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arXiv Machine Learning By Aran Ramakrishnan, Shubhi Mishra, Tyler Ham, Assel Kembay, Jason Eshraghian

Cross-User Adaptation for sEMG Gesture Recognition via Continual Learning

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The paper investigates how to adapt recurrent neural networks for surface electromyography (sEMG) gesture recognition across different users and new gesture classes using continual learning (CL). Three CL strategies—experience replay, elastic weight consolidation (EWC), and naive fine‑tuning—were evaluated on the NinaPro DB1 dataset under domain‑ and class‑incremental drift, with experience replay achieving the highest final accuracy (83.3%) and the lowest forgetting (1.8–5.8%). The study also shows that training on shuffled, independently‑windowed segments and incorporating hand‑crafted time‑domain features can significantly improve per‑task accuracy, but only experience replay maintains this advantage under class‑incremental drift.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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