Abstract
Drowsiness is one of the main contributors to road crashes. This research program examines the utility of drowsiness detection based on singular and hybrid approaches using physiological signals of EEG, EOG, and ECG. Four supervised machine learning models were developed to detect drowsiness levels, using physiological features known to be associated with drowsiness and performance impairment. The ground truth was subjective sleepiness responses while performing a repetitive reaction time task. The outcome of the study indicates that the selected features provided higher performance in the hybrid approaches than the singular approaches, which could be useful for future research implications.
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