Advertisement

The Assessment of Driver’s Arousal States from the Classification of Eye-Blink Patterns

  • Yoshihiro Noguchi
  • Keiji Shimada
  • Mieko Ohsuga
  • Yoshiyuki Kamakura
  • Yumiko Inoue
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5639)

Abstract

To realize the real-time assessment of driver’s arousal states, we propose the assessment method based on the analysis of eye-blink characteristics form image sequences. The driver’s arousal level while driving is not monotonous falling from high to low. We proposed the two-dimensional arousal states transition model which was taken into account the fact that a driver usually held out against sleepiness. The eye-blink pattern categories were classified from image sequence using HMM (Hidden Markov Model), then the driver’s arousal states were finally assessed using HMM by histogram distribution of those typical eye-blink categories. The arousal assessment results are also verified against the rating results by trained raters.

Keywords

arousal states drowsiness blink image EOG HMM driver 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hamada, T., Ito, T., Adachi, K., Nakano, T., Yamamoto, S.: Detecting method for drivers’ drowsiness applicable to individual features. Intelligent Transportation Systems 2, 1405–1410 (2003)Google Scholar
  2. 2.
    Miyakawa, T., Takano, H., Nakamura, K.: Development of non-contact real-time blink detection system for doze alarm. In: SICE 2004 Annual Conference, vol. 2, pp. 1626–1631 (2004)Google Scholar
  3. 3.
    Home, J.A., Reyner, L.A.: Driver Sleepiness. Sleep Monitoring, IEE Colloquium (1995)Google Scholar
  4. 4.
    Wierwille, W.W., Ellsworth, L.A.: Evaluation of Driver Drowsiness by Trained Raters. Accident, Analysis and Prevention 29(5), 571–581 (1994)CrossRefGoogle Scholar
  5. 5.
    Akerstedt, T., Gillberg, M.: Subjective and Objective Sleepiness in the Active Individual. Int. Journal of Neuroscience 52(1-2), 29–37 (1990)CrossRefGoogle Scholar
  6. 6.
    Noguchi, Y., Nopsuwanchai, R., Ohsuga, M., Kamakura, Y., Inoue, Y.: Classification of Blink Waveforms towards the Assessment of Drivers Arousal Levels - An Approach for HMM Based Classification from Blinking Video Sequence. In: Harris, D. (ed.) HCII 2007 and EPCE 2007. LNCS, vol. 4562, pp. 779–786. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Ohsuga, M., Kamakura, Y., Inoue, Y., Noguchi, Y., Nopsuwanchai, R.: Classification of Blink Waveforms toward the Assessment of Drivers Arousal Levels - An EOG Approach and the Correlation with Physiological Measures. In: [7], pp. 787–795Google Scholar
  8. 8.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yoshihiro Noguchi
    • 1
  • Keiji Shimada
    • 1
  • Mieko Ohsuga
    • 2
  • Yoshiyuki Kamakura
    • 2
  • Yumiko Inoue
    • 2
  1. 1.Information Technology Lab.AsahiKASEI Corp.KanagawaJapan
  2. 2.Biomedical EngneeringOsaka Institute of TechnologyOsakaJapan

Personalised recommendations