MyoFlow：用于跨会话和跨受试者HD-sEMG手势识别的锚定修正流
MyoFlow: Anchor-Tied Rectified Flow for HD-sEMG Gesture Recognition Across Sessions and Subjects
- Waseda University（早稻田大学）
- KDDI Research Inc.（KDDI综合研究所）
- The University of Manchester（曼彻斯特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
MyoFlow提出判别式流匹配框架，通过锚定修正流实现跨会话和跨受试者的HD-sEMG手势识别，无需独立分类头，显著提升准确率。
AI中文摘要：
高密度表面肌电（HD-sEMG）手势识别支持假肢控制、辅助机器人和康复治疗，但电极重新佩戴和生理变异性会导致分布偏移，从而降低跨会话和跨受试者的识别准确率。生成式HD-sEMG模型主要合成信号用于数据增强；尽管扩散模型增强了表示学习，但预测仍依赖独立的分类器。为了将学习到的动态与决策规则相结合，我们提出了MyoFlow，这是首个用于跨会话和跨受试者HD-sEMG识别的判别式流匹配框架。它将分类重新定义为锚定传输：一种领域条件化的修正流将编码窗口移向作为传输目标的手势锚点，并定义最近锚点的决策几何结构，从而无需独立分类头即可实现零样本预测。在Hyser数据集上，MyoFlow相比最强的基于扩散的基线，平均跨会话和跨受试者准确率分别提高了4.24%和6.37%，并在CEMHSEY数据集上实现了91.71%的平均零样本准确率和97.39%的平均少样本准确率（跨多天）。
英文摘要：
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 recognition 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.
