Cross-subject Muscle Fatigue Detection via Adversarial and Supervised Contrastive Learning with Inception-Attention Network

TL;DR

This paper introduces a novel neural network model that combines adversarial learning and supervised contrastive loss to improve cross-subject muscle fatigue detection using sEMG signals, achieving high accuracy and robustness.

Contribution

The study presents a new Inception-attention based neural network with domain adversarial training and contrastive loss for stable, subject-invariant muscle fatigue detection.

Findings

Achieved 93.54% accuracy in three-class fatigue classification.

Demonstrated improved generalization across subjects.

Provided a robust solution for rehabilitation applications.

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.