ESTIMATE VIGILANCE IN DRIVING SIMULATION BASED ON DETECTION OF LIGHT DROWSINESS

Hong-Jun Liu, Qing-Sheng Ren, Hong-Tao Lu

Abstract

Avoiding fatal accidents caused by low vigilance level in driving is very important in our daily lives. Electroencephalography (EEG) has been proved very effective for measuring the level of vigilance. In this paper, we identify light drowsiness state from other states to estimate vigilance level decline by using support vector machine (SVM). Light drowsiness EEG is marked by alpha increasing to 50%. Alert EEG is marked by dominant beta activity and other EEG is labeled as sleep state. Samples of EEG data are trained in SVM program by using 4 features from each frequency band. Mutual information based feature selection method is used to reduce the dimension of features. The accuracy in classification of alert and light drowsiness reaches 91.5% on average.

References

  1. Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory (pp.144-152). ACM.
  2. Cortes, C. and Vapnik, V. (1995). Support-vector networks. In Machine Learning 1995 (vol.20,pp.273- 297). Springer.
  3. Doughty, M. J. (2002). Further assessment of genderand blink pattern-related differences in the spontaneous eyeblink activity in primary gaze in young adult humans. In Optometry and Vision Science (vol.79,pp.439-447). Williams Wilkins.
  4. Hori, T., Hayashi, M., and Morikawa, T. (1994). Topographical eeg changes and the hypnagogic experience. In Sleep onset: Normal and abnormal processes (pp.237-253). American Psychological Association.
  5. Lal, S. K. and Craig, A. (2001). A critical review of the psychophysiology of driver fatigue. In Biological Psychology (vol.55,pp.173-194). Elsevier.
  6. Li, M., Fu, J.-W., and Lu, B.-L. (2008). Estimating vigilance in driving simulation using probabilistic pca. In Engineering in Medicine and Biology Society 2008 (pp.5000-5003). IEEE.
  7. Lin, C.-T., Ko, L.-W., Chung, I.-F., Huang, T.-Y., Chen, Y.-C., Jung, T.-P., and Liang, S.-F. (2006). Adaptive eeg-based alertness estimation system by using icabased fuzzy neural networks. In IEEE Transactions on circuits and systems (vol.53,pp.2469-2476). IEEE.
  8. Makeig, S., Jung, T.-P., and Sejnowski, T. J. (1996). Using feedforward neural networks to monitor alertness from changes in eeg correlation and coherence. In Advances in Neural Information Processing Systems (pp.931-937). MIT Press.
  9. Niedermeyer, E. and Silva, F. L. D. (2004). Electroencephalography: basic principles, clinical applications, and related fields (pp.194-209). Lippincott Williams & Wilkins.
  10. Noachtar, S., Binnie, S., Ebersole, C., Mauguiere, J., Sakamoto, F., Westmoreland, A., and Westmoreland, B. (2004). A glossary of terms most commonly used by clinical electroencephalographers and proposal for the report form for the eeg findings. In Klinische Neurophysiologie (vol.35,pp.5-21). George Thieme Verlag.
  11. Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information: Criteria of maxdependency, max-relevance, and min-redundancy. In IEEE Transactions on Pattern Analysis and machine Intelligence (vol.27,pp.1226-1238). IEEE.
  12. Schomer, D. L. (2007). The Clinical Neurophysiology Primer (pp.57-71). Humana Press.
  13. Shen, K.-Q., Ong, C.-J., Li, X.-P., Hui, Z., and WilderSmith, E. P. V. (2007). A feature selection method for multilevel mental fatigue eeg classification. In IEEE Transactions on Biomedical Engineering (vol.54,pp.1231-1237). IEEE.
  14. Shi, L.-C., Yu, H., and Lu, B.-L. (2007). Semi-supervised clustering for vigilance analysis based on eeg. In Neural Networks 2007 (pp.1518-1523).
  15. Weinger, M. B. (1999). Vigilance, boredom, and sleepiness. In Journal of Clinical Monitoring and Computing (vol.15,pp.549-552). Kluwer.
  16. Yeo, M. V., Li, X., Shen, K., and Wilder-Smith, E. P. (2009). Can svm be used for automatic eeg detection of drowsiness during car driving? In Safety Science (vol.47,pp.115-124). Elsevier.
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Paper Citation


in Harvard Style

Liu H., Ren Q. and Lu H. (2010). ESTIMATE VIGILANCE IN DRIVING SIMULATION BASED ON DETECTION OF LIGHT DROWSINESS . In Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010) ISBN 978-989-674-019-1, pages 131-134. DOI: 10.5220/0002724201310134


in Bibtex Style

@conference{bioinformatics10,
author={Hong-Jun Liu and Qing-Sheng Ren and Hong-Tao Lu},
title={ESTIMATE VIGILANCE IN DRIVING SIMULATION BASED ON DETECTION OF LIGHT DROWSINESS},
booktitle={Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010)},
year={2010},
pages={131-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002724201310134},
isbn={978-989-674-019-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010)
TI - ESTIMATE VIGILANCE IN DRIVING SIMULATION BASED ON DETECTION OF LIGHT DROWSINESS
SN - 978-989-674-019-1
AU - Liu H.
AU - Ren Q.
AU - Lu H.
PY - 2010
SP - 131
EP - 134
DO - 10.5220/0002724201310134