Eye-Blink Parameters Detect On-Road Track-Driving Impairment Following Severe Sleep Deprivation

Abstract

Study Objectives: Drowsiness leads to 20% of fatal road crashes, while inability to assess drowsiness has hampered drowsiness interventions. This study examined the accuracy of eye-blink parameters for detecting drowsiness related driving impairment in real time. Methods: Twelve participants undertook two sessions of 2-hour track-driving in an instrumented vehicle following a normal night's sleep or 32 to 34 hours of extended wake in a randomized crossover design. Eye-blink parameters and lane excursion events were monitored continuously. Results: Sleep deprivation increased the rates of out-of-lane driving events and early drive terminations. Episodes of prolonged eyelid closures, blink duration, the ratio of amplitude to velocity of eyelid closure, and John's Drowsiness Score (JDS, a composite score) were also increased following sleep deprivation. A timeon-task (drive duration) effect was evident for out-of-lane events rate and most eye-blink parameters after sleep deprivation. The JDS demonstrated the strongest association with the odds of out-of-lane events in the same minute, whereas measures of blink duration and prolonged eye closure were stronger indicators of risk for out-of-lane events over longer periods of 5 minutes and 15 minutes, respectively. Eye-blink parameters also achieved moderate accuracies (specificities from 70.12% to 84.15% at a sensitivity of 50%) for detecting out-of-lane events in the same minute, with stronger associations over longer timeframes of 5 minutes to 15 minutes. Conclusions: Eyelid closure parameters are useful tools for monitoring and predicting drowsiness-related driving impairment (out-of-lane events) that could be utilized for monitoring drowsiness and assessing the efficacy of drowsiness interventions.

On-road driving impairment following sleep deprivation differs according to age

“…Despite ocular metrics being a strong indicator of drowsiness 35 , only the younger adults demonstrated increased ocular indices of drowsiness, including increased blink duration, LEC rate and PERCLOS during their sleep-deprived drive. Sleep loss related increases in ocular metrics have been well described in young drivers 14 , and are reliable and sensitive measures of drowsiness related driving impairment in younger drivers 36 . In contrast, in highly-controlled laboratory studies, older adults have shown a smaller increase in ocular ‘drowsy’ metrics and were less likely to fall asleep, consistent with our findings 11 .…”

Blink rate measuredin situdecreases while reading from printed text or digital devices, regardless of task duration, complexity or working distance

“…Incomplete blinking has been reported as an important marker of ocular symptoms during reading on smartphone and computer [9, 11, 77] and also during driving [82]. Future studies using the wearable eye tracker will enable examination of blink amplitude in situ during various task and conditions.…”

Modeling and Analysis of Fatigue Detection With Multi-channel Data Fusion

“…The energy features of individual brain waves can thus be obtained.Omerbodzic et al [16] have verified that the EEG of epileptic patients can be classified by using energy signals in the time domain, and Zhong et al [17] have validated this in a large number of classification models using a public data set and obtained results ranging from 60.50% to 94.24% with different classification accuracies, further demonstrating that that energy features using EEG signals can be treated as task features for classification.…”