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PERCLOS-based technologies for detecting drowsiness: current evidence and future directions.

Takashi Abe1

  • 1International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan.

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|May 16, 2023
PubMed
Summary

The percentage of eye closure (PERCLOS) effectively detects drowsiness, but its sensitivity varies. Future research should standardize PERCLOS and integrate it with other measures for comprehensive drowsiness detection to prevent accidents.

Keywords:
PERCLOSalertnessdrowsinessdrowsy drivingmonitoringpsychomotor vigilance testsleepinessslow eyelid closurevigilant attention

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Area of Science:

  • Neuroscience
  • Human Factors Engineering
  • Sleep Medicine

Background:

  • Drowsiness is a significant risk factor for accidents and human error, often linked to sleep loss and circadian rhythm disruption.
  • The Percentage of Closure (PERCLOS) is a validated metric for passive drowsiness detection, showing increases with sleep deprivation and during specific tasks.
  • Limitations exist where PERCLOS may not accurately reflect drowsiness in certain populations (e.g., older adults) or task contexts (e.g., aviation).

Purpose of the Study:

  • To review the current evidence on PERCLOS as a drowsiness detection index.
  • To identify limitations and suggest future research directions for improving drowsiness detection technologies.
  • To explore the potential of PERCLOS-based technology in preventing accidents.

Main Methods:

  • This study is a narrative review of published evidence on PERCLOS and drowsiness detection.
  • Analysis of existing research on PERCLOS's effectiveness across various conditions and tasks.
  • Identification of gaps and recommendations for future research and technological development.

Main Results:

  • PERCLOS is a sensitive index for drowsiness, particularly during vigilance and driving tasks, and is affected by sleep deprivation.
  • PERCLOS's effectiveness can be limited in moderate drowsiness, older adults, and specific operational tasks.
  • No single index, including PERCLOS alone, is currently optimal for detecting all forms of driving impairment.

Conclusions:

  • Standardization of PERCLOS definition and extensive validation of PERCLOS-based technology are crucial.
  • Integrating PERCLOS with other behavioral and physiological indices is necessary for comprehensive drowsiness detection.
  • Further validation studies in real-world settings and for specific populations (e.g., sleep disorders) are recommended to enhance safety.