Problem
Research questions and friction points this paper is trying to address.
muscle fatigue detection
cross-subject
sEMG
feature instability
subject variability
Cross-subject Muscle Fatigue Detection via Adversarial and Supervised Contrastive Learning with Inception-Attention Network
🤖 AI Summary
This study addresses the challenge of unstable fatigue-related features and poor generalization of surface electromyography (sEMG) signals across subjects and under dynamic contraction conditions. To this end, the authors propose a novel neural network architecture integrating an Inception-attention module, which innovatively combines adversarial domain classification with supervised contrastive learning to effectively disentangle fatigue-related features from subject-specific information. The proposed method significantly enhances cross-subject robustness, achieving 93.54% accuracy, 92.69% recall, and 92.69% F1-score in a three-class muscle fatigue recognition task. These results demonstrate its strong potential as a reliable technical solution for fatigue monitoring in rehabilitation training scenarios.
📝 Abstract
Muscle fatigue detection plays an important role in physical rehabilitation. Previous researches have demonstrated that sEMG offers superior sensitivity in detecting muscle fatigue compared to other biological signals. However, features extracted from sEMG may vary during dynamic contractions and across different subjects, which causes unstability in fatigue detection. To address these challenges, this research proposes a novel neural network comprising an Inception-attention module as a feature extractor, a fatigue classifier and a domain classifier equipped with a gradient reversal layer. The integrated domain classifier encourages the network to learn subject-invariant common fatigue features while minimizing subject-specific features. Furthermore, a supervised contrastive loss function is also employed to enhance the generalization capability of the model. Experimental results demonstrate that the proposed model achieved outstanding performance in three-class classification tasks, reaching 93.54% accuracy, 92.69% recall and 92.69% F1-score, providing a robust solution for cross-subject muscle fatigue detection, offering significant guidance for rehabilitation training and assistance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
adversarial learning
supervised contrastive learning
Inception-Attention network
cross-subject generalization
sEMG-based fatigue detection
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