Earticle

초록

영어

Driving fatigue is a common occupational hazard for any long distance or professional driver, and fatigue detecting has major implications for transportation safety. Monitoring physiological signal while driving can provide the possibility to detect the fatigue and give the necessary warning. In this paper, fifty subjects participated in driving simulations experiment with their recorded EEG signals to induce two kinds of fatigue states: Alert and drowsy. Two nonlinear methods, approximate Entropy (AE) and Sample Entropy (SE), were used to characterize irregularity and complexity of EEG data. Subsequently Support Vector Machine (SVM) was applied to classify these two fatigue states. The experimental result shows that two complexity parameters are significantly decreased as the fatigue level increases. The result indicates that both of two nonlinear indicators can be used to characterize driver fatigue level. Furthermore, the combined measure feature results in higher classification accuracy, indicating the proposed classification method is more robust and effective, compared with single complexity measure.

목차

Abstract
 1. Introduction
 2. Materials and Methods
  2.1. Subject
  2.2. Data Acquisition
  2.3. Driving Simulation Task
  2.4. Data Preprocessing
  2.5. Feature Extraction
  2.6. Classification
 3. Result
 4. Discussion
 Acknowledgements
 References

키워드

저자

• Yijun Xiong [ College of Mechanical and Electrical Engineering, Wuhan Donghu University, Wuhan 430212, China ]
• Junfeng Gao [ Key Laboratory of cognitive science (South-Central University for Nationalities), State Ethnic Affairs Commission, Wuhan 430074, China ]
• Yong Yang [ School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China ]
• Xiaolin Yu [ Department of Information Engineering, Officer College of Armed Police Force, Chengdu 611731, China ]
• Wentao Huang [ School of Mathematics, Physics & Information Science, Zhejiang Ocean University, Zhejiang 316022, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605