From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG
Surface electromyography provides a practical way to infer human movement intention from wearable muscle recordings, but models trained under a single acquisition setting often lose reliability when the user, session, electrode layout, or gesture protocol changes. This paper proposes AEMG, a self-supervised learning approach designed to extract reusable neuromuscular representations from diverse EMG sources. Eight public gesture datasets are first transformed into a shared signal format to reduce discrepancies in channel configuration, sensor topology, and recording protocol. Instead of relying on fixed-length sliding windows, AEMG identifies contraction events from energy variations and ...
| Authors | Zhenghao Huang, Huilin Yao, Kaikai Wang |
|---|---|
| Primary category | cs.LG |
| Published | 5 May 2026 |
| Last updated at source | 24 May 2026 |
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