用注意力网络提升跨人肌疲劳检测准确率
Cross-subject Muscle Fatigue Detection via Adversarial and Supervised Contrastive Learning with Inception-Attention Network
- 设计含注意力模块的网络，提取共性疲劳特征
- 跨被试分类准确率达93.54%，召回与F1均超92%
- 适合康复训练中需跨人通用模型的场景
肌疲劳检测在物理康复中具有重要意义。以往研究显示，表面肌电（sEMG）在检测肌疲劳方面优于其他生物信号。然而，动态收缩过程中及不同个体间，sEMG特征存在差异，导致检测结果不稳定。为此，本文提出一种新型神经网络，包含基于Inception-注意力模块的特征提取器、疲劳分类器以及带梯度反转层的域分类器。该域分类器促使网络学习对个体不敏感的共性疲劳特征，同时抑制个体特异性特征。此外，引入监督对比损失函数以增强模型泛化能力。实验结果表明，该模型在三分类任务中达到93.54%的准确率、92.69%的召回率和92.69%的F1分数，为跨被试肌疲劳检测提供了稳健解决方案，对康复训练与辅助具有重要指导意义。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。