Eye Movement Detection for Assessing Driver Drowsiness by Electrooculography

Many studies show that driver drowsiness is one of the main reasons for road accidents. To prevent such car crashes, systems are needed to monitor and characterize the driver based on the driving information. In order to have highly reliable assistant systems, reference drowsiness measurements are required. Among different physiological measures, previous studies have introduced driver eye movements, particularly blinking, as a measure with high correlation to drowsiness. Hence, in this study, eye movements of 14 drivers have been observed using electrooculography (EOG) at the moving-base driving simulator of Mercedes Benz to assess driver drowsiness. Based on the measured signals, an adaptive detection approach is introduced to simultaneously detect not only eye blinks, but also other driving-relevant eye movements such as saccades and micro sleep events. Moreover, in spite of the fact that drowsiness influences eye movement patterns, the proposed algorithm distinguishes between the often-confused driving-related saccades and decreased amplitude blinks of a drowsy driver. The evaluation of results shows that the presented detection algorithm outperforms common methods so that eye movements are detected correctly during both awake and drowsy phases.