Abstract
To deal with the problem of detecting driver fatigue, we propose a vigilance estimation method using electrooculogram (EOG) and investigate the correlation between multiple EOG features and vigilance. We examine four kinds of features extracted from EOG: slow eye movement (SEM), saccade, blink, and EOG energy. First, we introduce three eye movement detection algorithms to identify SEMs, saccades, and blinks from EOGs. Then we define and extract twenty different features from those eye movements. Second, the features are processed by a linear dynamic system (LDS) approach, which can effectively remove noises and some EOG components that are irrelevant to vigilance. Finally, we analyze the de-noised features with the vigilance reference obtained from a monotonous visual task. Our experimental results on an EOG data set of twentytwo subjects indicate that combination of SEMs, saccades, blinks, and energy features of EOGs has a high correlation coefficient with the vigilance references, up to the average 0.75. This work provides a potent support to developing vigilance estimation system based on multiple EOG features for detecting driver fatigue.
Keywords: Driver fatigue, EOG, feature extraction, machine learning, vigilance.
Graphical Abstract
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