通过融合静态与时间特征，提升肌电信号疲劳识别稳定性。
FatigueFormer: Static-Temporal Feature Fusion for Robust sEMG-Based Muscle Fatigue Recognition
- 分路建模静态与时间特征，用Transformer并行捕捉
- 在20%-80%最大自主收缩下达到顶尖准确率
- 可可视化注意力分布，解释不同条件下的疲劳变化
我们提出FatigueFormer，一种半端到端框架，通过有选择性地分离显著特征并结合深度时序建模，从表面肌电（sEMG）中学习可解释且泛化性强的肌肉疲劳动态。针对以往方法在不同最大自主收缩（MVC）水平下因信号变异和信噪比低导致鲁棒性差的问题，FatigueFormer采用并行Transformer序列编码器分别捕获静态与时间特征动态，融合其互补表示以提升低-高MVC条件下的性能稳定性。在自采集数据集（30名参与者，4个MVC水平：20%-80%）上评估，该方法在轻度疲劳条件下实现最优准确率与强泛化能力。此外，支持基于注意力的疲劳动态可视化，揭示不同特征组与时间窗口在不同MVC水平下的贡献差异，提供对疲劳进程的可解释洞察。
原文摘要 · Abstract (English)
We present FatigueFormer, a semi-end-to-end framework that deliberately combines saliency-guided feature separation with deep temporal modeling to learn interpretable and generalizable muscle fatigue dynamics from surface electromyography (sEMG). Unlike prior approaches that struggle to maintain robustness across varying Maximum Voluntary Contraction (MVC) levels due to signal variability and low SNR, FatigueFormer employs parallel Transformer-based sequence encoders to separately capture static and temporal feature dynamics, fusing their complementary representations to improve performance stability across low- and high-MVC conditions. Evaluated on a self-collected dataset spanning 30 participants across four MVC levels (20-80%), it achieves state-of-the-art accuracy and strong generalization under mild-fatigue conditions. Beyond performance, FatigueFormer enables attention-based visualization of fatigue dynamics, revealing how feature groups and time windows contribute differently across varying MVC levels, offering interpretable insight into fatigue progression.
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