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

作　　者：杨延璞[1] 余文锋 安为岚 韩钟剑[2] 范昱 YANG Yanpu;YU Wenfeng;AN Weilan;HAN Zhongjian;FAN Yu(Key Laboratory of Road Construction Technology and Equipment,Ministry of Education,Chang’an University,Xi’an 710064;The 20th Research Institute of China Electronics Technology Group Corporation,Xi’an 710068)

机构地区：[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

出　　处：《机械设计》2024年第8期196-201,共6页Journal of Machine Design

基　　金：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)。

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0.99827和0.83218,与其他疲劳识别算法相比具有优越性。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 parametric modeling characteristics computational method of the SEMG signals were studied through the collection of surface electromyography(SEMG)signals and subjective fatigue levels.Kernel principal component analysis(KPCA)was employed to perform dimension reduction and recognization of muscle fatigue status during overhead work.The findings revealed that the trapezius muscle exhibited the highest contribution rate in terms of SEMG during overhead tasks.The recognition rates for fatigue using KPCASVM were 0.99827 and 0.83218 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 

分 类 号：R873[医药卫生—运动医学]