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arXiv Machine Learning By Tianhao Wu, Xu Wu, Amirmohammad Radmehr, Jiawei Yu, Yi Wu, Phuc Nguyen, Jian Liu

LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing

Read the original on arXiv Machine Learning →

LiteEMG-FM is an efficient hybrid CNN‑Transformer foundation model designed for electromyography (EMG) sensing. It is pretrained on 16 diverse upper‑ and lower‑limb EMG datasets, enabling representations that generalize across users and datasets. The model incorporates a lightweight, always‑on 1D‑CNN wake‑up module that filters rest and non‑target activity, activating LiteEMG‑FM only for valid gestures, and is evaluated in full inference offloading, split inference, and full on‑device processing scenarios, showing superior performance over state‑of‑the‑art time‑series foundation models and supervised baselines, especially in zero‑calibration cross‑participant and data‑scarce conditions.

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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