Gesture recognition based on surface electromyography (sEMG) has been gaining
importance in many 3D Interactive Scenes. However, sEMG is easily influenced by
various forms of noise in real-world environments, leading to challenges in
providing long-term stable interactions through sEMG. Existing methods often
struggle to enhance model noise resilience through various predefined data
augmentation techniques. In this work, we revisit the problem from a short term
enhancement perspective to improve precision and robustness against various
common noisy scenarios with learnable denoise using sEMG intrinsic pattern
information and sliding-window attention. We propose a Short Term Enhancement
Module(STEM) which can be easily integrated with various models. STEM offers
several benefits: 1) Learnable denoise, enabling noise reduction without manual
data augmentation; 2) Scalability, adaptable to various models; and 3)
Cost-effectiveness, achieving short-term enhancement through minimal
weight-sharing in an efficient attention mechanism. In particular, we
incorporate STEM into a transformer, creating the Short Term Enhanced
Transformer (STET). Compared with best-competing approaches, the impact of
noise on STET is reduced by more than 20%. We also report promising results on
both classification and regression datasets and demonstrate that STEM
generalizes across different gesture recognition tasks.

基于表面肌电图的手势识别，通过短期增强模块 (STEM) 和可学习的降噪机制，实现了对噪音的减少和对各种手势识别任务的泛化。