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• W2886857653 abstract "The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification." @default.
• W2886857653 created "2018-08-22" @default.
• W2886857653 creator A5007258873 @default.
• W2886857653 creator A5033024496 @default.
• W2886857653 creator A5036681394 @default.
• W2886857653 creator A5082647026 @default.
• W2886857653 date "2019-01-01" @default.
• W2886857653 modified "2023-10-16" @default.
• W2886857653 title "Automatic driver sleepiness detection using EEG, EOG and contextual information" @default.
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