Prediction of Drowsiness Events in Night Shift Workers During Morning Driving

Details

• Personal Author:
  Anderson C ; Czeisler, Charles A. ; Horrey WJ ; Howard ME ; Lee ML ; Liang Y ; O'Brien CS ; Shreeve MS
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Accidents, Occupational Automobiles Fatigue Occupational Health Safety Sleep
• Keywords:
  Accident Potential Accidents Author Keywords: Drowsy Driving Drivers Driving Performance EEG Electroencephalogram Infrared Oculograph Motor Vehicles Predictive Models Shift Workers Work Schedules

  More +
• ISSN:
  0001-4575
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Massachusetts ; OSHA Region 1
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  105-114
• Volume:
  126
• NIOSHTIC Number:
  nn:20068995
• Citation:
  Accid Anal Prev 2019 May; 126:105-114
• Contact Point Address:
  Yulan Liang, 157 Berkeley Street, T04C, Boston, MA 02116, USA
• Email:
  Liang.yulan@libertymutual.com
• Federal Fiscal Year:
  2019
• Performing Organization:
  Brigham and Women's Hospital, Boston, Massachusetts
• Peer Reviewed:
  True
• Start Date:
  20130901
• Source Full Name:
  Accident Analysis and Prevention
• End Date:
  20180831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:5a863e9f4c4fee164771370abce0ad06dbde5093709a089f2209a6fb5517cd7ffe97b6887675f2be23bdcc73f75d7ed8f799d00f80c7b7330bf16e4ca945c368
• Download URL:
  https://stacks.cdc.gov/view/cdc/207551/cdc_207551_DS1.pdf
• File Type:
  [PDF - 985.76 KB ]