated on the NinaPro DB6 multi-session dataset, our framework significantly bridges the inter-session accuracy gap with minimal overhead. Our results show that experience-replay updates yield superior stability under limited data, while meta-learning achieves competitive performance in one- and two-shot regimes using only a fraction of the data required by current benchmarks. This work establishes a path toward robust, "plug-and-play" myoelectric control for long-term prosthetic use. |
| title | Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.04181 |