MyoFlow: Anchor-Tied Rectified Flow for HD-sEMG Gesture Recognition Across Sessions and Subjects
Read the original on arXiv Machine Learning →MyoFlow introduces a discriminative flow-matching framework for high‑density surface electromyography (HD‑sEMG) gesture recognition that addresses distribution shifts caused by electrode re‑donning and physiological variability. By using a domain‑conditioned rectified flow to transport encoded windows toward gesture anchors, the method enables zero‑shot prediction without a separate classifier head. On the Hyser dataset, MyoFlow outperforms the strongest diffusion‑based baseline by 4.24 % in cross‑session accuracy and 6.37 % in cross‑subject accuracy, and achieves 91.71 % mean zero‑shot accuracy and 97.39 % mean few‑shot accuracy on the CEMHSEY dataset.
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