Classification of blink waveforms towards the assessment of driver's arousal level - an approach for HMM based classification from blinking video sequence

Authors:
Yoshihiro Noguchi;Roongroj Nopsuwanchai;Mieko Ohsuga;Yoshiyuki Kamakura
Affiliations:
Information Technology Lab., AsahiKASEI Corp., Atsugi-shi, Kanagawa, Japan;Information Technology Lab., AsahiKASEI Corp., Atsugi-shi, Kanagawa, Japan;Biomedical Engneering, Osaka Institute of Technology, Asahi-ku, Osaka, Japan;Biomedical Engneering, Osaka Institute of Technology, Asahi-ku, Osaka, Japan
Venue:
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
Year:
2007

Citing 1
Cited 2

Classification of blink waveforms toward the assessment of driver's arousal levels - an EOG approach and the correlation with physiological measures

EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics

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

EPCE '09 Proceedings of the 8th International Conference on Engineering Psychology and Cognitive Ergonomics: Held as Part of HCI International 2009
Classification of blink waveforms toward the assessment of driver's arousal levels - an EOG approach and the correlation with physiological measures

EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics

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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.