| 应用支持向量机的眼睑参数疲劳预测 |
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| 引用本文： | 胡淑燕,郑钢铁.应用支持向量机的眼睑参数疲劳预测[J].北京航空航天大学学报,2009,35(8):929-932. |
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| 作者姓名： | 胡淑燕 郑钢铁 |
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| 作者单位： | 北京航空航天大学,宇航学院,北京,100191;北京航空航天大学,宇航学院,北京,100191 |
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| 基金项目： | 欧盟SENSATION资助项目 |
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| 摘 要： | 研究表明疲劳驾驶是引起交通事故的重要原因之一,因此有必要采取预防措施,而能够提前对事故进行准确预报并保证低误报警率是问题的关键所在.提出了利用多眼睑运动特征参数建立支持向量机模型进行疲劳预测的方法,其中眼睑运动特征参数是从驾驶模拟器上采集的眼电信号提取出的.根据Karolinska睡眠等级选出25名缺乏睡眠并在实验中撞到振动带的驾驶员,保证其开始驾驶阶段是警觉的,而事故发生阶段是疲劳的,然后将20名驾驶员作为训练对象,另5名驾驶员作为验证对象.结果表明,所用的方法可以提前至少5 min对由疲劳导致的事故进行预报.
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| 关 键 词： | 支持向量机 眼电 疲劳预测 |
| 收稿时间： | 2008-07-07 |
Driver fatigue prediction with eyelid related parameters by support vector machine |
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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 Astronautics,2009,35(8):929-932. |
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| Authors: | Hu Shuyan Zheng Gangtie |
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| Institution: | 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... |
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| Keywords: | support vector machine Electrooculography( EOG) fatigue prediction |
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