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�������պ����ѧѧ�� 2009, Vol. 35 Issue (8) :929-932    DOI:
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Driver fatigue prediction with eyelid related parameters by support vector machine
Hu Shuyan, Zheng Gangtie*
School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

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Abstract�� Various investigations show that drivers- drowsiness is one of the main causes of traffic accidents. Thus, countermeasure device which should be able to predict the accidents accurately with low ratio of false alarms is currently required in many fields for sleepiness related hazard prevention. Drowsiness prediction was conducted by support vector machine (SVM) with eyelid related parameters extracted from the electrooculography(EOG) data collected in a driving simulator. 25 sleep-deprived subjects which hit the rumbles while driving in the experiment were selected based on the karolinska sleepiness scale (KSS) to make sure they were alert as they started driving and sleepy when the hits occurred, and then they were divided into training set including 20 subjects and validation set including the other 5 subjects. The validation results show that the hits can be successfully predicted at least five minutes ago by our SVM model.
Keywords�� support vector machine   Electrooculography(EOG)   fatigue prediction     
Received 2008-07-07;


About author: ������(1982-),Ů,ɽ�������,��ʿ��,huyan@sa.buaa.edu.cn.
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Hu Shuyan, Zheng Gangtie.Driver fatigue prediction with eyelid related parameters by support vector machine[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2009,V35(8): 929-932
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