Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in Surface Electromyographic Signal Analysis
arxiv(2024)
摘要
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
both classification and regression datasets and demonstrate that STEM
generalizes across different gesture recognition tasks.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要