Monitoring eye and eyelid movements by infrared reflectance oculography to measure drowsiness in drivers

Feeling sleepy? stop driving-awareness of fall asleep crashes

STUDY OBJECTIVES

To examine whether drivers are aware of sleepiness and associated symptoms, and how subjective reports predict driving impairment and physiological drowsiness.

METHODS

Sixteen shift workers (19-65 years; 9 women) drove an instrumented vehicle for 2 hours on a closed-loop track after a night of sleep and a night of work. Subjective sleepiness/symptoms were rated every 15 minutes. Severe and moderate driving impairment was defined by emergency brake maneuvers and lane deviations, respectively. Physiological drowsiness was defined by eye closures (Johns drowsiness scores) and EEG-based microsleep events.

RESULTS

All subjective ratings increased post night-shift (p < 0.001). No severe drive events occurred without noticeable symptoms beforehand. All subjective sleepiness ratings, and specific symptoms, predicted a severe (emergency brake) driving event occurring in the next 15 minutes (OR: 1.76-2.4, AUC > 0.81, p < 0.009), except "head dropping down". Karolinska Sleepiness Scale (KSS), ocular symptoms, difficulty keeping to center of the road, and nodding off to sleep, were associated with a lane deviation in the next 15 minutes (OR: 1.17-1.24, p<0.029), although accuracy was only "fair" (AUC 0.59-0.65). All sleepiness ratings predicted severe ocular-based drowsiness (OR: 1.30-2.81, p < 0.001), with very good-to-excellent accuracy (AUC > 0.8), while moderate ocular-based drowsiness was predicted with fair-to-good accuracy (AUC > 0.62). KSS, likelihood of falling asleep, ocular symptoms, and "nodding off" predicted microsleep events, with fair-to-good accuracy (AUC 0.65-0.73).

CONCLUSIONS

Drivers are aware of sleepiness, and many self-reported sleepiness symptoms predicted subsequent driving impairment/physiological drowsiness. Drivers should self-assess a wide range of sleepiness symptoms and stop driving when these occur to reduce the escalating risk of road crashes due to drowsiness.

An evaluation and comparison of commercial driver sleepiness detection technology: a rapid review

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.

Eye blink parameters to indicate drowsiness during naturalistic driving in participants with obstructive sleep apnea: A pilot study

OBJECTIVES

To determine whether continuous eye blink measures could identify drowsiness in patients with obstructive sleep apnea (OSA) during a week of naturalistic driving.

DESIGN

Observational study comparing OSA patients and healthy controls.

SETTING

Regular naturalistic driving across one week.

PARTICIPANTS

Fifteen untreated moderate to severe OSA patients and 15 age (± 5 years) and sex (female = 6) matched healthy controls.

MEASUREMENTS

Participants wore an eye blink drowsiness recording device during their regular driving for one week.

RESULTS

During regular driving, the duration of time with no ocular movements (quiescence), was elevated in the OSA group by 43% relative to the control group (mean [95% CI] 0.20[0.17, 0.25] vs 0.14[0.12, 0.18] secs, P = .011). During long drives only, the Johns Drowsiness Scale was also elevated and increased by 62% in the OSA group relative to the control group (1.05 [0.76, 1.33] vs 0.65 [0.36, 0.93], P = .0495). Across all drives, critical drowsiness events (defined by a Johns Drowsiness Scale score ≥2.6) were twice as frequent in the OSA group than the control group (rate ratio [95% CI] =1.93 [1.65, 2.25], P ≤ .001).

CONCLUSIONS

OSA patients were drowsier than healthy controls according to some of the continuous real time eye blink drowsiness measures. The findings of this pilot study suggest that there is potential for eye blink measures to be utilized to assess fitness to drive in OSA patients. Future work should assess larger samples, as well as the relationship of eye blink measures to conventional fitness to drive assessments and crash risk.

Tracking intermediate performance of vigilant attention using multiple eye metrics

Vigilance deficits account for a substantial number of accidents and errors. Current techniques to detect vigilance impairment measure only the most severe level evident in eyelid closure and falling asleep, which is often too late to avoid an accident or error. The present study sought to identify ocular biometrics of intermediate impairment of vigilance and develop a new technique that could detect a range of deficits in vigilant attention (VA). Sixteen healthy adults performed well-validated Psychomotor Vigilance Test (PVT) for tracking vigilance attention while undergoing simultaneous recording of eye metrics every 2 hours during 38 hours of continuous wakefulness. A novel marker was found that measured VA when the eyes were open-the prevalence of microsaccades. Notably, the prevalence of microsaccades decreased in response to sleep deprivation and time-on-task. In addition, a novel algorithm for detecting multilevel VA was developed, which estimated performance on the PVT by integrating the novel marker with other eye-related indices. The novel algorithm also tracked changes in intermediate level of VA (specific reaction times in the PVT, i.e. 300-500 ms) during prolonged time-on-task and sleep deprivation, which had not been tracked previously by conventional techniques. The implication of the findings is that this novel algorithm, named "eye-metrical estimation version of the PVT: PVT-E," can be used to reduce human-error-related accidents caused by vigilance impairment even when its level is intermediate.

Machine Learning Models for the Classification of Sleep Deprivation Induced Performance Impairment During a Psychomotor Vigilance Task Using Indices of Eye and Face Tracking

High risk professions, such as pilots, police officers, and TSA agents, require sustained vigilance over long periods of time and/or under conditions of little sleep. This can lead to performance impairment in occupational tasks. Predicting impaired states before performance decrement manifests is critical to prevent costly and damaging mistakes. We hypothesize that machine learning models developed to analyze indices of eye and face tracking technologies can accurately predict impaired states. To test this we trained 12 types of machine learning algorithms using five methods of feature selection with indices of eye and face tracking to predict the performance of individual subjects during a psychomotor vigilance task completed at 2-h intervals during a 25-h sleep deprivation protocol. Our results show that (1) indices of eye and face tracking are sensitive to physiological and behavioral changes concomitant with impairment; (2) methods of feature selection heavily influence classification performance of machine learning algorithms; and (3) machine learning models using indices of eye and face tracking can correctly predict whether an individual's performance is “normal” or “impaired” with an accuracy up to 81.6%. These methods can be used to develop machine learning based systems intended to prevent operational mishaps due to sleep deprivation by predicting operator impairment, using indices of eye and face tracking.

Caffeine may disrupt the impact of real-time drowsiness on cognitive performance: a double-blind, placebo-controlled small-sample study

Caffeine is widely used to promote alertness and cognitive performance under challenging conditions, such as sleep loss. Non-digestive modes of delivery typically reduce variability of its effect. In a placebo-controlled, 50-h total sleep deprivation (TSD) protocol we administered four 200 mg doses of caffeine-infused chewing-gum during night-time circadian trough and monitored participants' drowsiness during task performance with infra-red oculography. In addition to the expected reduction of sleepiness, caffeine was found to disrupt its degrading impact on performance errors in tasks ranging from standard cognitive tests to simulated driving. Real-time drowsiness data showed that caffeine produced only a modest reduction in sleepiness (compared to our placebo group) but substantial performance gains in vigilance and procedural decisions, that were largely independent of the actual alertness dynamics achieved. The magnitude of this disrupting effect was greater for more complex cognitive tasks.

The effects of unintentional drowsiness on the velocity of eyelid movements during spontaneous blinks

OBJECTIVE

Unintentional drowsiness, when we should be alert, as for example when driving a vehicle, can be very dangerous. In this investigation we examined the effects of unintentional drowsiness on the relative velocities of eyelid closing and reopening movements during spontaneous blinks.

APPROACH

Twenty-four young adults volunteered to take part in this experiment, and 18 were finally accepted. They performed a 15 min visual reaction-time test at the same time of day and under the same environmental conditions with and without overnight sleep deprivation, one week apart. Their eyelid movements during blinks were monitored by a system of infrared reflectance blepharometry during each test.

MAIN RESULTS

Very close relationships between the amplitude and maximum velocity of eyelid closing and reopening movements were confirmed. Frequency histograms of amplitude-velocity ratios (AVRs) for eyelid closing and reopening movements showed significant differences between alert and drowsy conditions. With drowsiness, eyelid movements became slower and AVRs increased for many but not all blinks. We also described a time-on-task effect on the relative velocities of eyelid movements which was more apparent in the drowsy condition. Eyelid movements became progressively slower during the first half of the test. This was presumably due to a short-lived alerting effect of starting the test.

SIGNIFICANCE

The relative velocity of eyelid closing and reopening movements during spontaneous blinks decreases with unintentional drowsiness but is sensitive to the brief alerting stimulus of starting a reaction-time test.

Scheduled Naps Improve Drowsiness and Quality of Nursing Care among 12-Hour Shift Nurses

Scheduled naps in the workplace are an effective countermeasure to drowsiness in safety-sensitive industries. This quasi-experimental study with a one-group, pre- and post-test design aimed to examine the effects of scheduled naps on nurses working 12-h shifts. Nurses in two pediatric intensive care units at a tertiary hospital were provided 30-min scheduled nap opportunities during their shifts. A total of 38 nurses completed pre- and post-test work diaries for sleepiness, fatigue, work demands and pace, and quality of nursing care at the end of each shift. The drowsiness of 13 nurses was continuously assessed during their shifts using infrared reflectance oculography. Nurses who reached naps reported improved levels of fatigue on the first night shift and better quality of nursing care the second night and day shifts post-test, while nurses who did not reach naps showed no significant improvements. The oculography successfully assessed drowsiness during 73% and 61% of the pre- and post-test total work hours, respectively. The total cautionary and cautionary or higher levels of drowsiness decreased. Nurse managers should consider scheduled naps in clinical settings to improve nurses' alertness during their shifts.

Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human-computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg-Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F₁ score, and Mathews correlation coefficient.

The effects of caffeine on drowsiness in patients with narcolepsy: a double-blind randomized controlled pilot study

PURPOSE

The effects of caffeine on drowsiness and reaction time in patients with narcolepsy are unclear. We aimed to assess the effects of caffeine as add-on therapy in narcolepsy patients.

METHODS

A randomized, double-blind, placebo-control clinical pilot trial was conducted with a parallel, two-arm trial allocation ratio of 1:1. Participants attended two study visits 7 days apart. The drug was administered orally in a single opaque capsule containing 200 mg caffeine/placebo daily in the morning for 1 week. Sleepiness was assessed objectively using infrared reflectance oculography to measure the percentage of long eye closure (LEC%) and subjectively using two sleepiness scales, the Stanford Sleepiness Scale (SSS) and Karolinska Sleepiness Scale (KSS). Parameters were measured at baseline (BL) prior to taking the drug, after taking the first dose (FD), and after 1 week (WD) of daily caffeine.

RESULTS

Sixteen participants with narcolepsy were included. No significant differences between groups in baseline measurements were observed. LEC% was significantly decreased after the FD and WD compared with baseline levels (BL 1.4 ± 2.1 vs. FD 0.06 ± 0.0.6 and WD 0.03 ± 0.04). Significant improvements in alertness were observed using the KSS when comparing BL with FD and WD (6.3 ± 1.6, 4.9 ± 1.7, and 4.7 ± 1.7, respectively; p = 0.01). No changes in reaction time or SSS scores were noted.

CONCLUSION

Our findings suggest that a small dose of caffeine has positive effects on alertness in patients with narcolepsy. However, larger trials are required to confirm these findings.

TRIAL REGISTRATION NO

ClinicalTrial.gov NCT02832336.

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

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.

EOG-based eye movement recognition using GWO-NN optimization

In recent times, the control of human-computer interface (HCI) systems is triggered by electrooculography (EOG) signals. Eye movements recognized based on the EOG signal pattern are utilized to govern the HCI system and do a specific job based on the type of eye movement. With the knowledge of various related examinations, this paper intends a novel model for eye movement recognition based on EOG signals by utilizing Grey Wolf Optimization (GWO) with neural network (NN). Here, the GWO is used to minimize the error function from the classifier. The performance of the proposed methodology was investigated by comparing the developed model with conventional methods. The results reveal the loftier performance of the adopted method with the error minimization analysis and recognition performance analysis in correspondence with varied performance measures such as accuracy, sensitivity, specificity, precision, false-positive rate (FPR), false-negative rate (FNR), negative predictive value (NPV), false discovery rate (FDR) and the F1 score.

A Comprehensive Survey of Driving Monitoring and Assistance Systems

Improving a vehicle driver's performance decreases the damage caused by, and chances of, road accidents. In recent decades, engineers and researchers have proposed several strategies to model and improve driving monitoring and assistance systems (DMAS). This work presents a comprehensive survey of the literature related to driving processes, the main reasons for road accidents, the methods of their early detection, and state-of-the-art strategies developed to assist drivers for a safe and comfortable driving experience. The studies focused on the three main elements of the driving process, viz. driver, vehicle, and driving environment are analytically reviewed in this work, and a comprehensive framework of DMAS, major research areas, and their interaction is explored. A well-designed DMAS improves the driving experience by continuously monitoring the critical parameters associated with the driver, vehicle, and surroundings by acquiring and processing the data obtained from multiple sensors. A discussion on the challenges associated with the current and future DMAS and their potential solutions is also presented.

Drowsiness measures for commercial motor vehicle operations

Timely detection of drowsiness in Commercial Motor Vehicle (C MV) operations is necessary to reduce drowsiness-related CMV crashes. This is relevant for manual driving and, paradoxically, even more so with increasing levels of driving automation. Measures available for drowsiness detection vary in reliability, validity, usability, and effectiveness. Passively recorded physiologic measures such as electroencephalography (EEG) and a variety of ocular parameters tend to accurately identify states of considerable drowsiness, but are limited in their potential to detect lower levels of drowsiness. They also do not correlate well with measures of driver performance. Objective measures of vigilant attention performance capture drowsiness reliably, but they require active driver involvement in a performance task and are prone to confounds from distraction and (lack of) motivation. Embedded performance measures of actual driving, such as lane deviation, have been found to correlate with physiologic and vigilance performance measures, yet to what extent drowsiness levels can be derived from them reliably remains a topic of investigation. Transient effects from external circumstances and behaviors - such as task load, light exposure, physical activity, and caffeine intake - may mask a driver's underlying state of drowsiness. Also, drivers differ in the degree to which drowsiness affects their driving performance, based on trait vulnerability as well as age. This paper provides a broad overview of the current science pertinent to a range of drowsiness measures, with an emphasis on those that may be most relevant for CMV operations. There is a need for smart technologies that in a transparent manner combine different measurement modalities with mathematical representations of the neurobiological processes driving drowsiness, that account for various mediators and confounds, and that are appropriately adapted to the individual driver. The research for and development of such technologies requires a multi-disciplinary approach and significant resources, but is technically within reach.

Prediction of drowsiness events in night shift workers during morning driving

The morning commute home is an especially vulnerable time for workers engaged in night shift work due to the heightened risk of experiencing drowsy driving. One strategy to manage this risk is to monitor the driver's state in real time using an in vehicle monitoring system and to alert drivers when they are becoming sleepy. The primary objective of this study is to build and evaluate predictive models for drowsiness events occurring in morning drives using a variety of physiological and performance data gathered under a real driving scenario. We used data collected from 16 night shift workers who drove an instrumented vehicle for approximately two hours on a test track on two occasions: after a night shift and after a night of rest. Drowsiness was defined by two outcome events: performance degradation (Lane-Crossing models) and electroencephalogram (EEG) characterized sleep episodes (Microsleep Models). For each outcome, we assessed the accuracy of sets of predictors, including or not including a driver factor, eyelid measures, and driving performance measures. We also compared the predictions using different time intervals relative to the events (e.g., 1-min prior to the event through 10-min prior). By examining the Area Under the receiver operating characteristic Curve (AUC), accuracy, sensitivity, and specificity of the predictive models, the results showed that the inclusion of an individual driver factor improved AUC and prediction accuracy for both outcomes. Eyelid measures improved the prediction for the Lane-Crossing models, but not for Microsleep models. Prediction performance was not changed by adding driving performance predictors or by increasing the time to the event for either outcome. The best models for both measures of drowsiness were those considering driver individual differences and eyelid measures, suggesting that these indicators should be strongly considered when predicting drowsiness events. The results of this paper can benefit the development of real-time drowsiness detection and help to manage drowsiness to avoid related motor-vehicle crashes and loss.

The Impact of Shift Work on Sleep, Alertness and Performance in Healthcare Workers

Shift work is associated with impaired alertness and performance due to sleep loss and circadian misalignment. This study examined sleep between shift types (day, evening, night), and alertness and performance during day and night shifts in 52 intensive care workers. Sleep and wake duration between shifts were evaluated using wrist actigraphs and diaries. Subjective sleepiness (Karolinska Sleepiness Scale, KSS) and Psychomotor Vigilance Test (PVT) performance were examined during day shift, and on the first and subsequent night shifts (3^rd, 4^th or 5^th). Circadian phase was assessed using urinary 6-sulphatoxymelatonin rhythms. Sleep was most restricted between consecutive night shifts (5.74 ± 1.30 h), consecutive day shifts (5.83 ± 0.92 h) and between evening and day shifts (5.20 ± 0.90 h). KSS and PVT mean reaction times were higher at the end of the first and subsequent night shift compared to day shift, with KSS highest at the end of the first night. On nights, working during the circadian acrophase of the urinary melatonin rhythm led to poorer outcomes on the KSS and PVT. In rotating shift workers, early day shifts can be associated with similar sleep restriction to night shifts, particularly when scheduled immediately following an evening shift. Alertness and performance remain most impaired during night shifts given the lack of circadian adaptation to night work. Although healthcare workers perceive themselves to be less alert on the first night shift compared to subsequent night shifts, objective performance is equally impaired on subsequent nights.

Synchronized drowsiness monitoring and simulated driving performance data under 50-hr sleep deprivation: A double-blind placebo-controlled caffeine intervention

This paper presents the 60-s time-resolution segment from our 50-h total sleep deprivation (TSD) dataset (Aidman et al., 2018) [1] that captures minute-by-minute dynamics of driving performance (lane keeping and speed variability) along with objective, oculography-derived drowsiness estimates synchronised to the same 1-min driving epochs. Eleven participants (5 females, aged 18–28) were randomised into caffeine (administered in four 200 mg doses via chewing gum in the early morning hours) or placebo groups. Every three hours they performed a 40 min simulated drive in a medium fidelity driving simulator, while their drowsiness was continuously measured with a spectacle frame-mounted infra-red alertness monitoring system. The dataset covers 15 driving periods of 40 min each, and thus contains over 600 data points of paired data per participant. The 1-min time resolution enables detailed time-series analyses of both time-since-wake and time-on-task performance dynamics and associated drowsiness levels. It also enables direct examination of the relationships between drowsiness and task performance measures. The question of how these relationships might change under various intervention conditions (caffeine in our case) seems worth further investigation.

Randomised controlled trial of the efficacy of a blue-enriched light intervention to improve alertness and performance in night shift workers

OBJECTIVES

Night workers often experience high levels of sleepiness due to misalignment of the sleep-wake cycle from the circadian pacemaker, in addition to acute and chronic sleep loss. Exposure to light, in particular short wavelength light, can improve alertness and neurobehavioural performance. This randomised controlled trial examined the efficacy of blue-enriched polychromatic light to improve alertness and neurobehavioural performance in night workers.

DESIGN

Participants were 71 night shift workers (42 males; 32.8±10.5 years) who worked at least 6 hours between 22:00 and 08:00 hours. Sleep-wake logs and wrist actigraphy were collected for 1-3 weeks, followed by 48-hour urine collection to measure the circadian 6-sulphatoxymelatonin (aMT6s) rhythm. On the night following at least two consecutive night shifts, workers attended a simulated night shift in the laboratory which included subjective and objective assessments of sleepiness and performance. Workers were randomly assigned for exposure to one of two treatment conditions from 23:00 hours to 07:00 hours: blue-enriched white light (17 000 K, 89 lux; n=36) or standard white light (4000 K, 84 lux; n=35).

RESULTS

Subjective and objective sleepiness increased during the night shift in both light conditions (p<0.05, η_p²=0.06-0.31), but no significant effects of light condition were observed. The 17 000 K light, however, did improve subjective sleepiness relative to the 4000 K condition when light exposure coincided with the time of the aMT6s peak (p<0.05, d=0.41-0.60).

CONCLUSION

This study suggests that, while blue-enriched light has potential to improve subjective sleepiness in night shift workers, further research is needed in the selection of light properties to maximise the benefits.

TRIAL REGISTRATION NUMBER

The Australian New Zealand Clinical Trials Registry ACTRN12610000097044 (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=320845&isReview=true).

Hypersomnolence and Traffic Safety

Many people die or become disabled because of motor vehicle accidents. Scientific data suggest that sleepy drivers or those driving at odd hours are more likely to make driving mistakes. Patients with obstructive sleep apnea and narcolepsy have been found to exhibit higher rates of falling asleep while driving. Treatment enhances the vigilance of these drivers. Tests measuring the extent of daytime sleepiness or drowsiness while driving can help identify at-risk drivers. There is a need to develop clear regulations governing periodic assessment of drivers' risks of falling asleep at the wheel, especially commercial drivers.

The effect of intermittent fasting during Ramadan on sleep, sleepiness, cognitive function, and circadian rhythm

PURPOSE

Studies have shown that experimental fasting can affect cognitive function, sleep, and wakefulness patterns. However, the effects of experimental fasting cannot be generalized to fasting during Ramadan due to its unique characteristics. Therefore, there has been increased interest in studying the effects of fasting during Ramadan on sleep patterns, daytime sleepiness, cognitive function, sleep architecture, and circadian rhythm.

METHOD

In this review, we critically discuss the current research findings in those areas during the month of Ramadan.

RESULTS

Available data that controlled for sleep/wake schedule, sleep duration, light exposure, and energy expenditure do not support the notion that Ramadan intermittent fasting increases daytime sleepiness and alters cognitive function. Additionally, recent well-designed studies showed no effect of fasting on circadian rhythms. However, in non-constrained environments that do not control for lifestyle changes, studies have demonstrated sudden and significant delays in bedtime and wake time.

CONCLUSIONS

Studies that controlled for environmental factors and sleep/wake schedule reported no significant disturbances in sleep architecture. Nevertheless, several studies have consistently reported that the main change in sleep architecture during fasting is a reduction in the proportion of REM sleep.

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced within-network functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants' eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal.

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

Slowed eyelid closure coupled with increased duration and frequency of closure is associated with drowsiness. This study assessed the utility of two devices for automated measurement of slow eyelid closure in a standard poor performance condition (alcohol) and following 12-h sleep deprivation. Twenty-two healthy participants (mean age=20.8 (SD 1.9) years) with no history of sleep disorders participated in the study. Participants underwent one baseline and one counterbalanced session each over two weeks; one 24-hour period of sleep deprivation, and one daytime session during which alcohol was consumed after a normal night of sleep. Participants completed a test battery consisting of a 30-min simulated driving task, a 10-min Psychomotor Vigilance Task (PVT) and the Karolinska Sleepiness Scale (KSS) each in two baseline sessions, and in two randomised, counterbalanced experimental sessions; following sleep deprivation and following alcohol consumption. Eyelid closure was measured during both tasks using two automated devices (Copilot and Optalert™). There was an increase in the proportion of time with eyelids closed and the Johns Drowsiness Score (incorporating relative velocity of eyelid movements) following sleep deprivation using Optalert (p<0.05 for both). These measures correlated significantly with crashes, PVT lapses and subjective sleepiness (r-values 0.46-0.69, p<0.05). No difference between the two sessions for PERCLOS recorded during the PVT or the driving task as measured by the Copilot. The duration of eyelid closure predicted frequent lapses following sleep deprivation (which were equivalent to the average lapses at a blood alcohol concentration of 0.05% - area under curve for ROC curve 0.87, p<0.01). The duration of time with slow eyelid closure, assessed by the automated devices, increased following sleep deprivation and was associated with deterioration in psychomotor performance and subjective sleepiness. Comprehensive algorithms inclusive of ocular parameters may be a better indicator of performance impairment following sleep loss.

Tests of a New Drowsiness Characterization and Monitoring System Based on Ocular Parameters

Drowsiness is the intermediate state between wakefulness and sleep. It is characterized by impairments of performance, which can be very dangerous in many activities and can lead to catastrophic accidents in transportation or in industry. There is thus an obvious need for systems that are able to continuously, objectively, and automatically estimate the level of drowsiness of a person busy at a task. We have developed such a system, which is based on the physiological state of a person, and, more specifically, on the values of ocular parameters extracted from images of the eye (photooculography), and which produces a numerical level of drowsiness. In order to test our system, we compared the level of drowsiness determined by our system to two references: (1) the level of drowsiness obtained by analyzing polysomnographic signals; and (2) the performance of individuals in the accomplishment of a task. We carried out an experiment in which 24 participants were asked to perform several Psychomotor Vigilance Tests in different sleep conditions. The results show that the output of our system is well correlated with both references. We determined also the best drowsiness level threshold in order to warn individuals before they reach dangerous situations. Our system thus has significant potential for reliably quantifying the level of drowsiness of individuals accomplishing a task and, ultimately, for preventing drowsiness-related accidents.

A practical efficient human computer interface based on saccadic eye movements for people with disabilities

Human computer interfaces (HCI) provide new channels of communication for people with severe motor disabilities to state their needs, and control their environment. Some HCI systems are based on eye movements detected from the electrooculogram. In this study, a wearable HCI, which implements a novel adaptive algorithm for detection of saccadic eye movements in eight directions, was developed, considering the limitations that people with disabilities have. The adaptive algorithm eliminated the need for calibration of the system for different users and in different environments. A two-stage typing environment and a simple game for training people with disabilities to work with the system were also developed. Performance of the system was evaluated in experiments with the typing environment performed by six participants without disabilities. The average accuracy of the system in detecting eye movements and blinking was 82.9% at first tries with an average typing rate of 4.5cpm. However an experienced user could achieve 96% accuracy and 7.2cpm typing rate. Moreover, the functionality of the system for people with movement disabilities was evaluated by performing experiments with the game environment. Six people with tetraplegia and significant levels of speech impairment played with the computer game several times. The average success rate in performing the necessary eye movements was 61.5%, which increased significantly with practice up to 83% for one participant. The developed system is 2.6×4.5cm in size and weighs only 15g, assuring high level of comfort for the users.

Real-time driver drowsiness feedback improves driver alertness and self-reported driving performance

Driver drowsiness has been implicated as a major causal factor in road accidents. Tools that allow remote monitoring and management of driver fatigue are used in the mining and road transport industries. Increasing drivers' own awareness of their drowsiness levels using such tools may also reduce risk of accidents. The study examined the effects of real-time blink-velocity-derived drowsiness feedback on driver performance and levels of alertness in a military setting. A sample of 15 Army Reserve personnel (1 female) aged 21-59 (M=41.3, SD=11.1) volunteered to being monitored by an infra-red oculography-based Optalert Alertness Monitoring System (OAMS) while they performed their regular driving tasks, including on-duty tasks and commuting to and from duty, for a continuous period of 4-8 weeks. For approximately half that period, blink-velocity-derived Johns Drowsiness Scale (JDS) scores were fed back to the driver in a counterbalanced repeated-measures design, resulting in a total of 419 driving periods under "feedback" and 385 periods under "no-feedback" condition. Overall, the provision of real-time feedback resulted in reduced drowsiness (lower JDS scores) and improved alertness and driving performance ratings. The effect was small and varied across the 24-h circadian cycle but it remained robust after controlling for time of day and driving task duration. Both the number of JDS peaks counted for each trip and their duration declined in the presence of drowsiness feedback, indicating a dynamic pattern that is consistent with a genuine, entropy-reducing feedback mechanism (as distinct from random re-alerting) behind the observed effect. Its mechanisms and practical utility have yet to be fully explored. Direct examination of the alternative, random re-alerting explanation of this feedback effect is an important step for future research.

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

OBJECTIVE

Slow eyelid closure is recognized as an indicator of sleepiness in sleep-deprived individuals, although automated ocular devices are not well validated. This study aimed to determine whether changes in eyelid closure are evident following acute sleep deprivation as assessed by an automated device and how ocular parameters relate to performance after sleep deprivation.

METHODS

Twelve healthy professional drivers (45.58 ± 10.93 years) completed 2 randomized sessions: After a normal night of sleep and after 24 h of total sleep deprivation. Slow eye closure (PERCLOS) was measured while drivers performed a simulated driving task.

RESULTS

Following sleep deprivation, drivers displayed significantly more eyelid closure (P < .05), greater variation in lane position (P < .01) and more attentional lapses (P < .05) compared to after normal sleep. PERCLOS was moderately associated with variability in both vigilance performance (r = 0.68, P < .05) and variation in lane position on the driving task (r = 0.61, P < .05).

CONCLUSIONS

Automated ocular measurement appears to be an effective means of detecting impairment due to sleep loss in the laboratory.

The Case for Addressing Operator Fatigue

Sleep deficiency, which can be caused by acute sleep deprivation, chronic insufficient sleep, untreated sleep disorders, disruption of circadian timing, and other factors, is endemic in the U.S., including among professional and non-professional drivers and operators. Vigilance and attention are critical for safe transportation operations, but fatigue and sleepiness compromise vigilance and attention by slowing reaction times and impairing judgment and decision-making abilities. Research studies, polls, and accident investigations indicate that many Americans drive a motor vehicle or operate an aircraft, train or marine vessel while drowsy, putting themselves and others at risk for error and accident. In this chapter, we will outline some of the factors that contribute to sleepiness, present evidence from laboratory and field studies demonstrating how sleepiness impacts transportation safety, review how sleepiness is measured in laboratory and field settings, describe what is known about interventions for sleepiness in transportation settings, and summarize what we believe are important gaps in our knowledge of sleepiness and transportation safety.

Excessive daytime sleepiness is associated with changes in salivary inflammatory genes transcripts

Excessive daytime sleepiness (EDS) is a ubiquitous problem that affects public health and safety. A test that can reliably identify individuals that suffer from EDS is needed. In contrast to other methods, salivary biomarkers are an objective, inexpensive, and noninvasive method to identify individuals with inadequate sleep. Although we have previously shown that inflammatory genes are elevated in saliva samples taken from sleep deprived individuals, it is unclear if inflammatory genes will be elevated in clinical populations with EDS. In this study, salivary samples from individuals with sleep apnea were evaluated using the Taqman low density inflammation array. Transcript levels for 3 genes, including prostaglandin-endoperoxide synthase 2 (PTGS2), were elevated in patients with sleep apnea. Interestingly, PTGS2 was also elevated in patients with EDS but who did not have sleep apnea. These data demonstrate the feasibility of using salivary transcript levels to identify individuals that self-report excessive daytime sleepiness.

Temporal dynamics of ocular indicators of sleepiness across sleep restriction

The current study characterized the temporal dynamics of ocular indicators of sleepiness during extended sleep restriction. Ten male participants (mean age ± SD = 23.3 ± 1.6 years) underwent 40 h of continuous wakefulness under constant routine (CR) conditions; they completed the Karolinska Sleepiness Scale (KSS) and a 10-min auditory psychomotor vigilance task (aPVT) hourly. Waking electroencephalography (EEG) and ocular measures were recorded continuously throughout the CR. Infrared-reflectance oculography was used to collect the ocular measures positive and negative amplitude-velocity ratio, mean blink duration, the percentage of eye closure, and a composite score of sleepiness levels (Johns Drowsiness Scale). All ocular measures, except blink duration, displayed homeostatic and circadian properties. Only circadian effects were detected in blink duration. Significant, phase-locked cross-correlations (p < 0.05) were detected between ocular measures and aPVT reaction time (RT), aPVT lapses, KSS, and EEG delta-theta (0.5-5.5 Hz), theta-alpha (5.0-9.0 Hz), and beta (13.0-20.0 Hz) activity. Receiver operating characteristic curve analysis demonstrated reasonable sensitivity and specificity of ocular measures in correctly classifying aPVT lapses above individual baseline thresholds (initial 16 h of wakefulness). Under conditions of sleep restriction, ocular indicators of sleepiness paralleled performance impairment and self-rated sleepiness levels, and demonstrated their potential to detect sleepiness-related attentional lapses. These findings, if reproduced in a larger sample, will have implications for the use of ocular-based sleepiness-warning systems in operational settings.

Sleepiness and Safety: Where Biology Needs Technology

Maintaining human alertness and behavioral capability under conditions of sleep loss and circadian misalignment requires fatigue management technologies due to: (1) dynamic nonlinear modulation of performance capability by the interaction of sleep homeostatic drive and circadian regulation; (2) large differences among people in neurobehavioral vulnerability to sleep loss; (3) error in subjective estimates of fatigue on performance; and (4) to inform people of the need for recovery sleep. Two promising areas of technology have emerged for managing fatigue risk in safety-sensitive occupations. The first involves preventing fatigue by optimizing work schedules using biomathematical models of performance changes associated with sleep homeostatic and circadian dynamics. Increasingly these mathematical models account for individual differences to achieve a more accurate estimate of the timing and magnitude of fatigue effects on individuals. The second area involves technologies for detecting transient fatigue from drowsiness. The Psychomotor Vigilance Test (PVT), which has been extensively validated to be sensitive to deficits in attention from sleep loss and circadian misalignment, is an example in this category. Two shorter-duration versions of the PVT recently have been developed for evaluating whether operators have sufficient behavioral alertness prior to or during work. Another example is online tracking the percent of slow eyelid closures (PERCLOS), which has been shown to reflect momentary fluctuations of vigilance. Technologies for predicting and detecting sleepiness/fatigue have the potential to predict and prevent operator errors and accidents in safety-sensitive occupations, as well as physiological and mental diseases due to inadequate sleep and circadian misalignment.

Feasibility of force platform based roadside drowsiness screening - a pilot study

Previous research on driver drowsiness detection has focused on developing in-car systems that continuously monitor the driver while driving and warn him/her when drowsiness compromises safety. In occupational settings a simple test of postural control has showed sensitivity to work shift induced fatigue in drivers. Whether the test is feasible for surveillance purposes in roadside settings is unknown. The present research sought to evaluate the feasibility of using a force platform test of postural control as a breathalyzer-like drowsiness-test at the roadside. Seventy-one commercial drivers stopped by at our measurement sites and volunteered to participate in the study. We tested postural control with a computerized force platform, on which the drivers stood eyes open while it sampled body center-of-pressure excursions at 33Hz for 30s and scored postural control as the area of the 95% confidence ellipse enclosing the excursions. The drivers also completed the Karolinska Sleepiness Scale (KSS) and we recorded each driver's wake up time, time on task, and time of testing. Five of the seventy-one drivers exhibited significantly poorer postural control than their peers (P=0.03). The wake up times and times on task for these five drivers indicated that they were on a night shift schedule or had a long time on task. Furthermore, their postural control and KSS scores correlated (r=-0.88, P=0.04), whereas the scores did not correlate for their peers (r=0.10, P=0.48). These results indicate that the force platform test identified drivers, whose impairment in postural control was drowsiness-related. Specifically, the test identified the few drivers in this roadside sample whose wake- and work histories resembled a night shift schedule. In this kind of roadside setting, with a demographically heterogeneous group and interindividual differences in people's responses to drowsiness, it suggests that the method, further developed, may provide a drowsiness test for roadside surveillance.

Now you hear me, now you don't: eyelid closures as an indicator of auditory task disengagement

STUDY OBJECTIVES

Eyelid closures in fatigued individuals signify task disengagement in attention-demanding visual tasks. Here, we studied how varying degrees of eyelid closure predict responses to auditory stimuli depending on whether a participant is well rested or sleep deprived. We also examined time-on-task effects and how more and less vulnerable individuals differed in frequency of eye closures and lapses.

DESIGN

Six repetitions of an auditory vigilance task were performed in each of two sessions: rested wakefulness (RW) and total sleep deprivation (TSD) (order counterbalanced).

SETTING

Sleep laboratory.

PARTICIPANTS

Nineteen healthy young adults (mean age: 22 ± 2.8 y; 11 males).

INTERVENTION

Approximately 24 h of TSD.

MEASUREMENTS AND RESULTS

Eyelid closure was rated on a 9-point scale (1 = fully closed to 9 = fully opened) using video segments time-locked to the auditory event. Eyes-open trials predominated during RW, but different degrees of eye closure were uniformly distributed during TSD. The frequency of lapses (response time > 800 ms or nonresponses) to auditory stimuli increased dramatically with greater degrees of eye closure, but the association was strong only during TSD. There were significant within-run time-on-task effects on eye closure and auditory lapses that were exacerbated by TSD. Participants who had more auditory lapses during TSD (more vulnerable) had greater variability in their eyelid closures.

CONCLUSIONS

Eyelid closures are a strong predictor of auditory task disengagement in the sleep-deprived state but are less relevant during rested wakefulness. Individuals relatively more impaired in this auditory vigilance task during total sleep deprivation display oculomotor evidence for greater state instability.

Objective assessment of drowsiness and reaction time during intermittent Ramadan fasting in young men: a case-crossover study

Background

Ramadan fasting and its attendant lifestyle changes induce changes in the circadian rhythm and in associated physiological and metabolic functions. Previous studies that have assessed psychomotor performance during Ramadan fasting have reported conflicting results. Therefore, we designed this study to objectively assess the effects of intermittent fasting during and outside Ramadan (to control for lifestyle changes) on drowsiness, blink total duration and mean reaction time (MRT) test while controlling for potential confounders.

Methods

Eight healthy volunteers with a mean age of 25.3 ± 2.9 years and a mean body mass index (BMI) of 23.4 ± 3.2 kg/m² reported to the sleep laboratory on four occasions for polysomnography (PSG) and drowsiness and psychomotor assessments as follows: 1) adaptation; 2) 4 weeks before Ramadan while performing the Islamic fasting for 1 week (baseline fasting) (BLF); 3) 1 week before Ramadan (non-fasting baseline) (BL); and 4) during the second week of Ramadan while fasting (Ramadan). OPTALERT™ was used to objectively assess daytime drowsiness using the Johns Drowsiness Scale (JDS), and blink total duration and a visual reaction time test were used to assess MRT.

Results

Rapid eye movement (REM) sleep percentage was significantly lower at BLF (17.7 ± 8.1%) and at Ramadan (18.6 ± 10.7%) compared with BL (25.6 ± 4.8%) (p < 0.05). There were no significant differences between JDS scores and blink total duration during the two test periods in BL, BLF and Ramadan. There were no significant changes in MRT during BL, BLF and Ramadan.

Conclusions

Under controlled conditions of fixed light/dark exposure, caloric intake, sleep/wake schedule and sleep quality, the Islamic intermittent fasting has no impact on drowsiness and vigilance as measured by the JDS, total blink duration and MRT.

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

A wearable device for measuring eye dynamics in real-world conditions

Drowsiness and lapses of responsiveness have the potential to cause fatalities in many occupations. One subsystem of a prototype device which aims to detect these lapses as they occur is described. A head-mounted camera measures several features of the eye that are known to correlate with drowsiness. The system was tested with eight combinations of eye colour, ambient lighting, and eye glasses to simulate typical real-world input conditions. A task was completed for each set of conditions to simulate a range of eye movement-saccades, tracking, and eye closure. Our image processing software correctly classified 99.3% of video frames as open/closed/partly closed, and the error rate was not affected by the combinations of input conditions. Most errors occurred during eyelid movement. The accuracy of the pupil localisation was also not influenced by input conditions, with the possible exception of one subject's glasses.

Detecting deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance of wakefulness tests

Several researchers have investigated the relation between vigilance and ocular variables such as saccade, slow eye movement, pupil, blink, or eyelid closure. This study was undertaken to find the most effective indicator among these ocular variables for evaluating short-term (1 min) fluctuation of vigilance, and to investigate the ability of the most effective ocular variable for predicting deteriorated vigilance during behavioral maintenance of the wakefulness test (Oxford sleep resistance test: OSLER test). Nine healthy volunteers (two women, 19-30 years old, 23.4±3.9 years old) participated in this study. Ocular variables were recorded during the OSLER test at 10 A.M. and 2 P.M. before and after partial sleep deprivation (4h sleep). The periods during the OSLER test were divided into 1 min epochs. Each epoch was classified according to the number of consecutive missed responses. Decreased blink frequency and pupil diameter as well as increased percentage of eyelid closure time (PERCLOS) and slow eye movement were observed as the consecutive missed responses increased. Among these variables, PERCLOS showed the highest ability to detect occurrence of any missed response and three or more consecutive missed responses. Moreover, a missed response seldom occurred (0.2±0.2/20 trial/min) when PERCLOS was less than 11.5% per minute. Results suggest that, among the ocular variables, PERCLOS can prevent error or accident caused by low vigilance most effectively.

PVT lapses differ according to eyes open, closed, or looking away

STUDY OBJECTIVES

A lapse during the psychomotor vigilance task (PVT) is usually defined as a response longer than 500 ms; however, it is currently unknown what psychobiological phenomena occur during a lapse. An assessment of what a participant is doing during a lapse may depict varying levels of "disengagement" during these events and provide more insight into the measurement of both a lapse and sleepiness.

DESIGN

Repeated measures.

SETTING

Participants underwent extended 30-min PVT sessions twice, at 22:00 and 04:00, under: (i) typical non-distractive laboratory conditions, and (ii) an additional distractive condition.

PARTICIPANTS

Twenty-four healthy young adults (mean age: 23.2 y +/- 2 y; range 21-25 y [12 m; 12 f]) without any sleep or medical problems and without any prior indication of daytime sleepiness.

INTERVENTIONS

One night of sleep loss. Distraction comprised a TV located at 90 degrees in the visual periphery showing a popular TV program. For the non-distraction condition, the TV was turned off.

MEASUREMENTS & RESULTS

Video data (bird's-eye and frontal view) were used to classify each lapse (> or = 500 ms) as occurring with eyes open (EO), eyes closed (EC), or due to a head turn (HT). EO lapses were more prevalent, with all lapses (EO, EC, and HT) increasing with sleepiness. There was a significant effect of distraction for HT lapses which was exacerbated when sleepy. For lapse duration there was little effect of sleepiness for EO lapses but a significant effect for EC and HT. The 95% confidence intervals for lapse duration and associated behavior showed those lapses greater than 2669 ms were 95% likely to be EC, whereas those 500-549 ms were 95% likely to be EO. Response times of 1217 ms had a 50:50 probability of being EO:EC.

CONCLUSIONS

Discriminating the varying causes of lapses whether due to visual inattention (eyes open), microsleep (eyes closed), or distraction (head turn) may provide further insight into levels of disengagement from the PVT and further insight into developing sleepiness.

Reaction of sleepiness indicators to partial sleep deprivation, time of day and time on task in a driving simulator--the DROWSI project

Studies of driving and sleepiness indicators have mainly focused on prior sleep reduction. The present study sought to identify sleepiness indicators responsive to several potential regulators of sleepiness: sleep loss, time of day (TOD) and time on task (TOT) during simulator driving. Thirteen subjects drove a high-fidelity moving base simulator in six 1-h sessions across a 24-h period, after normal sleep duration (8 h) and after partial sleep deprivation (PSD; 4 h). The results showed clear main effects of TOD (night) and TOT but not for PSD, although the latter strongly interacted with TOD. The most sensitive variable was subjective sleepiness, the standard deviation of lateral position (SDLAT) and measures of eye closure [duration, speed (slow), amplitude (low)]. Measures of electroencephalography and line crossings (LCs) showed only modest responses. For most variables individual differences vastly exceeded those of the fixed effects, except for subjective sleepiness and SDLAT. In a multiple regression analysis, SDLAT, amplitude/peak eye-lid closing velocity and blink duration predicted subjective sleepiness bouts with a sensitivity and specificity of about 70%, but were mutually redundant. The prediction of LCs gave considerably weaker, but similar results. In summary, SDLAT and eye closure variables could be candidates for use in sleepiness-monitoring devices. However, individual differences are considerable and there is need for research on how to identify and predict individual differences in susceptibility to sleepiness.

Predicting driver drowsiness using vehicle measures: recent insights and future challenges

INTRODUCTION

Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.

METHOD

The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.

RESULTS

Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.

CONCLUSIONS

Further research is necessary to examine more complex functions, as well as individual differences between drivers.

IMPACT ON INDUSTRY

A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.