Abstract
Currently, driver drowsiness detectors using video based technology is being widely studied. Eyelid closure degree (ECD) is the main measure of the video-based methods, however, drawbacks such as brightness limitations and practical hurdles such as distraction of the drivers limits its success. This study presents a way to compute the ECD using EEG sensors instead of video-based methods. The premise is that the ECD exhibits a linear relationship with changes of the occipital EEG. A total of 30 subjects are included in this study: ten of them participated in a simple proof-of-concept experiment to verify the linear relationship between ECD and EEG, and then twenty participated in a monotonous highway driving experiment in a driving simulator environment to test the robustness of the linear relationship in real-life applications. Taking the video-based method as a reference, the Alpha power percentage from the O2 channel is found to be the best input feature for linear regression estimation of the ECD. The best overall squared correlation coefficient (SCC, denoted by r2) and mean squared error (MSE) validated by linear support vector regression model and leave one subject out method is r2 = 0.930 and MSE = 0.013. The proposed linear EEG-ECD model can achieve 87.5% and 70.0% accuracy for male and female subjects, respectively, for a driver drowsiness application, percentage eyelid closure over the pupil over time (PERCLOS). This new ECD estimation method not only addresses the video-based method drawbacks, but also makes ECD estimation more computationally efficient and easier to implement in EEG sensors in a real time way.
Highlights
Driver drowsiness is one of the major causes of mortality in traffic accidents worldwide
According to the documents published by the US Federal Highway Administration (FHWA) [6,7], the measurement of high sensitive PERCLOS is given by Equation (1), where Eyelid closure degree (ECD) refers to Eyelid Closure Degree: PERCLOS =
Power spectra that vary with the five ECD segments are illustrated in Figure 9, where we can see clearly that the β power which is in the range of 12~30 Hz is gradually decayed as the ECD increases, while the α power, which is in the 8–12 Hz range, is increasing as the ECD increases
Summary
Driver drowsiness is one of the major causes of mortality in traffic accidents worldwide. From 2010 to 2013, 1223 people died in highway traffic accidents in Korea, and 31% of them died in accidents related to driver drowsiness [2,3]. One of the milestones in the monitoring of driver drowsiness is the usage of percentage eyelid closure over the pupil over time (PERCLOS) to give a warning if driving whilst drowsy is determined. PERCLOS is a video-based driver drowsiness monitoring technology. It assesses drowsiness by measuring slow eyelid closure and estimating the proportion of time for which the eyes are closed over specified time intervals.
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