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.
CITATION STYLE
Harris, D. (2017). Engineering Psychology and Cognitive Ergonomics: Performance, Emotion and Situation Awareness. Engineering Psychology and Cognitive Ergonomics, 10275, 220–231. Retrieved from http://link.springer.com/10.1007/978-3-319-58472-0
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