Prolonged Eyelid Closure Episodes during Sleep Deprivation in Professional Drivers

Alvaro, Pasquale K.; Jackson, Melinda L.; Berlowitz, David J.; Swann, Philip; Howard, Mark E. · 2016 · Crossref

DOI: 10.5664/jcsm.6044

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Summary

This study investigates the frequency and duration of prolonged eyelid closure episodes in professional drivers undergoing acute sleep deprivation. The research was motivated by the limitation of current automated drowsiness detection devices, which rely on averaged ocular metrics like blink duration. These averages may conceal the variability in eyelid closure episodes, potentially failing to detect the more prolonged closures that indicate severe drowsiness and high crash risk. The authors aimed to characterize these episodes to inform the development of more accurate real-time monitoring systems. The experimental design involved twenty male professional drivers who underwent 24 hours of continuous wakefulness. Participants completed a simulated driving task (AusEd), the Psychomotor Vigilance Task (PVT), and the Karolinska Sleepiness Scale (KSS) at seven intervals throughout the day. Eyelid closure episodes were manually analyzed from digital video recordings, with episodes defined as eyelid closure covering at least 80% of the pupil for one second or longer. Driving performance metrics, including lateral lane position, braking reaction time, and crashes, were recorded alongside subjective sleepiness and vigilance measures. Statistical analysis included Friedman tests to assess the effect of hours awake and Spearman correlations to link eyelid closure with performance outcomes. Results indicated that eyelid closure episodes were infrequent and short (1–3 seconds) during the first 14 hours of wakefulness. However, after 17 hours awake, the frequency and duration of these episodes increased significantly. By 20 hours of wakefulness, episodes lasting between 7 and 18 seconds became common. The median duration of eyelid closure per hour rose from zero seconds after 3 hours to 34 seconds after 23 hours. These prolonged eyelid closures were moderately to highly correlated with impaired driving performance, including increased standard deviation of lateral lane position, slower braking reaction times, more crashes, and reduced vigilance, as well as higher subjective sleepiness scores. The findings demonstrate that severe physiological impairment occurs during acute sleep deprivation, characterized by distinct, prolonged eyelid closures that are not captured by averaged data. The authors conclude that automated drowsiness detection devices must be capable of identifying these discrete, prolonged episodes to accurately assess risk during extended wakefulness. This has significant implications for road safety, suggesting that shift durations for professional drivers should be restricted to avoid driving after prolonged periods of wake, and that future device validation must account for the specific patterns of eyelid closure associated with severe sleep deprivation.

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Topics

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• drowsiness detection algorithms
• microsleep
• drowsiness
• sleep deprivation
• truck driver fatigue
• time on task

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software, measurement protocol