Abstract
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.
Abstract (translated)
基于表面电生理（sEMG）的手势识别在许多三维交互场景中越来越重要。然而，sEMG很容易受到现实环境中各种形式的噪声影响，导致通过sEMG提供长期稳定交互存在挑战。现有的方法通常很难通过各种预定义的数据增强技术增强模型的噪声韧性。在这项工作中，我们从短期增强的角度重新审视问题，以改善精度和对各种常见噪声场景的鲁棒性，使用sEMG固有模式信息和滑动窗口注意力进行学习去噪。我们提出了一个短期增强模块（STEM），可以轻松地与各种模型集成。STEM带来几个优点：1）可学习去噪，无需手动数据增强；2）可扩展性，适用于各种模型；3）性价比高，通过高效的注意力机制实现短期增强。特别地，我们将STEM集成到Transformer中，创建了短期增强Transformer（STET）。与最佳竞争方法相比，STET受到的噪声影响降低了20%以上。我们还报道了在分类和回归数据集上的积极结果，并证明了STEM在各种手势识别任务上通用。
URL
https://arxiv.org/abs/2404.11213
