EMGFlow: Robust and Efficient Surface Electromyography Synthesis via Flow Matching
Deep learning-based surface electromyography (sEMG) gesture recognition is frequently bottlenecked by data scarcity and limited subject diversity. While synthetic data generation via Generative Adversarial Networks (GANs) and diffusion models has emerged as a promising augmentation strategy, these approaches often face challenges regarding training stability or inference efficiency. To bridge this gap, we propose EMGFlow, a conditional sEMG generation framework. To the best of our knowledge, this is the first study to investigate the application of Flow Matching (FM) and continuous-time generative modeling in the sEMG domain. To validate EMGFlow across three benchmark sEMG datasets, we em...
| Authors | Boxuan Jiang, Chenyun Dai, Can Han |
|---|---|
| Primary category | cs.HC |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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