Abstract
With recent advances in image recognition, the assessment of driver’s arousal level using blinking image sequences has been expected. In this paper, we demonstrated the possibility of assessing driver’s arousal level by analyzing blinking image sequences. We focused on some typical blink waveform patterns occurred under drowsy condition. We used the results of EOG (Electro-occulogram) waveform clustering as the baseline for HMM (Hidden Markov Model) blinking labeling due to the difficulty of defining blinking labels from blinking image sequence. The blink pattern classes were classified by using the HMMs based on blinking image sequences. The driver’s arousal level was finally estimated by histogram variation per minute of those typical blink classes.
Chapter PDF
Similar content being viewed by others
References
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)
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)
Home, J.A., Reyner, L.A.: Driver Sleepiness. Sleep Monitoring, IEE Colloquium (1995)
Akerstedt, T.: Subjective and Objective Sleepiness in the Active Individual. Intern. J. Neuroscience 52, 29–37 (1990)
Ohsuga, M., Kamakura, Y., Inoue, Y., Noguchi, Y., Nopsuwanchai, R.: Classification of blink waveforms toward the assessment of driver’s arousal levels - An EOG approach and the correlation with physiological measures. Intern. Conf. Human-Computer Interaction (2007)
Bekiaris, E.D., Nikolaou, S.I.: Towards the development of design guidelines handbook for driver hypovigilance detection and warning – the AWAKE approach. In: RTIC 2004, 12th IEE International Conference, pp.314–320 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Noguchi, Y., Nopsuwanchai, R., Ohsuga, M., Kamakura, Y. (2007). Classification of Blink Waveforms Towards the Assessment of Driver’s Arousal Level - An Approach for HMM Based Classification from Blinking Video Sequence. In: Harris, D. (eds) Engineering Psychology and Cognitive Ergonomics. EPCE 2007. Lecture Notes in Computer Science(), vol 4562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73331-7_85
Download citation
DOI: https://doi.org/10.1007/978-3-540-73331-7_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73330-0
Online ISBN: 978-3-540-73331-7
eBook Packages: Computer ScienceComputer Science (R0)