Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Electroencephalogram-based Driver Drowsiness Detection System Using AR Coefficients and SVM

AR계수와 SVM을 이용한 뇌파 기반 운전자의 졸음 감지 시스템

  • Han, Hyungseob (School of Electrical Engineering, University of Ulsan) ;
  • Chong, Uipil (School of Electrical Engineering, University of Ulsan)
  • Received : 2012.08.15
  • Accepted : 2012.10.29
  • Published : 2012.12.25
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Abstract

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

운전 중 운전자의 졸음은 교통 사망사고를 일으키는 중요한 요인이며 음주운전보다도 더 위험할 수 도 있다. 이러한 이유로 운전자의 졸음을 판별하고 경고하는 시스템 개발이 최근에 매우 중요한 이슈로 떠올랐다. 그중에서도 졸음과 가장 밀접한 관련이 있는 생체 신호 분석이 많이 적용되는데 그중에서도 뇌파(Electroencephalogram, EEG)와 안구전도(Electrooculogram, EOG)를 분석하는 연구가 주류를 이루고 있다. 본 논문에서는 실험 프로토콜를 바탕으로 측정된 뇌파를 주파수별로 분석하여 운전자의 상태별 뇌파 데이터베이스를 구축하였고 선형예측(Linear Predictive Coding, LPC) 계수와 Support Vector Machine(SVM)을 이용한 운전자 졸음 감지 시스템을 제안한다. 실험결과로 졸음의 뇌파분석에서 알파파가 감소하며 세타파가 증가하는 추세를 보였으며, LPC 계수가 각성, 졸음 및 수면상태의 특징을 잘 반영하였다. 특히 제안한 시스템은 적은 샘플(250ms)에서도 96.5%의 높은 분류 결과를 얻어 짧은 순간에 일어날 운전시 돌발 상황을 실시간으로 예측할 수 있는 가능성을 보였다.

Keywords

Acknowledgement

Supported by : 울산대학교

References

  1. Qiang Ji, Zhiwei Zhu, and Peilin Lan, "Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue," IEEE Trans. on Vehicular Technology, vol. 53 no. 4, pp. 1052-1068, 2004. https://doi.org/10.1109/TVT.2004.830974
  2. J. D. Slater, "A definition of drowsiness: One purpose for sleep?," Med. Hypotheses, vol. 71, pp. 641-644, 2008. https://doi.org/10.1016/j.mehy.2008.05.035
  3. M. W. Johns, R. Chapman, K. Crowley, and A. Tucker, "A new method for assessing the risks of drowsiness while driving," Somnologie, vol. 12, pp. 66-74, 2008. https://doi.org/10.1007/s11818-008-0330-3
  4. Young Hoon Joo, Jin Kyu Kim, In Ho Ra, "Intelligent Drowsiness Drive Warning System," Journal of Korean Institute of Intelligent Systems, vol. 18, no. 2, pp. 223-229, 2008. https://doi.org/10.5391/JKIIS.2008.18.2.223
  5. Kataoka et al., 1998 H. Kataoka, H. Yoshida, A. Saijo, M. Yasuda and M. Osumi, "Development of a skin temperature measuring system for non-contact stress evaluation," Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 940-943, 1998.
  6. A. Bunde, S. Havlin, J.W. Kantelhardt, T. Penzel, J.-H. Peter and K. Voigt., "Correlated and Uncorrelated Regions in Heart-Rate Fluctuations during Sleep," Phys. Rev. Lett., vol. 85, pp. 3736-3739, 2000. https://doi.org/10.1103/PhysRevLett.85.3736
  7. Young-Bum Lee, Myoung-Ho Lee, "Automobile System for Drowsiness Accident Detection using EDA Signal Analysis," The Transactions of KIEE, vol. 56, no. 2, pp. 227-450, 2007.2.
  8. Mervyn V.M. Yeo, Xiaoping Li, Kaiquan Shen, Einar P.V. Wilder-Smith. "Can SVM be used for automatic EEG detection of drowsiness during car driving?," Safety Science, vol. 47 pp. 115-116, 2009. https://doi.org/10.1016/j.ssci.2008.01.007
  9. Steriade, M., "Brain electrical activity and sensory processing during waking and sleep states," In: Kryger, M.H., Roth, T., Dement,W.C. (Eds.), Principles and Practice of Sleep Medicine. Saunders, Philadelphia, pp. 93-111, 2000.
  10. Lal, S.K.L., Craig, A. "A critical review of the psychophysiology of driver fatigue," Biological Psychology, vol. 55, pp. 173-94, 2001. https://doi.org/10.1016/S0301-0511(00)00085-5
  11. Yeo, M.V.M., Li, X., Wilder-Smith, E.P.V., "Characteristic EEG differences between voluntary recumbent sleep onset in bed and involuntary sleep onset in a driving simulator," Clinical Neurophysiology, vol. 118, pp. 1315-1323, 2007. https://doi.org/10.1016/j.clinph.2007.02.001
  12. Moller, H.J., Kayumov, L., Bulmash, E.L., Nhan, J., Shapiro, C.M., "Simulator performance, microsleep episodes, and subjective sleepiness: normative data using convergent methodologies to assess driver drowsiness," Journal of Psychosomatic Research, vol. 61, pp. 335-342, 2006. https://doi.org/10.1016/j.jpsychores.2006.04.007
  13. Murray W. Johns, "A new method for measuring daytime sleepiness: the Epworth sleepiness scale," Sleep, vol. 14, no. 6, pp. 540-545, 1991.
  14. J. D. Markel and A. H. Gray, Jr., "Linear Prediction of Speech," Springer-Verlag, Berlin Heidelberg, New York, pp. 42-53, 1976.
  15. R. Debnath, N. Takahide and H. Takahashi, "A decision based one-against-one method for multi-class support vector machine," Pattern Analysis & Applications, vol. 7, no. 2, pp. 164-175, 2004.
  16. Chih-Wei Hsu and Chih-Jen Lin, "A Comparison of Methods for Multiclass Support Vector Machine," IEEE TRANSACTIONS ON NEURAL NETWORKS, vol. 13, no. 2, 2002.
  17. Man-Sun Kim, Sang-Yong Lee, "Multiple SVM Classifier for Pattern Classification in Data Mining," Journal of Korean Institute of Intelligent Systems, vol. 15, no. 3, pp. 289-293, 2005. https://doi.org/10.5391/JKIIS.2005.15.3.289
  18. Sang-Wook Seo, Hyun-Chang Yang, Kwee-Bo Sim, "Behavior Learning and Evolution of Swarm Robot System using Support Vector Machine," Journal of Korean Institute of Intelligent Systems, vol. 18, no. 5, pp. 712-717, 2008. https://doi.org/10.5391/JKIIS.2008.18.5.712
  19. Hyungseob Han, Dajung Kim, Daeyun An, Gi Beom Hong, Uipil Chong, "Driver Drowsiness and Alertness Detection Method Using Linear Predictive Coding and Electroencephalographic Change," Proceedings of KISPS Falls Conference 2011, pp. 237-239, Dec. 2011.

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