Problem
Research questions and friction points this paper is trying to address.
HD-sEMG
gesture recognition
distribution shifts
cross-session
cross-subject
MyoFlow: Anchor-Tied Rectified Flow for HD-sEMG Gesture Recognition Across Sessions and Subjects
🤖 AI Summary
为解决HD-sEMG手势识别中因电极重置和生理变化导致的精度下降问题，提出MyoFlow框架，通过锚点绑定的修正流方法提高跨会话和跨受试者的识别准确性。
📝 Abstract
High-density surface electromyography (HD-sEMG) gesture recognition supports prosthetic control, assistive robotics, and rehabilitation, but electrode re-donning and physiological variability cause distribution shifts that degrade accuracy across sessions and subjects. Generative HD-sEMG models primarily synthesize signals for augmentation; although diffusion models enhance representation learning, prediction still relies on a separate classifier. To tie learned dynamics to the decision rule, we propose MyoFlow, the first discriminative flow-matching framework for HD-sEMG recognition across sessions and subjects. It recasts classification as anchor-tied transport: a domain-conditioned rectified flow moves encoded windows toward gesture anchors that serve as transport targets and define the nearest-anchor decision geometry, enabling zero-shot prediction without an independent head. On the Hyser dataset, MyoFlow improves mean cross-session and cross-subject accuracy over the strongest diffusion-based baseline by 4.24\% and 6.37\%, respectively, and achieves 91.71\% mean zero-shot accuracy and 97.39\% mean few-shot accuracy across multiple days on the CEMHSEY dataset.
Innovation
Methods, ideas, or system contributions that make the work stand out.
discriminative flow-matching
anchor-tied transport
domain-conditioned rectified flow
zero-shot prediction
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