应用支持向量机的眼睑参数疲劳预测

胡淑燕; 郑钢铁

应用支持向量机的眼睑参数疲劳预测

北京航空航天大学宇航学院, 北京 100191

基金项目: 欧盟SENSATION资助项目FP6(507231)

详细信息

作者简介:
胡淑燕(1982-),女,山东诸城人,博士生,huyan@sa.buaa.edu.cn.

中图分类号: U 491.31; TP 391.4
计量
- 文章访问数: 3282
- HTML全文浏览量: 63
- PDF下载量: 1156
- 被引次数: 0
出版历程
- 收稿日期: 2008-07-07
- 网络出版日期: 2009-08-31

Driver fatigue prediction with eyelid related parameters by support vector machine

School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

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.
- 支持向量机 /
- 眼电 /
- 疲劳预测
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.
- support vector machine /
- Electrooculography(EOG) /
- fatigue prediction

HTML全文

参考文献(1)

[1] Hamada T, Ito T, Adachi K, et al. Detecting method for drivers-drowsiness applicable to individual features [J]. IEICE Trans on Information and Systems,2004,E87-D (1):88-96 [2] Wierwille W W, Lewin M G, Fairbanks R J. Final reports: research on vehicle-based driver status/performance Monitoring, Part III . DOT HS 808 640, 1996 [3] Hayami T, Matsunaga K, Shidoji K, et al. Detecting drowsiness while driving by measuring eye movement-a pilot study In Proc 5th Int Conference on Intelligent Transportation Systems. USA: IEEE, 2002:156-161 [4] Ji Q, Yang X J. Real-time eye, gaze, and face pose tracking for monitoring driver vigilance [J]. Real-Time Imaging,2002, 8:357-377 [5] Liu X, Xu F L, Fujimura K. Real-time eye detection and tracking for driver observation under various light conditions IEEE Intelligent Vehicle Symposium. USA: IEEE, 2002:344-351 [6] Morris T L, Miller J C. Electrooculographic and performance indices of fatigue during simulated flight [J]. Biological Psychology, 1996,42:343-360 [7] Ji Q, Zhu Z Z,Lan P L. Real-time nonitrusive monitoring and prediction of driver fatigue [J]. IEEE Transactions on Vehicular Technology,2004, 53(4):1052-1068 [8] Caffier P P, Erdmann U,Ullsperger P. Experimental evaluation of eye-blink parameters as a drowsiness measure [J]. Eur J Appl Physiol,2003,89:319-325 [9] Burges C J C. A tutorial on support vector machines for pattern recognition [J]. Data Min Knowl Disc, 1998,2 (2):121-167 [10] Cristiannini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge: Cambridge University Press, 2000 [11] kerstedt T, Gillberg M. Subjective and objective sleepiness in the active individual [J]. International Journal of Neuroscience,1990,52:29-37 [12] Chang C C, Lin C J. LIBSVM: a library for support vector machines . 2001,Taipei:National Taiwom University, http://www.csie.ntu.edu.tw/cjlin/libsvm

施引文献

资源附件(0)

访问统计