手过头作业上肢肌肉疲劳状态识别研究
Research on fatigue state recognition of upper limb muscles in overhead work
杨延璞 1余文锋 1安为岚 1韩钟剑 2范昱2
作者信息
- 1. 长安大学道路施工技术与装备教育部重点实验室,陕西西安 710064
- 2. 中国电子科技集团第二十研究所,陕西西安 710068
- 折叠
摘要
为对手过头作业中的上肢肌肉疲劳状态进行有效识别,结合复杂装备的维修任务设计了手过头作业试验.通过采集被试的表面肌电信号(Surface Electromyography,SEMG)和主观疲劳状态及研究SEMG信号的时域、频域、非线性及参数模型特征计算方法,基于支持向量机(Support Vector Machine,SVM)采用核主成分分析(Kernel Principal Component Analysis,KPCA)进行特征降维并对手过头作业的肌肉疲劳状态进行识别.研究结果表明:手过头作业中斜方肌的SEMG贡献率最高;KPCA-SVM对训练集和测试集的疲劳识别率分别为0.998 27和0.832 18,与其他疲劳识别算法相比具有优越性.
Abstract
To effectively recognize the state of upper limb muscle fatigue during overhead work,an overhead work experiment was designed in conjunction with complex equipment maintenance tasks.Time-domain,frequency-domain,nonlinear,and paramet-ric modeling characteristics computational method of the SEMG signals were studied through the collection of surface electromyo-graphy(SEMG)signals and subjective fatigue levels.Kernel principal component analysis(KPCA)was employed to perform di-mension reduction and recognization of muscle fatigue status during overhead work.The findings revealed that the trapezius mus-cle exhibited the highest contribution rate in terms of SEMG during overhead tasks.The recognition rates for fatigue using KPCA-SVM were 0.998 27 and 0.832 18 for training set and test set,demonstrating superiority over other fatigue identification algorithms.
关键词
人机工效/手过头作业/肌肉疲劳/表面肌电信号/核主成分分析/支持向量机Key words
ergonomics/overhead work/muscle fatigue/surface electromyography/kernel principal component analysis/support vector machine引用本文复制引用
基金项目
基础加强计划技术领域基金(2021-JCJQ-JJ-1018)
长安大学中央高校基本科研业务费项目(300102253107)
出版年
2024
NETL
NSTL
万方数据