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Title: Prediction of drowsiness events in night shift workers during morning driving
Austin Authors: Liang, Yulan;Horrey, William J;Howard, Mark E ;Lee, Michael L;Anderson, Clare;Shreeve, Michael S;O’Brien, Conor S;Czeisler, Charles A
Affiliation: Institute for Breathing and Sleep, Austin Health, Heidelberg, Victoria, Australia
Liberty Mutual Research Institute for Safety, Hopkinton, MA, USA
Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, Victoria, Australia
Sleep Health Institute and Division of Sleep and Medicine, Harvard Medical School, Boston, MA, USA
Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
Monash Institute of Cognitive and Clinical Neuroscience, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
Issue Date: May-2019 2017-11-07
Publication information: Accident; Analysis and Prevention 2019; 126: 105-114
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.
DOI: 10.1016/j.aap.2017.11.004
PubMed URL: 29126462
Type: Journal Article
Subjects: Drowsy driving
Predictive models
Electroencephalogram (EEG)
Driving performance
Infrared oculograph
Appears in Collections:Journal articles

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