The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness

Jackson, Melinda L.; Kennedy, Gerard; Clarke, Catherine A. M.; Gullo, Melissa; Swann, Philip; Downey, Luke A.; Hayley, Amie C.; Pierce, Robert J; Howard, Mark

doi:10.1016/j.aap.2015.11.033

Cited by 57 publications

(54 citation statements)

References 30 publications

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“…Recently, devices that measure drowsiness through eyelid parameters have become commercially available. These measures are accurate at measuring drowsiness and drowsiness‐related errors or crashes in laboratory (Jackson et al, ; Johns, Chapman, Crowley, & Tucker, ; Wilkinson et al, ) and field settings (Ftouni et al, ; Lee et al, 2016; Liang et al, in press). This allows for real‐time on‐road monitoring of drowsiness levels with alerts to drivers if they reach a level indicative of severe impairment that is unacceptable for driving.…”

Section: Introductionmentioning

confidence: 99%

“…Eyelid parameters are good indicators of drowsiness in the laboratory (Jackson et al, ; Johns et al, ; Schleicher, Galley, Briest, & Galley, ; Wierwille & Ellsworth, ), with the ability to detect impairment leading to drowsiness‐related errors (Anderson, Chang, Sullivan, Ronda, & Czeisler, ; Jackson, Kennedy, et al, ; Wilkinson et al, ). In particular, eyelid closure duration and the ratio of amplitude to velocity of eyelid movements are accurate indicators of drowsiness‐related psychomotor errors (Wilkinson et al, ) and have the potential to be used to monitor for drowsiness in real‐life situations, such as driving (Johns et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the validity of eyelid parameters to detect impairment due to benzodiazepines

Wilkinson

Jackson

Westlake

et al. 2020

Human Psychopharmacology

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Objective Benzodiazepines impair driving ability and psychomotor function. Eyelid parameters accurately reflect drowsiness; however, the effects of benzodiazepines on these measures have not been extensively studied. The aim of this study was to investigate the effect of benzodiazepines on eyelid parameters and evaluate their accuracy for detecting psychomotor impairment. Methods Eyelid parameters were recorded during a psychomotor vigilance task (PVT) and driving simulation over 2 days, baseline, and after 20‐mg oral temazepam. The utility of eyelid parameters for detecting PVT lapses was evaluated using receiver operating characteristic curves, and cut‐off levels indicating impairment (≥1 and ≥2 PVT lapses per min) were identified. The accuracy of these cut‐off levels for detecting driving simulator crashes was then examined. Results PVT and driving simulator performance was significantly impaired following benzodiazepine administration (p < .05). Average eyelid closure duration (inter‐event duration) was a reliable indicator of PVT lapses (area under the curve [AUC] of 0.87–0.90). The cut‐off value of eyelid closure duration derived from PVT AUC was able to predict driving simulator crashes with moderately high sensitivity and specificity (76.23% and 75.00%). Conclusions Eyelid parameters were affected by benzodiazepines and accurately detected the psychomotor impairment. In particular, eyelid closure duration is a promising real‐time indicator of benzodiazepine impairment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing the validity of eyelid parameters to detect impairment due to benzodiazepines

Wilkinson

Jackson

Westlake

et al. 2020

Human Psychopharmacology

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“…For example, the percentage of eyelid closure (PERCLOS) 5 reflects the proportion of time an eyelid covers more than 80% of the pupil during a 1-min time interval. PERCLOS is associated with increased drowsiness, 4 decreased vigilance and psychomotor performance, and lane departures and crashes during simulated driving in sleep deprived drivers. [4][5][6][7][8] Similarly, the average blink duration and amplitude and velocity of eyelid movements are related to impaired vigilance, frequent lapses in attention, and real-life BRIEF SUMMARY Current Knowledge/Study Rationale: Measures of eyelid closure episodes accurately reflect drowsiness in drivers, but most ocular devices of eyelid closure episodes use average blink duration.…”

Section: Prolonged Eyelid Closure Episodes During Sleep Deprivation Imentioning

confidence: 99%

“…3,4 Indeed, automated devices that measure eyelid closure episodes in real time have been developed. 3 They rely on averaged values of these eyelid closure metrics rather than identifying individual episodes…”

Section: Introductionmentioning

confidence: 99%

“…PERCLOS is associated with increased drowsiness, 4 decreased vigilance and psychomotor performance, and lane departures and crashes during simulated driving in sleep deprived drivers. [4][5][6][7][8] Similarly, the average blink duration and amplitude and velocity of eyelid movements are related to impaired vigilance, frequent lapses in attention, and real-life BRIEF SUMMARY Current Knowledge/Study Rationale: Measures of eyelid closure episodes accurately reflect drowsiness in drivers, but most ocular devices of eyelid closure episodes use average blink duration. Averaged data may conceal variability in duration of eyelid closure episodes, and more prolonged episodes indicate more extreme drowsiness.…”

Section: Introductionmentioning

confidence: 99%

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Prolonged Eyelid Closure Episodes during Sleep Deprivation in Professional Drivers

Alvaro

Jackson

Berlowitz

et al. 2016

Journal of Clinical Sleep Medicine

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Study Objectives: Real life ocular measures of drowsiness use average blink duration, amplitude and velocity of eyelid movements to reflect drowsiness in drivers. However, averaged data may conceal the variability in duration of eyelid closure episodes, and more prolonged episodes that indicate higher levels of drowsiness. The current study aimed to describe the frequency and duration of prolonged eyelid closure episodes during acute sleep deprivation. Methods: Twenty male professional drivers (mean age ± standard deviation = 41.9 ± 8.3 years) were recruited from the Transport Workers Union newsletter and newspaper advertisements in Melbourne, Australia. Each participant underwent 24 hours of sleep deprivation and completed a simulated driving task (AusEd), the Psychomotor Vigilance Task, and the Karolinska Sleepiness Scale. Eyelid closure episodes during the driving task were recorded and analyzed manually from digital video recordings. Results: Eyelid closure episodes increased in frequency and duration with a median of zero s/h of eyelid closure after 3 h increasing to 34 s/h after 23 h awake. Eyelid closure episodes were short and infrequent from 3 to 14 h of wakefulness. After 17 h of sleep deprivation, longer and more frequent eyelid closure episodes began to occur. Episodes lasting from 7 seconds up to 18 seconds developed after 20 h of wakefulness. Length of eyelid closure episodes was moderately to highly correlated with the standard deviation of lateral lane position, braking reaction time, crashes, impaired vigilance, and subjective sleepiness. Conclusions:The frequency and duration of episodes of prolonged eyelid closure increases during acute sleep deprivation, with very prolonged episodes after 17 hours awake. Automated devices that assess drowsiness using averaged measures of eyelid closure episodes need to be able to detect prolonged eyelid closure episodes that occur during more severe sleep deprivation. Keywords: drowsiness, sleepiness, eyelid closure episodes, professional drivers, averaged data, automated devices Citation: Alvaro PK, Jackson ML, Berlowitz DJ, Swann P, Howard ME. Prolonged eyelid closure episodes during sleep deprivation in professional drivers. I NTRO DUCTI O NSleep deprivation and resultant drowsiness are common causes of impaired performance and motor vehicle accidents (MVA) in both the general population and professional drivers.1,2 A lack of real-time drowsiness measures has hampered efforts to develop and assess interventions for sleep deprivation. Objective measures of real-time drowsiness that accurately identify sleepy individuals before and during driving events can potentially prevent MVAs by alerting the driver to significant drowsiness levels and in turn suggest the use of countermeasures, such as a nap or caffeine. They would also provide an opportunity to evaluate the impact of different driving schedules, identifying high risk schedules that result in drowsy driving and assisting with the design of safer work schedules.Slow, prolonged eyelid closure is associa...

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Driver’s Drowsiness Detection Through Computer Vision: A Review

Ullah

Aslam

Ullah

et al. 2018

Lecture Notes in Computer Science

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The utility of automated measures of ocular metrics for detecting driver drowsiness during extended wakefulness

Cited by 57 publications

References 30 publications

Assessing the validity of eyelid parameters to detect impairment due to benzodiazepines

Assessing the validity of eyelid parameters to detect impairment due to benzodiazepines

Prolonged Eyelid Closure Episodes during Sleep Deprivation in Professional Drivers

Driver’s Drowsiness Detection Through Computer Vision: A Review

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