PERCLOS: A Valid Psychophysiological Measure of Alertness As Assessed by Psychomotor Vigilance

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Sleep and Work-Related Fatigue Human-Automation Interaction and Safety Mind wandering and attention

Abstract

This Tech Brief summarizes an Intelligent Transportation System (ITS) study titled Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and as the Basis for Alertness Management. The study was funded in part by FHWA’s Office of Motor Carriers and managed by the National Highway Traffic Safety Administration (NHTSA). The project’s goal was to evaluate the validity and reliability of several drowsiness-detection measures and technologies in a controlled laboratory setting, and to analyze the effects of alerting stimuli on drivers’ alertness levels. Of the drowsiness-detection measures and technologies evaluated in this study, the measure referred to as “PERCLOS” was found to be the most reliable and valid determination of a driver’s alertness level. PERCLOS is the percentage of eyelid closure over the pupil over time and reflects slow eyelid closures (“droops”) rather than blinks. A PERCLOS drowsiness metric was established in a 1994 driving simulator study as the proportion of time in a minute that the eyes are at least 80 percent closed. (Wierwille et al., 1994) Based on research by Wierwille and colleagues (1994), FWHA and NHTSA consider PERCLOS to be among the most promising known real-time measures of alertness for in-vehicle drowsiness-detection systems. The results of this research support the development of a “first-ever” real-time drowsiness detection sensor that would measure the percentage of eyelid closure over the pupil, over time (i.e., PERCLOS)\n

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2014EOG-based drowsiness detection using convolutional neural networks 2018A contextual and temporal algorithm for driver drowsiness detection 2012Driver’s eye blinking detection using novel color and texture segmentation algorithms 2020Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation 2019Comprehensive Drowsiness Level Detection Model Combining Multimodal Information 2015Detecting Driver Drowsiness: A survey of system designs and technology 2022Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets 2010Drowsiness detection based on visual signs: blinking analysis based on high frame rate video

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