Citation

Liang Y, Horrey WJ, Howard ME, Lee ML, Anderson C, Shreeve MS, O'Brien CS, Czeisler CA. Accid. Anal. Prev. 2019; 126: 105-114.

Affiliation

Sleep Health Institute and Division of Sleep and Medicine, Harvard Medical School, 164 Longwood Ave., Room 106, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, 75 Francis St., Boston, MA 02115, USA.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.aap.2017.11.004

PMID

29126462

Abstract

The morning commute home is an especially vulnerable time for workers engaged in night shift work due to the heightened risk of experiencing drowsy driving. One strategy to manage this risk is to monitor the driver's state in real time using an in vehicle monitoring system and to alert drivers when they are becoming sleepy. The primary objective of this study is to build and evaluate predictive models for drowsiness events occurring in morning drives using a variety of physiological and performance data gathered under a real driving scenario. We used data collected from 16 night shift workers who drove an instrumented vehicle for approximately two hours on a test track on two occasions: after a night shift and after a night of rest. Drowsiness was defined by two outcome events: performance degradation (Lane-Crossing models) and electroencephalogram (EEG) characterized sleep episodes (Microsleep Models). For each outcome, we assessed the accuracy of sets of predictors, including or not including a driver factor, eyelid measures, and driving performance measures. We also compared the predictions using different time intervals relative to the events (e.g., 1-min prior to the event through 10-min prior). By examining the Area Under the receiver operating characteristic Curve (AUC), accuracy, sensitivity, and specificity of the predictive models, the results showed that the inclusion of an individual driver factor improved AUC and prediction accuracy for both outcomes. Eyelid measures improved the prediction for the Lane-Crossing models, but not for Microsleep models. Prediction performance was not changed by adding driving performance predictors or by increasing the time to the event for either outcome. The best models for both measures of drowsiness were those considering driver individual differences and eyelid measures, suggesting that these indicators should be strongly considered when predicting drowsiness events. The results of this paper can benefit the development of real-time drowsiness detection and help to manage drowsiness to avoid related motor-vehicle crashes and loss.

Copyright © 2017 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Driving performance; Drowsy driving; Electroencephalogram (EEG); Fatigue; Infrared oculograph; Predictive models