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Introduction: Driver drowsiness detection technology that assesses eye blinks is increasingly being used as a safety intervention in the transport industry. It is unclear how alcohol consumption to common legal driving limits impacts upon this technology. The aim of the study was to assess the impact of a blood alcohol content (BAC) of 0.05% and of 0.08% on drowsiness detection technology during simulated driving., Methods: Participants completed a 60-min driving simulation and sleepiness questionnaire under three conditions: 1-0.00% BAC, 2-0.05% BAC and 3-0.08% BAC. During the driving simulation task participants wore a commercial eye blink drowsiness detection technology (Optalert) with the drowsiness alarms silenced., Results: Twelve participants (3 female) completed all alcohol conditions. Relative to baseline, all eye blink parameters were affected at 0.08% BAC (all p < 0.05), whereas 0.05% BAC only affected the composite eye blink drowsiness measure (the Johns Drowsiness Scale)., Conclusions: Alcohol consumption to 0.08% BAC impaired eye blink measures to a level that would be considered a moderate drowsiness risk. Therefore, employers should be aware that drowsiness alerts from these technologies may increase after alcohol consumption., (© 2023 The Authors. Human Psychopharmacology: Clinical and Experimental published by John Wiley & Sons Ltd.)

Objective . Sleepiness-related motor vehicle crashes, caused by lack of sleep or driving during night-time hours, often result in serious injury or fatality. Sleepiness detection technology is rapidly emerging as a sleepiness risk mitigation strategy for drivers. Continuous monitoring technologies assess and alert to driver sleepiness in real-time, while fit for duty technologies provide a single assessment of sleepiness state. The aim of this rapid review was to evaluate and compare sleepiness detection technologies in relation to specifications, cost, target consumer group and validity. Approach . We evaluated a range of sleepiness detection technologies suitable for consumer groups ranging from regular drivers in private vehicles through to work-related drivers within large businesses. Main results . Continuous monitoring technologies typically ranged between $100 and $3000 AUD and had ongoing monthly costs for telematics functionality and manager alerts. Fit for duty technologies had either a one-off purchase cost or a monthly subscription cost. Of concern, the majority of commercial continuous monitoring technologies lacked scientific validation. While some technologies had promising findings in terms of their ability to detect and reduce driver sleepiness, further validation work is required. Field studies that evaluate the sensitivity and specificity of technology alerts under conditions that are regularly experienced by drivers are necessary. Additionally, there is a need for longitudinal naturalistic driving studies to determine whether sleepiness detection technologies actually reduce sleepiness-related crashes or near-crashes. Significance . There is an abundance of sleepiness detection technologies on the market, but a majority lacked validation. There is a need for these technologies and their validation to be regulated by a driver safety body. Otherwise, consumers will base their technology choices on cost and features, rather than the ability to save lives., (© 2021 Institute of Physics and Engineering in Medicine.)

Sleepiness remains a major contributor to road crashes. Driver monitoring systems identify early signs of sleepiness and alert drivers, using real-time analysis of eyelid movements, EEG activity, and steering control. Other vehicle adaptations warn drivers of lane departures or collision hazards, with higher vehicle automation actively taking over vehicle control to prevent run off the road incidents and institute emergency braking. Similarly, road adaptations warn drivers (rumble strips) or mitigate crash severity (barriers). Infrastructure to encourage drivers to use countermeasures, such as rest stops for napping, is also important. The effectiveness of adaptations varies for different road users., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Summary: Drowsy driving is a major cause of fatal and serious injury motor vehicle accidents. The inability objectively to assess drowsiness has hindered the assessment of fitness to drive and the development of drowsy driving regulations. This study evaluated whether spontaneous eye blink parameters measured briefly pre‐ and post‐drive could be used to detect drowsy driving impairment. Twelve healthy participants (6 female) drove an instrumented vehicle for 2 h on a closed‐loop track during a rested (8–10 h awake) and an extended wake condition (32–34 h awake). Pre‐ and post‐drive, the participants completed a 5 min eye blink task, a psychomotor vigilance task (PVT), and the Karolinska sleepiness scale (KSS). Whole drive impairment was defined as >3.5 lane departures per hour. Severe end of drive impairment was defined as ≥2 lane departures in the last 15 min. The pre‐drive % of time with eyes closed best predicted the whole drive impairment (area under the curve [AUC] 0.87). KSS had similar prediction ability (AUC 0.85), while PVT reaction time (AUC 0.72) was less accurate. The composite eye blink parameter, the Johns drowsiness scale was the best retrospective detector of severe end of drive impairment (AUC 0.99). The PVT reaction time (AUC 0.92) and the KSS (AUC 0.93) were less accurate. Eye blink parameters detected drowsy driving impairment with an accuracy that was similar to, or marginally better than, PVT and KSS. As eye blink measures are simple to measure, are objective and have high accuracy, they present an ideal option for the assessment of fitness for duty and roadside drowsiness. [ABSTRACT FROM AUTHOR]

Most objective drowsiness measures have limited ability to provide continuous, accurate assessment of drowsiness state in operational settings. Spontaneous eye blink parameters are ideal for drowsiness assessment as they are objective, non-invasive, and can be recorded continuously during regular activities. Studies that have assessed the spontaneous eye blink as a drowsiness measure are diverse, varying greatly in respect to study design, eye blink acquisition technology and eye blink parameters assessed. The purpose of this narrative review is to collate these studies to determine 1) which eye blink parameters provide the best state drowsiness measures; 2) how well eye blink parameters relate to and predict conventional drowsiness measures and 3) whether eye blink parameters can identify drowsiness impairment in obstructive sleep apnoea (OSA) - a highly prevalent disorder associated with excessive sleepiness and increased accident risk. In summary, almost all eye blink parameters varied consistently with drowsiness state, with blink duration and percentage of eye closure the most robust. All eye blink parameters were associated with and predicted conventional drowsiness measures, with generally fair to good accuracy. Eye blink parameters also showed utility for identifying OSA patients and treatment response, suggesting these parameters may identify drowsiness impairment in this group. [ABSTRACT FROM AUTHOR]

• Heavy vehicle drivers on average had 5.1-hours sleep during a 7-hour rest break. • Heavy vehicle drivers on average had 6.5-hours sleep during an 11-hour rest break. • Subjective sleepiness was reduced after the 11- vs. 7-hour rest break. • Objective alertness when driving was improved after the 11- vs. 7-hour rest break. • Some driving performance metrics were improved after the 11- vs. 7-hour rest break. An inadequate rest break between shifts may contribute to driver sleepiness. This study assessed whether extending the major rest break between shifts from 7-hours (Australian industry standard) to 11-hours, improved drivers' sleep, alertness and naturalistic driving performance. 17 heavy vehicle drivers (16 male) were recruited to complete two conditions. Each condition comprised two 13-hour shifts, separated by either a 7- or 11-hour rest break. The initial 13-hour shift was the drivers' regular work. The rest break and following 13-hour shift were simulated. The simulated shift included 5-hours of naturalistic driving with measures of subjective sleepiness, physiological alertness (ocular and electroencephalogram) and performance (steering and lane departures). 13 drivers provided useable data. Total sleep during the rest break was greater in the 11-hour than the 7-hour condition (median hours [25th to 75th percentile] 6.59 [6.23, 7.23] vs. 5.07 [4.46, 5.38], p = 0.008). During the simulated shift subjective sleepiness was marginally better for the 11-hour condition (mean Karolinska Sleepiness Scale [95th CI] = 4.52 [3.98, 5.07] vs. 5.12 [4.56, 5.68], p = 0.009). During the drive, ocular and vehicle metrics were improved for the 11-hour condition (p<0.05). Contrary to expectations, mean lane departures p/hour were increased during the 11-hour condition (1.34 [−0.38,3.07] vs. 0.63 [−0.2,1.47], p = 0.027). Extending the major rest between shifts substantially increases sleep duration and has a modest positive impact on driver alertness and performance. Future work should replicate the study in a larger sample size to improve generalisability and assess the impact of consecutive 7-hour major rest breaks. [ABSTRACT FROM AUTHOR]