东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (6): 850-853.DOI: 10.12068/j.issn.1005-3026.2014.06.020

• 机械工程 • 上一篇    下一篇

基于无线体域网中多生理信号驾驶疲劳检测

付荣荣，王宏，王琳，张驰   

  1. (东北大学 机械工程与自动化学院， 辽宁 沈阳110819)
  • 收稿日期:2013-07-01 修回日期:2013-07-01 出版日期:2014-06-15 发布日期:2014-04-11
  • 通讯作者: 付荣荣
  • 作者简介:付荣荣(1985-)，女，辽宁鞍山人，东北大学博士研究生；王宏(1960-)，女，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61071057).

Detection of Driver Fatigue Based on Multiphysiological Signals in Wireless Body Area Network

FU Rongrong， WANG Hong， WANG Lin， ZHANG Chi   

  1. School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China.
  • Received:2013-07-01 Revised:2013-07-01 Online:2014-06-15 Published:2014-04-11
  • Contact: WANG Hong
  • About author:-
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摘要： 利用生理信号的无线测量设备实现了对驾驶员在驾驶过程中的脑电信号、肌电信号和呼吸信号的采集，并对其进行分析处理，从而实现驾驶员的疲劳检测.首先分别计算三个生理信号的近似熵并将其作为疲劳检测的特征参数，然后使用主成分分析对特征参数进行降维优化处理，同时对原始特征参数和分析后的主成分分别进行统计分析，基于优化处理后的特征参数利用回归方程建立驾驶疲劳估计模型.最后通过交叉验证对本方法进行评价，并使用数据融合方法给出了综合的评价结果.评价结果表明提出的方法对驾驶员疲劳状态的检测正确率达到90%以上.

关键词: 疲劳驾驶, 无线体域网, 脑电, 肌电, 呼吸

Abstract: Driver fatigue was detected using electroencephalograph， electromyography and respiration signals， which were collected wirelessly. The approximate entropies of the three signals were selected as features， and the reduction of feature dimensions was achieved by principle component analysis. Statistical analyses were then given to both original features and principle components and an evaluation model for driver fatigue was established using regression equation. The experimental results were evaluated by cross validation and the accuracy was more than 90% based on data fusion method. The results verify that the model is effective in detecting driver fatigue.

Key words: driver fatigue, wireless body area network, electroencephalograph, electromyography, respiration

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