中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (6): 235-246.doi: 10.16265/j.cnki.issn1003-3033.2024.06.1674

• 职业卫生 • 上一篇    下一篇

基于脑电信号特征的高铁调度员疲劳状态识别

张光远1,2,3(), 邓龙1,2,3, 王亚伟1,2,3, 孙自伟4, 李莎1,2,3, 陈诚5   

  1. 1 西南交通大学 交通运输与物流学院,四川 成都 610031
    2 西南交通大学 综合交通运输智能化国家地方联合工程实验室,四川 成都 610031
    3 西南交通大学 综合交通大数据应用技术国家工程实验室,四川 成都 610031
    4 西南交通大学 信息科学与技术学院,四川 成都 610031
    5 中国铁道科学研究院集团有限公司 运输及经济研究所,北京 100081
  • 收稿日期:2023-12-27 修回日期:2024-03-26 出版日期:2024-06-28
  • 作者简介:

    张光远 (1979—),男,辽宁庄河人,博士,高级实验师,主要从事铁路运输行车指挥与安全行为等方面的研究。E-mail:

  • 基金资助:
    四川省自然科学基金资助(2024NSFSC0178); 西南交通大学本科教改项目(20221103)

Recognition of fatigue state of high-speed rail dispatchers based on EEG signal characteristics

ZHANG Guangyuan1,2,3(), DENG Long1,2,3, WANG Yawei1,2,3, SUN Ziwei4, LI Sha1,2,3, CHEN Cheng5   

  1. 1 School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan 610031, China
    2 National and Local Joint Engineering Laboratory of Integrated Transportation Intelligence, Southwest Jiaotong University, Chengdu Sichuan 610031, China
    3 National Engineering Experiment of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China
    4 School of Information Science and Technology, Southwest Jiaotong University Chengdu, Sichuan 610031, China
    5 Transportation and Economics Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081,China
  • Received:2023-12-27 Revised:2024-03-26 Published:2024-06-28

摘要:

为增强铁路行车的稳定性与安全性,有效识别调度员的疲劳状态对行车组织的影响,基于脑电(EEG)信号特征,提出一种调度员疲劳状态识别方法,根据作业时间段划分调度员的疲劳状态,设计高铁调度模拟试验获取脑电信号数据,通过小波级数展开和傅里叶变换提取高铁调度被试的3种脑电波频域幅值作为特征值,结合调度员作业特征和脑电信号特征,验证疲劳状态的划分结果,通过Python语言环境搭建ResNet18+SoftMax和MobileNet V2+SoftMax这2种模型,基于深度学习方法,将输入特征转换为三维立体矩形模型,并优化调整权重,获得最优模型,从而判断高铁调度员的疲劳状态。研究结果表明:ResNet18+SoftMax和MobileNet V2+SoftMax神经网络模型对高铁调度试验参与人员的疲劳状态识别准确率分别为92.78%和99.17%;相较于支持向量机(SVM)模型,这2种模型可提升清醒状态和疲劳状态的识别精度,并降低运算时间,其中,MobileNet V2+SoftMax模型的识别准确率和运行速度最优。以MobileNet V2+SoftMax模型原理为内核,可以更快速准确地识别高铁调度员在长时间作业条件下的潜在疲劳风险。

关键词: 脑电(EEG)信号, 高铁调度员, 疲劳状态识别, MobileNet V2网络, ResNet18网络, SoftMax回归

Abstract:

In order to enhance the stability and safety of railway driving and effectively identify the influence of the dispatcher's fatigue state on the driving organization, a method for identifying the fatigue state of the dispatcher was proposed based on the characteristics of EEG signals. The fatigue state of the dispatcher was divided according to the working time period, and the high-speed rail scheduling simulation experiment was designed to collect EEG data. The three types of brainwave frequency-domain amplitudes of high-speed rail dispatching subjects were extracted as the characteristic value by wavelet series expansion and Fourier transform, and the classification results of fatigue state were verified by combining the operation characteristics and EEG signal characteristics of dispatchers. The ResNet18+SoftMax model and MobileNet V2+SoftMax model were built through the Python language environment. The input features were converted into a three-dimensional rectangular model based on deep learning. The weights were optimized and adjusted to obtain the optimal model, so as to judge the fatigue state of high-speed rail dispatchers. The research results show that the fatigue state recognition accuracy of the participants in the high-speed rail scheduling experiment by ResNet18+SoftMax and MobileNet V2+SoftMax two models is 92.78% and 99.17%, respectively, compared with support vector machines(SVM) model to improve the awake state and fatigue state recognition accuracy, and reduce the model computing time. Among them, the MobileNet V2+SoftMax model can better identify the fatigue state of the dispatcher. With the principle of MobileNet V2+SoftMax model as the core, the potential fatigue risk of high-speed rail dispatchers under long-term working conditions can be identified more quickly and accurately.

Key words: electroencephalogram(EEG) signal, high-speed rail dispatcher, fatigue state recognition, MobileNet V2 network, ResNet18 network, SoftMax regression

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