Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
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Neural-network-based Driver Drowsiness Detection System Using Linear Predictive Coding Coefficients and Electroencephalographic Changes

선형예측계수와 뇌파의 변화를 이용한 신경회로망 기반 운전자의 졸음 감지 시스템

  • Received : 2012.04.25
  • Accepted : 2012.08.01
  • Published : 2012.07.30
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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 neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP 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 신호인 뇌파 (Electroencephalogram, EEG)와 안구전도 (Electrooculogram, EOG)를 분석하는 연구가 주류를 이루고 있다. 본 논문에서는 실험 프로토콜에 의거하여 측정된 뇌파를 주파수별로 분석하여 운전자의 상태별 뇌파 데이터베이스를 구축하고 선형예측(Linear Predictive coding, LPC) 계수를 특징벡터로 한 신경회로망 기반 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)을 가지고도 96.5%라는 높은 분류 결과를 얻어 짧은 순간에 일어날 수 있는 운전 시 돌발 상황을 실시간으로 검출 가능성을 확인하였다.

Keywords

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, 7, 2004. https://doi.org/10.1109/TVT.2004.830974
  2. J. D. Wu, and T. R. Chen, "Development of a drowsiness warning system based on the fuzzy logic images analysis", Expert Systems with Applications, Vol. 34, pp. 1556-1561, 2008. https://doi.org/10.1016/j.eswa.2007.01.019
  3. Kataoka et al., 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.
  4. 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., 85, p. 3736 -3739, 2000. https://doi.org/10.1103/PhysRevLett.85.3736
  5. 이영범, 이명호 "피부 임피던스 신호 분석을 통한 졸음운전 판단 시스템 개발," 전기학회논문지, 제56권 제2호, pp. 227-450, 2007.2.
  6. Mervyn V.M. Yeo, Xiaoping Li, Kaiquan Shen, Einar P.V. Wilder-Smith. "Can SVM be used for automatic EEG detection of drowsinessduring car driving?", Safety Science, vol. 47 pp. 115 -116, 2009. https://doi.org/10.1016/j.ssci.2008.01.007
  7. Mast, T. M., Jones, H.V., Heimstra, N.W., "Effects of fatigue on performance in a driving device," Highway Research Record, Vol. 122, 93(Abridgement) 1966.
  8. Brown, I. D., "Car driving and fatigue," Triangle (Sandoz Journal of Medical Science), vol. 8, pp. 131-137, 1967
  9. Dureman, E.I., Boden, C., "Fatigue in simulated car driving", Ergonomics, vol. 15, pp.299 -308, 1972. https://doi.org/10.1080/00140137208924432
  10. Chase, M.H.,. "Brain electrical activity and sensory processing during waking and sleep states," In: Kryger, M.H., Roth, T.H., Dement, W.C.(Eds.), Principles and Practice of Sleep Medicine. Saunders, New York, pp. 93-111, 2000.
  11. 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
  12. 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
  13. 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
  14. Eoh, H.J., Chung, M.K., Kim, S-H, "Electroencephalographic study of drowsiness in simulated driving with sleep deprivation". International journal of Industrial Ergonomics, vol. 35, pp. 307-320, 2005. https://doi.org/10.1016/j.ergon.2004.09.006
  15. Y. M. Zhan and A.K.S. Jardine, "Adaptive autoregressive modeling of non-stationary vibration signals under distinct gear states. Part 1: modeling", Journal of Sound and Vibration, Vol. 286, No. 3, pp. 429-450, 2005. https://doi.org/10.1016/j.jsv.2004.10.024
  16. J. D. Markel and A. H. Gray, Jr., "Linear Prediction of Speech", Springer-Verlag, Berlin Heidelberg, New York, pp. 42-53, 1976.
  17. Johns, M.W., "A new method for measuring daytime sleepiness: the Epworth sleepiness scale", Sleep 14, pp. 540-546, 1991.
  18. Richard P. Lippmann, "Pattern Classification Using Neural Network," IEEE Communication Magazine, pp. 47-50, 1989.
  19. Simon Haykin, "Neural Networks: A Comprehensive Foundation," Prentice-Hall, New Jersey, pp. 156-248, 1999.