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arXiv Machine Learning By Jethro Odeyemi, W. J. Zhang

Personalising a Cross-User Surface Electromyography Encoder Under a Small Calibration Budget

Read the original on arXiv Machine Learning →

The paper investigates how to personalize a cross-user surface electromyography (sEMG) encoder when only a few calibration repetitions are available. Four methods—prototypical adaptation, linear probes, scaled fine‑tuning, and full fine‑tuning—were evaluated across 77 subjects on two databases. Full fine‑tuning consistently achieved the highest accuracy, but a gradient‑free prototypical rule captured 52–78 % of the benefit without per‑user weight copies, enabling quick donning‑time personalization.

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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