FatigueFormer: Static-Temporal Feature Fusion for Robust sEMG-Based Muscle Fatigue Recognition

TL;DR

FatigueFormer is a novel semi-end-to-end framework that combines saliency-guided feature separation with deep temporal modeling using Transformers to improve muscle fatigue recognition from sEMG signals across varying MVC levels.

Contribution

It introduces a Transformer-based approach that separately captures static and temporal features, enhancing robustness and interpretability in muscle fatigue detection.

Findings

Achieves state-of-the-art accuracy on a multi-participant dataset.

Demonstrates strong generalization across different MVC levels.

Provides interpretable visualization of fatigue dynamics.

Abstract

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.