Countermeasures to driver fatigue: a review of public awareness campaigns and legal approaches

Subjective awareness of sleepiness while driving in younger and older adults

Understanding whether drivers can accurately assess sleepiness is essential for educational campaigns advising drivers to stop driving when feeling sleepy. However, few studies have examined this in real-world driving environments, particularly among older drivers who comprise a large proportion of all road users. To examine the accuracy of subjective sleepiness ratings in predicting subsequent driving impairment and physiological drowsiness, 16 younger (21-33 years) and 17 older (50-65 years) adults drove an instrumented vehicle for 2 h on closed loop under two conditions: well-rested and 29 h sleep deprivation. Sleepiness ratings (Karolinska Sleepiness Scale, Likelihood of Falling Asleep scale, Sleepiness Symptoms Questionnaire) were obtained every 15min, alongside lane deviations, near crash events, and ocular indices of drowsiness. All subjective sleepiness measures increased with sleep deprivation for both age groups (p < 0.013). While most subjective sleepiness ratings significantly predicted driving impairment and drowsiness in younger adults (OR: 1.7-15.6, p < 0.02), this was only apparent for KSS, likelihood of falling asleep, and "difficulty staying in the lane for the older adults" (OR: 2.76-2.86, p = 0.02). This may be due to an altered perception of sleepiness in older adults, or due to lowered objective signs of impairment in the older group. Our data suggest that (i) younger and older drivers are aware of sleepiness; (ii) the best subjective scale may differ across age groups; and (iii) future research should expand on the best subjective measures to inform of crash risk in older adults to inform tailored educational road safety campaigns on signs of sleepiness.

Factors Associated with Fatigued Driving among Australian Truck Drivers: A Cross-Sectional Study

BACKGROUND

Fatigued driving is one of the leading factors contributing to road crashes in the trucking industry. The nature of trucking, prolonged working time, and irregular sleep patterns can negatively impact drivers' health and wellbeing. However, there is limited research in Australia investigating the impact of demographic, occupational, or lifestyle factors on fatigue among truck drivers.

OBJECTIVE

This cross-sectional study examines the role of demographic, occupational, lifestyle, and other health risk factors associated with fatigue among Australian truck drivers.

METHOD

This study was part of a larger study that used a short online survey with a follow-up telephone survey to capture in-depth information on a wide range of determinants related to truck drivers' physical and mental health outcomes. Fatigue was measured by three questions, including the frequency of fatigue, fatigue management training, and strategies used to combat fatigue. Multivariate regression analysis was used to determine the specific impact of demographics, occupational factors, lifestyle factors, and other health risk factors on fatigue.

RESULTS

In total, 332 drivers completed both the online and telephone surveys; 97% were male, representing drivers from broad age groups and professional experience. The odds of being in the high-risk fatigue group were nearly three times higher in drivers who worked 40-60 h compared to those who worked < 40 h. Poor sleep increased the odds of high-risk fatigue by seventimes (95% CI: 2.26-21.67, p = 0.001). Drivers who reported experiencing loneliness also had double the odds of being at high risk of fatigued driving.

CONCLUSIONS

The increased risk of fatigue in truck drivers is associated with prolonged working hours, poor sleep, and social aspects such as loneliness. Further interventions seeking to reduce driver fatigue should consider the impact of work schedules, the availability of quality sleeping spaces, and the level of social connections.

Effects of mental fatigue on risk preference and feedback processing in risk decision-making

Mental fatigue is a common phenomenon in modern people, especially after a long period of mental work. Individuals frequently have to make critical decisions when in a mentally fatigued state. As an important and complex cognitive function, risk decision-making might be influenced by mental fatigue, which is consequent with increased distraction and poor information processing. However, how mental fatigue shapes individuals’ decision-making remains relatively unclear. The purpose of this study was to examine the effect of mental fatigue on risk decision-making performance and risk-preference in a simple gambling task, using both behavioral methods and event-related potential techniques. Forty young adults were divided into a mental fatigue group and a no-fatigue group and participated in the experiments. Results showed that individuals with mental fatigue tended to be more risk-averse than those without fatigue when facing risk options. The P300 amplitudes were smaller and FRN amplitudes were larger in the mental fatigue group than in the no-fatigue group. These findings provide insight into a relationship between mental fatigue and risk decision-making, from the perspective of the neurological mechanism.

Impact of Safety Culture Implementation on Driving Performance among Oil and Gas Tanker Drivers: A Partial Least Squares Structural Equation Modelling (PLS-SEM) Approach

This research aims to develop a safety culture model by investigating the relationship between safety culture and driving performance. In previous studies, safety culture has been one of the factors that determine safety issues. These issues were then contextually transformed via a pilot study and organized in the form of a theoretical model. The data were collected from 307 oil and gas tanker drivers in Malaysia through questionnaire surveys. Consequently, structural equation models of partial least squares (PLS-SEM) were applied to statistically assess the final model of this study. The results showed that the implementation of safety culture contributes to driving performance at a substantial level; there is a strong association with an effect of 67.3%. The findings of this research would serve as a benchmark for decision-makers in the oil and gas transportation sector, as promoting an awareness of safety culture should boost the efficiency of drivers. This research fills a gap in knowledge by identifying that positive safety culture practices and mindset are direct antecedents for the improvement of driver performance and, thus, the avoidance of road accidents.

Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue

Driving fatigue is a serious issue for the transportation sector, decreasing the driver’s performance and increasing accident risk. This study aims to investigate how fatigue mediates the relationship between the nature of work factors and driving performance. The approach included a review of the previous studies to select the dimensional items for the data collection instrument. A pilot test to identify potential modification to the questionnaire was conducted, then structural equation modelling (SEM) was performed on a stratified sample of 307 drivers, to test the suggested hypotheses. Based on the results, five hypotheses have indirect relationships, four of which have a significant effect. Besides, the results show that driving fatigue partially mediates the relationship between the work schedule and driving performance and fully mediates in the relationship between work activities and driving performance. The nature of work and human factors is the most common reason related to road accidents. Therefore, the emphasis on driving performance and fatigue factors would thereby lead to preventing fatal crashes and life loss.

Effects of partially automated driving on the development of driver sleepiness

The objective of this study was to compare the development of sleepiness during manual driving versus level 2 partially automated driving, when driving on a motorway in Sweden. The hypothesis was that partially automated driving will lead to higher levels of fatigue due to underload. Eighty-nine drivers were included in the study using a 2 × 2 design with the conditions manual versus partially automated driving and daytime (full sleep) versus night-time (sleep deprived). The results showed that night-time driving led to markedly increased levels of sleepiness in terms of subjective sleepiness ratings, blink durations, PERCLOS, pupil diameter and heart rate. Partially automated driving led to slightly higher subjective sleepiness ratings, longer blink durations, decreased pupil diameter, slower heart rate, and higher EEG alpha and theta activity. However, elevated levels of sleepiness mainly arose from the night-time drives when the sleep pressure was high. During daytime, when the drivers were alert, partially automated driving had little or no detrimental effects on driver fatigue. Whether the negative effects of increased sleepiness during partially automated driving can be compensated by the positive effects of lateral and longitudinal driving support needs to be investigated in further studies.

A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification

Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.

Using Muse: Rapid Mobile Assessment of Brain Performance

The advent of mobile electroencephalography (mEEG) has created a means for large scale collection of neural data thus affording a deeper insight into cognitive phenomena such as cognitive fatigue. Cognitive fatigue - a neural state that is associated with an increased incidence of errorful performance - is responsible for accidents on a daily basis which at times can cost human lives. To gain better insight into the neural signature of cognitive fatigue in the present study we used mEEG to examine the relationship between perceived cognitive fatigue and human-event related brain potentials (ERPs) and electroencephalographic (EEG) oscillations in a sample of 1,000 people. As a secondary goal, we wanted to further demonstrate the capability of mEEG to accurately measure ERP and EEG data. To accomplish these goals, participants performed a standard visual oddball task on an Apple iPad while EEG data were recorded from a Muse EEG headband. Counter to traditional EEG studies, experimental setup and data collection was completed in less than seven minutes on average. An analysis of our EEG data revealed robust N200 and P300 ERP components and neural oscillations in the delta, theta, alpha, and beta bands. In line with previous findings we observed correlations between ERP components and EEG power and perceived cognitive fatigue. Further, we demonstrate here that a linear combination of ERP and EEG features is a significantly better predictor of perceived cognitive fatigue than any ERP or EEG feature on its own. In sum, our results provide validation of mEEG as a viable tool for research and provide further insight into the impact of cognitive fatigue on the human brain.

Traversing Community Attitudes and Interaction Experiences with Large Agricultural Vehicles on Rural Roads

Agriculture is one of Australia’s largest rural industries. Oversized and slow moving industry equipment and vehicles, hereafter referred to as large agricultural vehicles (LAVs), use public roads. Restrictions exist regarding their on-road operation, but whether this is a function of the risk that their on-road use represents is unknown. A convenience sample of community members was used to explore perspectives about LAVs’ presence on roads. An online survey was used to explore LAV interaction experiences, risk perceptions, and how best to promote safe interactions. Ethics approval was obtained. The participants’ (N = 239) exposure to LAVs on roads in the last 12 months was variable, but there were clear seasonal points when encounters could be expected. The participants indicated that LAVs have a right to drive on the road (94.8%), and most interactions were neutral, with four LAV crashes reported. Other vehicle types were perceived as representing a higher risk to rural road safety than LAVs. The use of the driver’s license test to increase knowledge about LAVs’ presence, how to respond, and the use of signs were suggested in order to improve safety. The participants commonly interacted with LAVs, and rarely experienced negative events such as crashes. Continued communication about LAV presence on rural roads is an important consideration in order to help ensure safe interactions.

The impact of a meal, snack, or not eating during the night shift on simulated driving performance post-shift

Objective:

The commute home following a night shift is associated with an increased risk for accidents. This study investigated the relationship between food intake during the night shift and simulated driving performance post-shift.

Methods:

Healthy non-shift working males (N=23) and females (N=16), aged 18–39 years (mean 24.5, standard deviation 5.0, years) participated in a seven-day laboratory study and underwent four simulated night shifts Participants were randomly allocated to one of three conditions: meal at night (N=12; 7 males), snack at night (N=13; 7 males) or no eating at night (N=14; 9 males). During the night shift at 00:30 hours, participants either ate a large meal (meal at night condition), a snack (snack at night condition), or did not eat during the night shift (no eating at night condition). During the second simulated night shift, participants performed a 40-minute York driving simulation at 20:00, 22:30, 01:30, 04:00, and 07:30 hours (similar time to a commute from work).

Results:

The effects of eating condition, drive time, and time-on-task, on driving performance were examined using mixed model analyses. Significant condition×time interactions were found, where at 07:30 hours, those in the meal at night condition displayed significant increases in time spent outside of the safe zone (percentage of time spent outside 10 km/hour of the speed limit and 0.8 meters of the lane center; P<0.05), and greater lane and speed variability (both P<0.01) compared to the snack and no eating conditions. There were no differences between the snack and no eating conditions.

Conclusion:

Driver safety during the simulated commute home is greater following the night shift if a snack, rather than a meal, is consumed during the shift.

Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks

Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the technology across the driving population. While it has been shown that three-dimensional (3D) operations are more suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multi-step approaches. However, computationally expensive techniques that achieve superior results on action recognition benchmarks (e.g. 3D convolutions, optical flow extraction) create bottlenecks for real-time, safety-critical applications on mobile devices. Here, we show how depthwise separable 3D convolutions, combined with an early fusion of spatial and temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. In particular, increased accuracy is achieved when assessment requires motion information, for example, when sunglasses conceal the eyes. Further, a custom TensorFlow-based smartphone application shows the true impact of various approaches on inference times and demonstrates the effectiveness of real-time monitoring based on out-of-sample data to alert a drowsy driver. Our model is pre-trained on ImageNet and Kinetics and fine-tuned on a publicly available Driver Drowsiness Detection dataset. Fine-tuning on large naturalistic driving datasets could further improve accuracy to obtain robust in-vehicle performance. Overall, our research is a step towards practical deep learning applications, potentially preventing micro-sleeps and reducing road trauma.

Driving Fatigue Detection from EEG Using a Modified PCANet Method

The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.

Classification of Driving Fatigue in High-Altitude Areas

Driving fatigue is one of the main causes of traffic accidents. Thus, to prevent traffic accidents and ensure traffic safety, the properties of driving fatigue at the wheel must be determined. The Qinghai–Tibet Plateau in China is known for its high elevation, causing hypoxia, and presence of severely cold areas; all these easily lead to fatigue during driving. This, in turn, seriously affects the traffic safety on the high-altitude highway. Therefore, the factors leading to driving fatigue and the influence of high-altitude on driving fatigue affecting the driver must be further studied. In this study, we classified and quantified driving fatigue according to the driving fatigue degree. We determined three levels of driving fatigues (i.e., mild, moderate, and severe fatigues) to present their influence on drivers. Our study shows that in this high-altitude area, drivers became fatigued within a significantly shorter time.

Drowsiness and driving performance on commuter trips

INTRODUCTION

Driver fatigue is a major road safety problem. While much is known about the effects of fatigue and the factors that contribute to it, fatigue on commuter trips has received comparatively little attention in road safety. Most interventions have focused on longer trips, while investigations of commuting have typically examined particular groups, such as shift workers.

METHOD

This study examined the effects of mild sleep deprivation on driving performance in simulated driving tasks in the morning and evening. Three groups of participants with different levels of sleep deprivation (Group 1: no deprivation; Group 2: two-hour deprivation; Group 3: four-hour deprivation) drove in a simulator for 45 min in the morning and evening, following a practice session the previous day.

RESULTS

Results showed that participants reported feeling more drowsy in the afternoon, and performance impairments (increased lane deviations) were most evident in the morning for those with sleep deprivation. Measurements of eye closure did not reflect drowsiness in participants, despite performance impairments.

PRACTICAL APPLICATIONS

These results suggest that mild levels of sleep deprivation (2 h), which many people regularly experience, can result in poor on-road performance, and that these effects are present in the morning, and on relatively short trips. These results warrant follow-up in naturalistic and on-road studies.

The Effects of Fatigue on Cognitive Performance in Police Officers and Staff During a Forward Rotating Shift Pattern

Background

Few studies have examined the effects of a forward rotating shift pattern on police employee performance and well-being. This study sought to compare sleep duration, cognitive performance, and vigilance at the start and end of each shift within a three-shift, forward rotating shift pattern, common in United Kingdom police forces.

Methods

Twenty-three police employee participants were recruited from North Yorkshire Police (mean age, 43 years). The participants were all working the same, 10-day, forward rotating shift pattern. No other exclusion criteria were stipulated. Sleep data were gathered using both actigraphy and self-reported methods; cognitive performance and vigilance were assessed using a customized test battery, comprising five tests: motor praxis task, visual object learning task, NBACK, digital symbol substitution task, and psychomotor vigilance test. Statistical comparisons were conducted, taking into account the shift type, shift number, and the start and end of each shift worked.

Results

Sleep duration was found to be significantly reduced after night shifts. Results showed a significant main effect of shift type in the visual object learning task and NBACK task and also a significant main effect of start/end in the digital symbol substitution task, along with a number of significant interactions.

Conclusion

The results of the tests indicated that learning and practice effects may have an effect on results of some of the tests. However, it is also possible that due to the fast rotating nature of the shift pattern, participants did not adjust to any particular shift; hence, their performance in the cognitive and vigilance tests did not suffer significantly as a result of this particular shift pattern.

Assessment of Sleep Deprivation and Fatigue Among Chemical Transportation Drivers in Chonburi, Thailand

BACKGROUND

Fatigue and sleepiness are inter-related and common among road transport drivers. In this study, sleep deprivation and fatigue among chemical transportation drivers were examined.

METHODS

A cross-sectional study surveying 107 drivers from three hazardous types of chemical production and transportation industries (nonflammable gases, flammable gases, and flammable liquids) was conducted. Data on sleep deprivation were collected using questionnaires of the Stanford Sleeping Scale and the Groningen Sleep Quality Scale. Fatigue was assessed using an interview questionnaire and a flicker fusion instrument.

RESULTS

Chemical drivers had a mean sleeping scale (Stanford Sleeping Scale) of 1.98 (standard deviation 1.00) and had a mean score of 1.89 (standard deviation 2.06) on the Groningen Sleep Quality Scale. High-risk drivers had higher scores in both the Stanford Sleeping Scale and the Groningen Sleep Quality Scale with a mean score of 2.59 and 4.62, respectively, and those differences reached statistical significance (p < 0.05). The prevalence of fatigue, as assessed through a critical flicker fusion analyzer, subjective fatigue question, and either of the instruments, was 32.32%, 16.16%, and 43.43%, respectively. Drivers who slept <7 hours and had poor sleep quality were found to have more fatigue than those who slept enough and well. Drivers who had a more sleepiness score resulted in significantly more objective fatigue than those who had a less sleepiness score.

CONCLUSION

Sleep quality and sleeping hour can affect a driver's fatigue. Optimization of work-rest model should be considered to improve productivity, driver retention, and road safety.

A contextual and temporal algorithm for driver drowsiness detection

This study designs and evaluates a contextual and temporal algorithm for detecting drowsiness-related lane. The algorithm uses steering angle, pedal input, vehicle speed and acceleration as input. Speed and acceleration are used to develop a real-time measure of driving context. These measures are integrated with a Dynamic Bayesian Network that considers the time dependencies in transitions between drowsiness and awake states. The Dynamic Bayesian Network algorithm is validated with data collected from 72 participants driving the National Advanced Driving Simulator. The algorithm has a significantly lower false positive rate than PERCLOS-the current gold standard-and baseline, non-contextual, algorithms under design parameters that prioritize drowsiness detection. Under these parameters, the algorithm reduces false positive rate in highway and rural environments, which are typically problematic for vehicle-based detection algorithms. This algorithm is a promising new approach to driver impairment detection and suggests contextual factors should be considered in subsequent algorithm development processes. It may be combined with comprehensive mitigation methods to improve driving safety.

The relative importance of real-time in-cab and external feedback in managing fatigue in real-world commercial transport operations

OBJECTIVE

Real-time driver monitoring systems represent a solution to address key behavioral risks as they occur, particularly distraction and fatigue. The efficacy of these systems in real-world settings is largely unknown. This article has three objectives: (1) to document the incidence and duration of fatigue in real-world commercial truck-driving operations, (2) to determine the reduction, if any, in the incidence of fatigue episodes associated with providing feedback, and (3) to tease apart the relative contribution of in-cab warnings from 24/7 monitoring and feedback to employers.

METHODS

Data collected from a commercially available in-vehicle camera-based driver monitoring system installed in a commercial truck fleet operating in Australia were analyzed. The real-time driver monitoring system makes continuous assessments of driver drowsiness based on eyelid position and other factors. Data were collected in a baseline period where no feedback was provided to drivers. Real-time feedback to drivers then occurred via in-cab auditory and haptic warnings, which were further enhanced by direct feedback by company management when fatigue events were detected by external 24/7 monitors. Fatigue incidence rates and their timing of occurrence across the three time periods were compared.

RESULTS

Relative to no feedback being provided to drivers when fatigue events were detected, in-cab warnings resulted in a 66% reduction in fatigue events, with a 95% reduction achieved by the real-time provision of direct feedback in addition to in-cab warnings (p < 0.01). With feedback, fatigue events were shorter in duration a d occurred later in the trip, and fewer drivers had more than one verified fatigue event per trip.

CONCLUSIONS

That the provision of feedback to the company on driver fatigue events in real time provides greater benefit than feedback to the driver alone has implications for companies seeking to mitigate risks associated with fatigue. Having fewer fatigue events is likely a reflection of the device itself and the accompanying safety culture of the company in terms of how the information is used. Data were analysed on a per-truck trip basis, and the findings are indicative of fatigue events in a large-scale commercial transport fleet. Future research ought to account for individual driver performance, which was not possible with the available data in this retrospective analysis. Evidence that real-time driver monitoring feedback is effective in reducing fatigue events is invaluable in the development of fleet safety policies, and of future national policy and vehicle safety regulations. Implications for automotive driver monitoring are discussed.

Driver sleepiness on YouTube: A content analysis

Driver sleepiness is a major contributor to severe crashes and fatalities on our roads. Many people continue to drive despite being aware of feeling tired. Prevention relies heavily on education campaigns as it is difficult to police driver sleepiness. The video sharing social media site YouTube is extremely popular, particularly with at risk driver demographics. Content and popularity of uploaded videos can provide insight into the quality of publicly accessible driver sleepiness information. The purpose of this research was to answer two questions; firstly, how prevalent are driver sleepiness videos on YouTube? And secondly, what are the general characteristics of driver sleepiness videos in terms of (a) outlook on driver sleepiness, (b) tone, (c) countermeasures to driver sleepiness, and, (d) driver demographics. Using a keywords search, 442 relevant videos were found from a five year period (2nd December 2009-2nd December 2014). Tone, outlook, and countermeasure use were thematically coded. Driver demographic and video popularity data also were recorded. The majority of videos portrayed driver sleepiness as dangerous. However, videos that had an outlook towards driver sleepiness being amusing were viewed more often and had more mean per video comments and likes. Humorous videos regardless of outlook, were most popular. Most information regarding countermeasures to deal with driver sleepiness was accurate. Worryingly, 39.8% of videos with countermeasure information contained some kind of ineffective countermeasure. The use of humour to convey messages about the dangers of driver sleepiness may be a useful approach in educational interventions.

Cognitive impairment in Crohn's disease is associated with systemic inflammation, symptom burden and sleep disturbance

BACKGROUND

Patients with Crohn's disease (CD) frequently complain of cognitive difficulties such as problems with concentration and clouding of thought, yet this has scarcely been objectively defined and underlying mechanisms remain unknown.

OBJECTIVE

The objective of this article is to objectively measure cognitive impairments in patients with CD compared with healthy controls, and if present, to identify potentially modifiable, contributing factors associated with cognitive impairment.

METHODS

CD patients and healthy age-/sex-matched controls completed surveys encompassing clinical, demographic, psychiatric, fatigue and sleep parameters. Contemporaneously, disease activity assessment with serum CRP, faecal calprotectin, Harvey-Bradshaw Index and the Subtle Cognitive Impairment test (SCIT) were performed, with the primary measure of response time (SCIT-RT) compared between groups. Multiple linear regression assessed for factors associated with slower SCIT-RT, denoting subtle cognitive impairment.

RESULTS

A total of 49 CD and 31 control individuals participated, with median age 44 years (range 22-65) and 43 years (21-63), respectively. Compared to controls, SCIT-RT was slower across all timepoints in CD patients (ANOVA p < 0.001). In multivariate analysis, serum CRP (standardised beta coefficient 0.27, 95% CI (0.02, 0.51)), abdominal pain (0.43 (0.16, 0.70)), plasma haemoglobin (1.55 (1.42, 1.68)), and concurrent fatigue (0.56 (0.25, 0.88)) were each independently associated with slower SCIT-RT in CD (each p < 0.05), with a trend for poorer sleep quality 0.54 (-0.03, 1.11) (p = 0.06), yet conversely, higher faecal calprotectin titres were associated with faster SCIT-RT (-1.77 (-1.79, -1.76), p < 0.01).

CONCLUSIONS

Patients with CD demonstrated subtle cognitive impairment utilising the objective SCIT, correlating with systemic inflammation and other disease burden measures, although higher faecal calprotectin titres were unexpectedly associated with less cognitive impairment.

Designing Fatigue Warning Systems: The perspective of professional drivers

Professional drivers have been characterized as experiencing heavy fatigue resulting from long driving time in their daily work. This study aimed to explore the potential demand of Fatigue Warning Systems (FWSs) among professional drivers as a means of reducing the danger of fatigue driving and to examine their opinions regarding the design of FWSs. Six focus groups with 35 participants and a questionnaire survey with 600 respondents were conducted among Chinese truck and taxi drivers to collect qualitative and quantitative data concerning the current situation of fatigue driving and opinions regarding the design of FWSs. The results revealed that both truck and taxi drivers had a positive attitude toward FWSs, and they hoped this system could not only monitor and warn them regarding their fatigue but also somewhat relieve their fatigue before they could stop and rest. As for warning signals, participants preferred auditory warnings, as opposed to visual, vibrotactile or electric stimuli. Interestingly, it was proposed that verbal warnings involving the information regarding consequences of fatigue driving or the wishes of drivers' family members would be more effective. Additionally, different warning patterns, including graded, single and continuous warnings, were discussed in the focus group. Finally, the participants proposed many other suggestions, as well as their concerns regarding FWSs, which will provide valuable information for companies who wish to develop FWSs for professional drivers.

Self-Reporting by Unsafe Drivers Is, with Education, More Effective than Mandatory Reporting by Doctors

STUDY OBJECTIVES

Health professionals are frequently required to report to relevant authorities all drivers who are potentially unsafe due to medical conditions. We aimed to assess both the effect of mandatory reporting (MR) on patient self-predicted behavior and what factors might encourage unsafe drivers to self-report to these authorities.

METHODS

We included 5 questions in the South Australian Health Omnibus Survey, an annual, community based, face-to-face survey. We asked (1) how subjects would behave towards their doctor in light of MR if they believed their licences were at risk due to a medical condition; and (2) which factor(s) would cause them to self-report to the same authorities.

RESULTS

Responses to 3,007 surveys (response rate 68.5%, age 15-98) showed that 9.0% would avoid diagnosis, lie to their doctor, or doctor shop in order to keep their licence; 30.8% were unaware of the legislated requirement to self-report; and 37.9% were unaware of potentially jeopardizing insurance support if they failed to comply. If educated in these 2 areas, warned about the dangers of driving against medical advice and instructed to do so by their doctor, then 95.8% of people would self-report to the authorities, a number significantly higher than could be reported by their doctors (91.0%).

CONCLUSIONS

MR causes 9.0% of people to predict to behave towards their doctor in a manner that reduces road safety. With education and encouragement to do so, more people will self-report to the authorities than could be reported by their doctors via the MR pathway.

COMMENTARY

A commentary on this article appears in this issue on page 287.

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p <; 0.05).

Driving fatigue in professional drivers: a survey of truck and taxi drivers

OBJECTIVES

Fatigue among truck drivers has been studied extensively; however, less is known regarding the fatigue experience of taxi drivers in heavily populated metropolitan areas. This study aimed to compare the differences and similarities between truck and taxi driver fatigue to provide implications for the fatigue management and education of professional drivers.

METHODS

A sample of 274 truck drivers and 286 taxi drivers in Beijing was surveyed via a questionnaire, which included items regarding work characteristics, fatigue experience, accident information, attitude toward fatigue, and methods of counteracting fatigue.

RESULTS

Driver fatigue was prevalent among professional drivers, and it was even more serious for taxi drivers. Taxi drivers reported more frequent fatigue experiences and were involved in more accidents. Among the contributing factors to fatigue, prolonged driving time was the most important factor identified by both driver groups. Importantly, the reason for the engagement in prolonged driving was neither due to the lack of awareness concerning the serious outcome of fatigue driving nor because of their poor detection of fatigue. The most probable reason was the optimism bias, as a result of which these professional drivers thought that fatigue was more serious for other drivers than for themselves, and they thought that they were effective in counteracting the effect of fatigue on their driving performance. Moreover, truck drivers tended to employ methods that require stopping to counteract fatigue, whereas taxi drivers preferred methods that were simultaneous with driving. Although both driver groups considered taking a nap as one of the most effective means to address fatigue, this method was not commonly used. Interestingly, these drivers were aware that the methods they frequently used were not the most effective means to counteract fatigue.

CONCLUSIONS

This study provides knowledge on truck and taxi drivers' characteristics in fatigue experience, fatigue attitude, and fatigue countermeasures, and these findings have practical implications for the fatigue management and education of professional drivers.

Effects of driving time on microsaccadic dynamics

Driver fatigue is a common cause of car accidents. Thus, the objective detection of driver fatigue is a first step toward the effective management of fatigue-related traffic accidents. Here, we investigated the effects of driving time, a common inducer of driver fatigue, on the dynamics of fixational eye movements. Participants drove for 2 h in a virtual driving environment while we recorded their eye movements. Microsaccade velocities decreased with driving time, suggesting a potential effect of fatigue on microsaccades during driving.

The effect of three low-cost engineering treatments on driver fatigue: A driving simulator study

Three engineering treatments were implemented in a driving simulator study to assess the effect of road-based measures on alleviating the symptoms of fatigue. Using results from previous research on the effect of circadian rhythms on fatigue-related crashes, two groups of male drivers were recruited for this study: young shift workers under the age of 35, who attended immediately after their night shift, and older drivers over the age of 45, who completed the study during the 'post lunch dip' period, after consuming lunch. Eye tracking (PERCLOS) and lateral driver performance measures were used to assess whether baseline measures of fatigue changed after drivers experienced each of the three treatments, which included variable message signs, chevrons and rumble strips. Results showed a marked difference in these measures between drivers' baseline (not fatigued) and experimental (fatigued) visits. There were also some reductions in lateral deviation and eye closure (as measured by PERCLOS) when the treatments were encountered, but no marked difference between the three treatments. These results suggest that in addition to driver- and vehicle-based methods currently employed to mitigate the effects of fatigue, the inclusion of such engineering measures may help alleviate fatigue-related impairments in driving, particularly if such treatments are implemented during long stretches of straight monotonous roads which are known to be associated with fatigue-related crashes. However, positive effects of the treatments were short lived, prompting the need for further investigations on their optimal frequency of presentation and combination to achieve maximum impact from these low-cost, road-based treatments.

Young drivers' perceptions of culpability of sleep-deprived versus drinking drivers

INTRODUCTION

Sleep-deprived driving can be as dangerous as alcohol-impaired driving, however, little is known about attitudes toward sleep-deprived drivers. This study examined the extent to which young drivers regard sleep-deprived compared to drinking drivers as culpable for a crash, and how their perceptions of driving while in these conditions differ.

METHOD

University student participants (N=295; M=20.4years, SD=1.3; 81% women) were randomly assigned to read one of five fatal motor-vehicle crash scenarios, which differed by aspects of the driver's condition. Culpability ratings for the drinking driver were higher than those for the sleep-deprived driver.

RESULTS

Qualitative findings revealed that driving while sleep-deprived was viewed as understandable, and driving after drinking was viewed as definitely wrong. The dangers of sleep-deprived driving remain under-recognized.

At home and away: measuring the sleep of Australian truck drivers

The causes of fatigue in truck drivers related to work hours have been studied extensively and are reasonably well understood. However, much less is known about how rest opportunities can be structured to optimise recovery from fatigue. The nature of the road transport industry often requires that rest be taken in various locations. New investigation in this area, focusing on sleep obtained in truck cabs and other non-home environments is critically important to complement existing understanding. This study examined sleep at home and in truck cabs, in truck drivers who were actively working during the time of the study. Thirty-seven male drivers aged between 24 and 63 years (age: 48.7 ± 9.0 years; mean ± SD) wore activity monitors (also known as 'sleep watches') and completed work and sleep diaries for a period of 21 days, recording their subjective fatigue levels before, during and after work shifts, and before and after sleep periods. They also self-rated their sleep quality and noted the number of times they woke during sleep periods. Analyses focused on home versus in-truck sleep periods. The subjective data suggested that a greater quantity (P<.001) and quality (P<.05) of sleep was obtained at home than in the truck, and that sleeping at home more effectively reduced fatigue levels (P<.001). The objective data showed trends towards longer sleep length at home, but other variables, including total sleep per 24h and sleep quality, showed no significant differences. This study demonstrates that measuring sleep quantity and quality in operational road transport environments is feasible. The findings caution against over-reliance on laboratory and simulator studies since there are critical aspects of the operating environment that cannot be validly studied in artificially controlled settings. This study is unique in its direct examination of sleep quantity and quality in truck drivers sleeping at home and away from home.

Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model

A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: (1) lack of generalizability, (2) failure to address individual variability in generalized models, and/or (3) lack of a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief "identification" tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability.

Risk factors for fatal crashes in rural Australia

This paper presents findings from the rural and remote road safety study, conducted in Queensland, Australia, from March 2004 till June 2007, and compares fatal crashes and non-fatal but serious crashes in respect of their environmental, vehicle and operator factors. During the study period there were 613 non-fatal crashes resulting in 684 hospitalised casualties and 119 fatal crashes resulting in 130 fatalities. Additional information from police sources was available on 103 fatal and 309 non-fatal serious crashes. Over three quarters of both fatal and hospitalised casualties were male and the median age in both groups was 34 years. Fatal crashes were more likely to involve speed, alcohol and violations of road rules and fatal crash victims were 2½ times more likely to be unrestrained inside the vehicle than non-fatal casualties, consistent with current international evidence. After controlling for human factors, vehicle and road conditions made a minimal contribution to the seriousness of the crash outcome. Targeted interventions to prevent fatalities on rural and remote roads should focus on reducing speed and drink driving and promoting seatbelt wearing.

The challenges and opportunities of technological approaches to fatigue management

There are a number of different strategies to mitigate the effects of fatigue in transportation and other occupational settings. Many are centered on regulatory or organizational approaches, such as work scheduling restriction and employer screening practices. While these generally benefit safety and productivity, there are clearly limitations to these approaches. Technologies that objectively detect or predict operator fatigue may be used to effectively complement or even supplant organizational or regulatory approaches. Over the past decade and a half, there have been considerable advances in relevant technologies, including onboard devices that monitor drivers' state or level of performance as well as devices that predict fatigue in advance of a work cycle or trip. In this paper, we discuss the challenges and opportunities for technological approaches to fatigue management, beginning with a discussion of the "ideal" system, followed by some of the general issues and limitations of current technologies. We also discuss some of the critical and outstanding issues related to the human interaction with these systems, including user acceptance and compliance. Finally, we discuss future directions in next generation technology for fatigue management.

Sleep disorders, medical conditions, and road accident risk

Sleep disorders and various common acute and chronic medical conditions directly or indirectly affect the quality and quantity of one's sleep or otherwise cause excessive daytime fatigue. This article reviews the potential contribution of several prevalent medical conditions - allergic rhinitis, asthma, chronic obstructive pulmonary disease, rheumatoid arthritis/osteoarthritis - and chronic fatigue syndrome and clinical sleep disorders - insomnia, obstructive sleep apnea, narcolepsy, periodic limb movement of sleep, and restless legs syndrome - to the risk for drowsy-driving road crashes. It also explores the literature on the cost-benefit of preventive interventions, using obstructive sleep apnea as an example. Although numerous investigations have addressed the impact of sleep and medical disorders on quality of life, few have specifically addressed their potential deleterious effect on driving performance and road incidents. Moreover, since past studies have focused on the survivors of driver crashes, they may be biased. Representative population-based prospective multidisciplinary studies are urgently required to clarify the role of the fatigue associated with common ailments and medications on traffic crash risk of both commercial and non-commercial drivers and to comprehensively assess the cost-effectiveness of intervention strategies.

Using microstate intensity for the analysis of spontaneous EEG: tracking changes from alert to the fatigue state

Fatigue is a negative symptom of many illnesses and also has major implications for road safety. This paper presents results using a method called microstate segmentation (MSS). It was used to distinguish changes from an alert to a fatigue state. The results show a significant increase in MSS instantaneous amplitude during the fatigue state. Plotting the linear gradient of the nonlinear part of the phase data from the MSS also showed a significant difference (P<0.01) in the gradients of the alert state compared to the fatigue state. The results suggest that MSS can be used in analyzing spontaneous electroencephalography (EEG) signals to detect changes in physiological states. The results have implications for countermeasures used in detecting fatigue.

Improving classification rates for use in fatigue countermeasure devices using brain activity

Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor the onset of fatigue. This paper examines using electroencephalography (EEG) signals to classify fatigue and alert states as a function of subjective self-report, driving performance and physiological symptoms. The results show that EEG classification network for fatigue improved from 75% to 80% when these factors are applied, especially when the data is grouped by subjective self-report of fatigue with classification accuracy improving to 84.5%.

Fell asleep and caused a fatal head-on crash? A case study of multidisciplinary in-depth analysis vs. the court

OBJECTIVES

Recognizing road accidents as sleep/fatigue-related is a challenging task due to the lack of validated criteria and reliable devices (cf. breath analyzer for alcohol levels). Consequently, it is difficult to incorporate fatigue in operationalized terms into either traffic or criminal law. Finnish Road Traffic Act explicitly forbids driving while tired but only on a general level regarding the driver's fitness to drive. The aim was to explore and compare the discussions held and conclusions reached by multidisciplinary accident-investigation teams and Finnish courts.

METHODS

We describe nine fatal head-on crashes in which, according to the multidisciplinary investigation teams, the guilty nonintoxicated surviving driver had fallen asleep and caused the death of an occupant in the other vehicle.

RESULTS

Despite the obvious difficulties with the data collection, the investigation teams provided sufficient information and explanation as to why falling asleep was the most probable cause of these nine accidents. On the other hand, there was wide variation in the court discussions and decisions. The court extensively deliberated on the role of fatigue in the four cases and only one driver was charged under the article of the Road Traffic Act covering driver fatigue.

CONCLUSIONS

In conclusion, this study illustrates difficulties in enforcing the law that forbids driving while tired. Although multidisciplinary investigation teams analyze fatal accidents for safety-research purposes, and have a wider degree of freedom when making their conclusions, we believe that such expert evidence would be beneficial to the courts when they consider similar cases.

The psychophysiological determinants of fatigue

Driver fatigue is a major risk for road accidents that can often result in injury and death. However, considerable debate still exists concerning factors associated with driver fatigue. Because of the complex nature of fatigue, this paper reports a study that investigated both physiological and psychological determinants of fatigue. Three fatigue outcome measures were used, including a physiological, psychological and a combined physiological and psychological measure. Fifty participants performed a driving simulator task till they showed symptoms of fatigue and were assessed before and after the task. Significant factors associated with physiological fatigue included higher levels of baseline delta activity and an extraverted personality. Factors related to the psychological fatigue outcome measure included sleepiness, low healthy lifestyle status, an extraverted personality and tension-prone personality, and negative mood states. The combined fatigue outcome measure was associated with factors such as a tension-prone and extraverted personality, low systolic blood pressure, and negative mood states. The findings emphasize the importance of assessing fatigue using a range of outcome measures in order to achieve a thorough understanding of what factors contribute to fatigue and highlight the need to develop fatigue countermeasures that employ a broad range of measures.