Development of fatigue symptoms during simulated driving

A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition

Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers' mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset. The model first selects critical EEG channels using the Differential Evolution (DE) algorithm, extracting important electrode channel information to enhance recognition accuracy. These electrode channels are used to construct a Functional Brain Network (FBN), from which the topological feature set is extracted. Empirical Mode Decomposition (EMD) is then applied to extract the intrinsic mode components as network nodes, thereby reducing the influence of the number of electrode channels on the brain functional network. The topological features extracted from these components form the suboptimal feature set. To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. The optimal feature subsets were input into various classifiers, and the results showed that the K-Nearest Neighbor (KNN)-based classifier achieved the highest recognition accuracy of 96.11% ± 0.43%, demonstrating the method's stability and robustness. Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention.

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

BACKGROUND

Driving fatigue is a significant concern in contemporary society, contributing to a considerable number of traffic accidents annually. This study explores novel methods for fatigue detection, aiming to enhance driving safety.

METHODS

This study utilizes electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to monitor driver fatigue during simulated driving experiments lasting up to 7 h.

RESULTS

Analysis reveals a significant correlation between behavioral data and hemodynamic changes in the prefrontal lobe, particularly around the 4 h mark, indicating a critical period for driver performance decline. Despite a small participant cohort, the study's outcomes align closely with established fatigue standards for drivers.

CONCLUSIONS

By integrating fNIRS into non-voluntary attention brain function experiments, this research demonstrates promising efficacy in accurately detecting driving fatigue. These findings offer insights into fatigue dynamics and have implications for shaping effective safety measures and policies in various industrial settings.

Reducing passive driver fatigue through a suitable secondary motor task by means of an interactive seating system

OBJECTIVE

The primary objective of the study was to evaluate the effect of a secondary motor task induced by an interactive seating system (IASS) on passive driver fatigue in a monotonous simulated driving task. The effect was compared to that of a state-of-the-art massage seating system (MS), which may reduce monotony through additional tactile stimuli. The secondary objective was to compare the user experience of both systems.

METHOD

The independent variables were three conditions: one with the IASS, another with the MS, and a control without intervention. The study included seven dependent variables in total: a rating of subjective fatigue, three parameters measuring lane keeping ability, and three parameters reflecting fatigue-related eye movements. The duration of the simulator ride was 40 min in each condition. The study included thirty-five subjects.

RESULTS

The assessment of subjective fatigue and lane keeping showed that the use of the IASS resulted in significantly lower passive driver fatigue compared to the massage and control conditions. The alerting effects of the IASS were also reflected by an increased eyelid distance. Frequency and duration of blinks, however, showed no clear patterns of fatigue over time in any of the conditions. Thus, both parameters did not seem be suitable to capture passive driver fatigue in this study. Regarding user experience, the subjects preferred the IASS over the MS as well.

CONCLUSION

The IASS showed a strong potential as an effective measure against passive driver fatigue within monotonous driving situations. The MS, on the other hand, induced no measurable effects.

A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm

This study aims to identify new electroencephalography (EEG) features for the detection of driving fatigue. The most common EEG feature in driving fatigue detection is the power spectral density (PSD) of five frequency bands, i.e., alpha, beta, gamma, delta, and theta bands. PSD has proved to be useful, however its flaw is that it covers much implicit information of the time domain. In this study we propose a new approach, which combines ensemble empirical mode decomposition (EEMD) and PSD, to explore new EEG features for driving fatigue detection. Through EEMD we get a series of intrinsic mode function (IMF) components, from which we can extract PSD features. We used six features to compare with the proposed features, including the PSD of five frequency bands, PSD of empirical mode decomposition (EMD)-IMF components, PSD, permutation entropy (PE), sample entropy (SE), and fuzzy entropy (FE) of EEMD-IMF components, and common spatial pattern. Feature overlap ratio and multiple machine learning methods were applied to evaluate these feature extraction approaches. The results show that the classification accuracy and overlap ratio of experiments based on IMF's energy spectrum is far superior to other features. Through channel optimization and a comparison of accuracy, we conclude that our new feature selection approach has a better performance based on the modified hierarchical extreme learning machine algorithm with Particle Swarm Optimization (PSO-H-ELM) classifier, which has the highest average accuracy of 97.53%.

Functional Connectivity Analysis and Detection of Mental Fatigue Induced by Different Tasks Using Functional Near-Infrared Spectroscopy

Objectives

The objective of this study was to investigate common functional near-infrared spectroscopy (fNIRS) features of mental fatigue induced by different tasks. In addition to distinguishing fatigue from non-fatigue state, the early signs of fatigue were also studied so as to give an early warning of fatigue.

Methods

fNIRS data from 36 participants were used to investigate the common character of functional connectivity network corresponding to mental fatigue, which was induced by psychomotor vigilance test (PVT), cognitive work, or simulated driving. To analyze the network reorganizations quantitatively, clustering coefficient, characteristic path length, and small worldness were calculated in five sub-bands (0.6-2.0, 0.145-0.600, 0.052-0.145, 0.021-0.052, and 0.005-0.021 Hz). Moreover, we applied a random forest method to classify three fatigue states.

Results

In a moderate fatigue state: the functional connectivity strength between brain regions increased overall in 0.021-0.052 Hz, and an asymmetrical pattern of connectivity (right hemisphere > left hemisphere) was presented. In 0.052-0.145 Hz, the connectivity strength decreased overall, the clustering coefficient decreased, and the characteristic path length increased significantly. In severe fatigue state: in 0.021-0.052 Hz, the brain network began to deviate from a small-world pattern. The classification accuracy of fatigue and non-fatigue was 85.4%. The classification accuracy of moderate fatigue and severe fatigue was 82.8%.

Conclusion

The preliminary research demonstrates the feasibility of detecting mental fatigue induced by different tasks, by applying the functional network features of cerebral hemoglobin signal. This universal and robust method has the potential to detect early signs of mental fatigue and prevent relative human error in various working environments.

Usability and Effectiveness of Immersive Virtual Grocery Shopping for Assessing Cognitive Fatigue in Healthy Controls: Protocol for a Randomized Controlled Trial

BACKGROUND

Cognitive fatigue (CF) is a human response to stimulation and stress and is a common comorbidity in many medical conditions that can result in serious consequences; however, studying CF under controlled conditions is difficult. Immersive virtual reality provides an experimental environment that enables the precise measurement of the response of an individual to complex stimuli in a controlled environment.

OBJECTIVE

We aim to examine the development of an immersive virtual shopping experience to measure subjective and objective indicators of CF induced by instrumental activities of daily living.

METHODS

We will recruit 84 healthy participants (aged 18-75 years) for a 2-phase study. Phase 1 is a user experience study for testing the software functionality, user interface, and realism of the virtual shopping environment. Phase 2 uses a 3-arm randomized controlled trial to determine the effect that the immersive environment has on fatigue. Participants will be randomized into 1 of 3 conditions exploring fatigue response during a typical human activity (grocery shopping). The level of cognitive and emotional challenges will change during each activity. The primary outcome of phase 1 is the experience of user interface difficulties. The primary outcome of phase 2 is self-reported CF. The core secondary phase 2 outcomes include subjective cognitive load, change in task performance behavior, and eye tracking. Phase 2 uses within-subject repeated measures analysis of variance to compare pre- and postfatigue measures under 3 conditions (control, cognitive challenge, and emotional challenge).

RESULTS

This study was approved by the scientific review committee of the National Institute of Nursing Research and was identified as an exempt study by the institutional review board of the National Institutes of Health. Data collection will begin in spring 2021.

CONCLUSIONS

Immersive virtual reality may be a useful research platform for simulating the induction of CF associated with the cognitive and emotional challenges of instrumental activities of daily living.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04883359; http://clinicaltrials.gov/ct2/show/NCT04883359.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/28073.

Oculomotor Fatigue and Neuropsychological Assessments mirror Multiple Sclerosis Fatigue

Fatigue is a major complaint in MS. Up to now no objective assessment tools have been established which hampers any treatment approach. Previous work has indicated an association of fatigue with cognitive measures of attention. Oculomotor tests have been established in healthy individuals as a read-out of fatigue, and to some extent in MS patients. Based on these observations we compared two groups of MS patients, one with fatigue (n=28) and one without fatigue (n=21) and a group of healthy subjects (n=15) with a standardised computerised measure of alertness and an oculomotor stress test. Patients with fatigue showed highly significant changes of their saccade dynamics as defined by the Main Sequence and Phase Plane plots: They showed slowing of saccades, the characteristical fatigue double peak, and an asymmetrical phase plane. Oculomotor tests differentiated significantly between fatigue and fatigabiliy in our MS patients. They also showed significantly worse performance in the alertness test as well as in the oculomotor task. Significantly slower reaction times were observed for tonic alertness in 2 series without a cue (p=.025 and p=.037) but not in phasic alertness with a cue (p=.24 and p=.34). Performance was influenced by disability as well as by affective state. We conclude, when controlling for disability and depression, saccadic stress tests and alertness tests could be used as an objective read-out for fatigability and fatigue in MS patients.

Effects of a Motion Seat System on Driver's Passive Task-Related Fatigue: An On-Road Driving Study

Passive task-related (TR) fatigue caused by monotonous driving can negatively affect driving safety by impairing driver alertness and performance. This study aims to evaluate the effectiveness of a motion seat system on the driver’s passive TR fatigue in terms of driving performance, physiological response, and subjective fatigue by using automotive and physiological sensors those applicable to on-road driving environment. Twenty drivers (5 females and 15 males; age = 38.5 ± 12.2) with more than two years of driving experience participated in an on-road experiment with two driving conditions: driving in the static seat condition during the first half of the driving session and then in the static (static–static, SS) or motion seat (static–motion, SM) condition during the second half. The SM condition showed significantly lower passive TR fatigue by 4.4~56.5% compared to the SS condition in terms of the standard deviation of velocity, percentage of eyelid closure rate (PERCLOS), and the ratio of low- to high-frequency power (LF/HF) of electrocardiography signals. The drivers rated significantly lower subjective state changes of overall fatigue, mental fatigue, passive TR fatigue, drowsiness, and decreased concentration in the SM condition than those in the SS condition. The findings of the study support the use of a motion seat system can be an effective countermeasure to reduce passive TR fatigue.

Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study

Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject’s drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers’ drowsiness, compared to the other techniques.

Investigating an Integrated Sensor Fusion System for Mental Fatigue Assessment for Demanding Maritime Operations

Human-related issues are currently the most significant factor in maritime causalities, especially in demanding operations that require coordination between two or more vessels and/or other maritime structures. Some of these human-related issues include incorrect, incomplete, or nonexistent following of procedures; lack of situational awareness; and physical or mental fatigue. Among these, mental fatigue is especially dangerous, due to its capacity to reduce reaction time, interfere in the decision-making process, and affect situational awareness. Mental fatigue is also especially hard to identify and quantify. Self-assessment of mental fatigue may not be reliable and few studies have assessed mental fatigue in maritime operations, especially in real time. In this work we propose an integrated sensor fusion system for mental fatigue assessment using physiological sensors and convolutional neural networks. We show, by using a simulated navigation experiment, how data from different sensors can be fused into a robust mental fatigue assessment tool, capable of achieving up to 100 % detection accuracy for single-subject classification. Additionally, the use of different sensors seems to favor the representation of the transition between mental fatigue states.

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.

Name that tune: Mitigation of driver fatigue via a song naming game

Fatigued driving contributes to a substantial number of motor vehicle accidents each year. Music listening is often employed as a countermeasure during driving in order to mitigate the effects of fatigue. Though music listening has been established as a distractor in the sense that it increases cognitive load during driving, it is possible that increased cognitive load is desirable under particular circumstances. For instance, during situations that typically result in cognitive underload, such as driving in a low-traffic monotonous stretch of highway, it may be beneficial for cognitive load to increase, thereby necessitating allocation of greater cognitive resources to the task of driving and attenuating fatigue. In the current study, we employed a song-naming game as a countermeasure to fatigued driving in a simulated monotonous environment. During the first driving session, we established that driving performance deteriorates in the absence of an intervention following 30min of simulated driving. During the second session, we found that a song-naming game employed at the point of fatigue onset was an effective countermeasure, as reflected by simulated driving performance that met or exceeded fresh driving behavior and was significantly better relative to fatigued performance during the first driving session.

Physiological signal analysis for fatigue level of experienced and inexperienced drivers

OBJECTIVE

We studied the changes in driving fatigue levels of experienced and inexperienced drivers at 3 periods of the day: 9:00 a.m.-12:00 p.m., 12:00 p.m.-2:00 p.m., and 4:00 p.m.-6:00 p.m.

METHODS

Thirty drivers were involved in 120-min real-car driving, and sleepiness ratings (Stanford Sleepiness Scale, SSS; Hoddes et al. 1973 ), electroencephalogram (EEG) signals, and heart rates (HRs) were recorded. Together with principal component analysis, the relationship between EEG signals and HR was explored and used to determine a comprehensive indicator of driving fatigue. Then the comprehensive indicator was assessed via paired t test.

RESULTS

Experienced and inexperienced drivers behaved significantly differently in terms of subjective fatigue during preliminary trials. At the beginning of trials and after termination, subjective fatigue level was aggravated with prolonged continuous driving. Moreover, we discussed the changing rules of EEG signals and HR and found that with prolonged time, the ratios of δ and β waves significantly declined, whereas that of the θ wave significantly rose. The ratio of (α + θ)/β significantly rose both before trials and after termination, but HR dropped significantly. However, one-factor analysis of variance shows that driving experience significantly affects the θ wave, (α + θ)/β ratio, and HR.

CONCLUSIONS

We found that in a monotonous road environment, fatigue symptoms occurred in inexperienced drivers and experienced drivers after about 60 and 80 min of continuous driving, respectively. Therefore, as for drivers with different experiences, restriction on continuous driving time would avoid fatigued driving and thereby eliminate traffic accidents. We find that the comprehensive indicator changes significantly with fatigue level. The integration of different indicators improves the recognition accuracy of different driving fatigue levels.

Correlation between Driver Subjective Fatigue and Bus Lateral Position in a Driving Simulator

BACKGROUND

Driver fatigue as a leading cause of death in the transportation industry can impair the driving performance in long-distance driving task. Studies on the links of driver subjective fatigue and the bus lateral position are still an exploratory issue that requires further investigation. This study aimed to determine the correlation between the driver subjective fatigue and the bus lateral position in a driving simulator.

METHODS

This descriptive-analytical research was conducted on 30 professional male bus drivers participated in a two-hour driving session. The driver subjective fatigue was assessed by the Fatigue Visual Analogue Scale (F-VAS) at 10-min intervals. Simultaneously, the performance measures of lane drifting as the mean and standard deviation of the bus lateral position (SDLP) were calculated during the simulated driving task. Descriptive statistics and the Spearman correlation coefficient were used to describe and analyze the data.

RESULTS

Fatigue levels had an increasing trend as the time-on-task of driving increased. Time-on-task of driving had the greatest effect on the fatigue self-evaluation (r = 0.605, p < 0.0001). The results showed a significant correlation between fatigue self-evaluation and bus lateral position (r = 0.567, p < 0.0001).

CONCLUSION

As the time of driving increased, driving performance was affected adversely, as shown by the increase in the SDLP. Even so, the effect of individual differences on driving performance should not be overlooked. This work concludes that predicting the state of a driver fatigue based on the group mean data has some complications for any application.

Are drivers aware of sleepiness and increasing crash risk while driving?

Drivers are advised to take breaks when they feel too tired to drive, but there is question over whether they are able to detect increasing fatigue and sleepiness sufficiently to decide when to take a break. The aim of this study was to investigate the extent to which drivers have access to cognitive information about their current state of sleepiness, likelihood of falling asleep, and the implications for driving performance and the likelihood of crashing. Ninety drivers were recruited to do a 2h drive in a driving simulator. They were divided into three groups: one made ratings of their sleepiness, likelihood of falling asleep and likelihood of crashing over the next few minutes at prompts occurring at 200s intervals throughout the drive, the second rated sleepiness and likelihood of falling asleep at prompts but pressed a button on the steering wheel at any time if they felt they were near to crashing and the third made no ratings and only used a button-press if they felt a crash was likely. Fatigue and sleepiness was encouraged by monotonous driving conditions, an imposed shorter than usual sleep on the night before and by afternoon testing. Drivers who reported that they were possibly, likely or very likely to fall asleep in the next few minutes, were more than four times more likely to crash subsequently. Those who rated themselves as sleepy or likely to fall asleep had a more than 9-fold increase in the hazards of a centerline crossing compared to those who rated themselves as alert. The research shows clearly that drivers can detect changes in their levels of sleepiness sufficiently to make a safe decision to stop driving due to sleepiness. Therefore, road safety policy needs to move from reminding drivers of the signs of sleepiness and focus on encouraging drivers to respond to obvious indicators of fatigue and sleepiness and consequent increased crash risk.

The impact of continuous driving time and rest time on commercial drivers' driving performance and recovery

PROBLEM

This real road driving study was conducted to investigate the effects of driving time and rest time on the driving performance and recovery of commercial coach drivers.

METHODS

Thirty-three commercial coach drivers participated in the study, and were divided into three groups according to driving time: (a) 2 h, (b) 3 h, and (c) 4 h. The Stanford Sleepiness Scale (SSS) was used to assess the subjective fatigue level of the drivers. One-way ANOVA was employed to analyze the variation in driving performance.

RESULTS

The statistical analysis revealed that driving time had a significant effect on the subjective fatigue and driving performance measures among the three groups. After 2 h of driving, both the subjective fatigue and driving performance measures began to deteriorate. After 4 h of driving, all of the driving performance indicators changed significantly except for depth perception. A certain amount of rest time eliminated the negative effects of fatigue. A 15-minute rest allowed drivers to recover from a two-hour driving task. This needed to be prolonged to 30 min for driving tasks of 3 to 4 h of continuous driving.

PRACTICAL IMPLICATIONS

Drivers' attention, reactions, operating ability, and perceptions are all affected in turn after over 2 h of continuous driving. Drivers should take a certain amount of rest to recover from the fatigue effects before they continue driving.

Specific sleepiness symptoms are indicators of performance impairment during sleep deprivation

Drivers are not always aware that they are becoming impaired as a result of sleepiness. Using specific symptoms of sleepiness might assist with recognition of drowsiness related impairment and help drivers judge whether they are safe to drive a vehicle, however this has not been evaluated. In this study, 20 healthy volunteer professional drivers completed two randomized sessions in the laboratory - one under 24h of acute sleep deprivation, and one with alcohol. The Psychomotor Vigilance Task (PVT) and a 30min simulated driving task (AusEdTM) were performed every 3-4h in the sleep deprivation session, and at a BAC of 0.00% and 0.05% in the alcohol session, while electroencephalography (EEG) and eye movements were recorded. After each test session, drivers completed the Karolinska Sleepiness Scale (KSS) and the Sleepiness Symptoms Questionnaire (SSQ), which includes eight specific sleepiness and driving performance symptoms. A second baseline session was completed on a separate day by the professional drivers and in an additional 20 non-professional drivers for test-retest reliability. There was moderate test-retest agreement on the SSQ (r=0.59). Significant correlations were identified between individual sleepiness symptoms and the KSS score (r values 0.50-0.74, p<0.01 for all symptoms). The frequency of all SSQ items increased during sleep deprivation (χ(2) values of 28.4-80.2, p<0.01 for all symptoms) and symptoms were related to increased subjective sleepiness and performance deterioration. The symptoms "struggling to keep your eyes open", "difficulty maintaining correct speed", "reactions were slow" and "head dropping down" were most closely related to increased alpha and theta activity on EEG (r values 0.49-0.59, p<0.001) and "nodding off to sleep" and "struggling to keep your eyes open" were related to slow eye movements (r values 0.67 and 0.64, p<0.001). Symptoms related to visual disturbance and impaired driving performance were most accurate at detecting severely impaired driving performance (AUC on ROC curve of 0.86-0.91 for detecting change in lateral lane position greater than the change at a BAC of 0.05%). Individual sleepiness symptoms are related to impairment during acute sleep deprivation and might be able to assist drivers in recognizing their own sleepiness and ability to drive safely.

A study on the effects of fatigue driving and drunk driving on drivers' physical characteristics

OBJECTIVE

The purpose of this study was to analyze the effects of fatigue driving and drunk driving on drivers' physical characteristics; to analyze the differences in drivers' physical characteristics affected by different kinds of fatigue; and to compare the differences in the effects of the 2 driving states, fatigue driving and drunk driving.

METHODS

Twenty-five participants' physical characteristics were collected under 5 controlled situations: normal, tired driving, drowsy driving, drowsiness + tired driving, and drunk driving. In this article, fatigue driving refers to tiredness and drowsiness and includes 3 situations: tired driving, drowsy driving, and drowsiness + tired driving. The drivers' physical characteristics were measured in terms of 9 parameters: systolic blood pressure (SBP), heart rate (HR), eyesight, dynamic visual acuity (DVA), time for dark adaption (TDA), reaction time to sound (RTS), reaction time to light (RTL), deviation of depth perception (DDP), and time deviation of speed anticipation (TDSA). They were analyzed using analysis of variance (ANOVA) with repeated measures. Binary logistical regression analysis was used to explain the relationship between drivers' physical characteristics and the two driving states.

RESULTS

Most of the drivers' physical characteristic parameters were found to be significantly different under the influence of different situations. Four indicators are significantly affected by fatigue driving during deep fatigue (in decreasing order of influence): HR, RTL, SBP and RTS. HR and RTL are significant in the logistical regression model of the drowsiness + tired driving situation and normal situations. Six indicators of the drivers' physical characteristics are significantly affected by drunk driving (in decreasing order of influence): SBP, RTL, DDP, eyesight, RTS, and TDSA. SBP and DDP have a significant effect in the logistical regression model of the drunk driving situation and the normal situation.

CONCLUSIONS

Both fatigue driving and drunk driving are found to impair drivers' physical characteristics. However, their impacts on the parameters SBP, HR, eyesight, and TDSA are different. A driver's physical characteristics will be impaired more seriously when he continues driving while drowsy, compared to driving under normal situation. These findings contribute to the current research on identifying drivers' driving state and quantifying the effects of fatigue driving and drunk driving on driving ability and driving behavior.

Regional brain wave activity changes associated with fatigue

Assessing brain wave activity is a viable strategy for monitoring fatigue when performing tasks such as driving, and numerous studies have been conducted in this area. However, results of a systematic review on changes in brain wave activity associated with fatigue have revealed equivocal findings. This study investigated brain wave activity associated with fatigue in 48 nonprofessional healthy drivers as they participated in a simulated driving task until they fatigued. The results showed that as a person fatigues, slow wave activity increased over the entire cortex, in theta and in alpha 1 and 2 bands, while no significant changes were found in delta wave activity. Substantial increases also occurred in fast wave activity, though mostly in frontal sites. The results suggest that as a person fatigues, the brain loses capacity and slows its activity, and that attempts to maintain vigilance levels lead to increased beta activity.

Positive effects of Red Bull® Energy Drink on driving performance during prolonged driving

BACKGROUND

The purpose of this study was to examine if Red Bull® Energy Drink can counteract sleepiness and driving impairment during prolonged driving.

METHODS

Twenty-four healthy volunteers participated in this double-blind placebo-controlled crossover study. After 2 h of highway driving in the STISIM driving simulator, subjects had a 15-min break and consumed Red Bull® Energy Drink (250 ml) or placebo (Red Bull® Energy Drink without the functional ingredients: caffeine, taurine, glucuronolactone, B vitamins (niacin, pantothenic acid, B6, B12), and inositol) before driving for two additional hours. A third condition comprised 4 h of uninterrupted driving. Primary parameter was the standard deviation of lateral position (SDLP), i.e., the weaving of the car. Secondary parameters included SD speed, subjective driving quality, sleepiness, and mental effort to perform the test.

RESULTS

No significant differences were observed during the first 2 h of driving. Red Bull® Energy Drink significantly improved driving relative to placebo: SDLP was significantly reduced during the 3rd (p < 0.046) and 4th hour of driving (p < 0.011). Red Bull® Energy Drink significantly reduced the standard deviation of speed (p < 0.004), improved subjective driving quality (p < 0.0001), and reduced mental effort to perform the test (p < 0.024) during the 3rd hour of driving. Subjective sleepiness was significantly decreased during both the 3rd and 4th hour of driving after Red Bull® Energy Drink (p < 0.001 and p < 0.009, respectively). Relative to uninterrupted driving, Red Bull® Energy Drink significantly improved each parameter.

CONCLUSION

Red Bull® Energy Drink significantly improves driving performance and reduces driver sleepiness during prolonged highway driving.

Monitoring drivers' mental workload in driving simulators using physiological measures

Many traffic accidents are caused by, or at least related to, inadequate mental workload, when it is either too low (vigilance) or too high (stress). Creating variations in mental workload and accident-prone driving for research purposes is difficult in the real world. In driving simulators the measurement of driver mental workload is relatively easily conducted by means of physiological measures, although good research skills are required and it is time-consuming. The fact that modern driving simulator environments are laboratory-equivalent nowadays allows full control with respect to environmental conditions, scenarios and stimuli, and enables physiological measurement of parameters of mental workload such as heart rate and brain activity. Several examples are presented to illustrate the potential of modern high-standard driving simulator environments regarding the monitoring of drivers' mental workload during task performance.

The Relationship Between Spectral Changes in Heart Rate Variability and Fatigue

Fatigue is a prevalent problem in the workplace and a common symptom of many diseases. However, its relationship with the autonomic nervous system, specifically with sympathetic arousal, needs clarification. The objective of this study was to determine the association between fatigue and heart rate variability (HRV). HRV is regarded as an indicator of the autonomic regulation activity of heart rate, specifically sympathetic and parasympathetic activity. Spectral changes in low-frequency (LF; 0.04–0.15 Hz) and high-frequency (HF; 0.15–0.4 Hz) components of HRV have been reported to be associated with distressing conditions such as hemorrhagic shock, acute myocardial infarction, elevated anxiety, and depressed mood. While HRV changes have been found in persons with chronic fatigue syndrome, its association with fatigue in healthy individuals still needs clarification. HRV was assessed in a total of 50 participants who were asked to perform a task until becoming fatigued. Low-frequency HRV activity increased, while indices of parasympathetic modulation such as RMSSD and pNN50 remained stable as participants experienced fatigue, suggesting that fatigue in healthy individuals may be associated with increased sympathetic arousal. In addition, employing multiple regression analyses, we could positively associate the change in LF/HF HRV ratio from baseline to fatigue with factors such as emotional stability, warmth and tension and negatively associate it with social boldness and self-reported levels of vigor.

Alertness maintaining tasks (AMTs) while driving

We evaluated the effectiveness of alertness maintaining tasks (AMTs) on driver performance, subjective feelings, and psychophysiological state in monotonous simulated driving in two experiments. In the first experiment, 12 professional truck drivers participated in five sessions of simulated driving: driving only, driving with one of three AMTs (counterbalanced), and driving while listening to music. AMTs were not equally effective in maintaining alertness. The trivia AMT prevented driving performance deterioration, and increased alertness (measured by standardized HRV). The choice reaction time AMT was least demanding but also increased subjective sleepiness and reduced arousal (measured by alpha/beta ratio). The working memory AMT caused a significant decrement in driving speed, increased subjective fatigue, and was regarded by the participants as detrimental to driving. Trivia was preferred by the majority of the drivers over the other two AMTs. Experiment 2 further examined the utility of the trivia AMT. When the drivers engaged in the trivia AMT they maintained better driving performance and perceived the driving duration as shorter than the control condition. The two experiments demonstrated that AMTs can have a positive effect on alertness. The effect is localized in the sense that it does not persist beyond the period of the AMT activation.

Task-induced fatigue and collisions in adult drivers with attention deficit hyperactivity disorder

OBJECTIVE

This study compares collision involvement between adult drivers with attention deficit hyperactivity disorder (ADHD) and control participants in a simulation experiment designed to enhance the effects of fatigue. Because the effects of ADHD include difficulties in maintaining attention, drivers with ADHD were hypothesized to be more susceptible to the effects of fatigue while driving.

METHODS

Data are drawn from a validated driving simulation study, portions of which were focused on enhancing the effects of fatigue. The simulator data are supplemented with written questionnaire data. Drivers with ADHD were compared with controls.

RESULTS

The self-report data indicated that drivers with ADHD were more likely to report having been involved in an accident within the previous five years. Simulation data showed that time of day of participation in the experiment were significantly related to likelihood of collision, and that these effects were further exacerbated by ADHD status. Participants with ADHD were more likely than controls to be involved in a crash in the simulator regardless of time of day, but the effects were particularly pronounced in the morning, and the rate of increase in accident involvement from the late afternoon into the evening was greater among participants with ADHD. No differences in self-reported sleep patterns or caffeine use were found between participants with ADHD and controls.

CONCLUSIONS

The results suggest that drivers with ADHD became fatigued more quickly than controls. Such drivers thus face greater risk of involvement in accidents on highways or open roadways where the visual and task monotony of the environment contribute to greater driver fatigue.

Road characteristics and driver fatigue: a simulator study

Two experiments examined the influence of road characteristics on driver fatigue in a prolonged simulator drive. In experiment one, ten military truck drivers drove a mixed route, with straight, winding, and straight highway segments. In experiment two, 16 additional drivers drove either a straight, a winding, or a mixed route. Fatigue symptoms were assessed using performance, subjective, and psychophysiological measures (HRV). We hypothesized that drivers adopt different fatigue-coping strategies relative to the demands of the drive. Thus, on straight roads drivers are more likely to loosen their driving demands by either increasing their driving speed and/or not maintaining the lane position, as the road is tolerant to both strategies, whereas on winding roads, drivers are more likely to increase their speed but not their lane positioning. Our results confirm that decremental changes in driving performance varied among road types. In the straight road components, we found decrements in the quality of lane maintaining (experiment one) and steering quality (experiments one and two) and longitudinal speed (experiment two). In the winding road, we found that drivers increased their driving speed over time (experiments one and two).

Detecting eye movements in dynamic environments

To take advantage of the increasing number of in-vehicle devices, automobile drivers must divide their attention between primary (driving) and secondary (operating in-vehicle device) tasks. In dynamic environments such as driving, however, it is not easy to identify and quantify how a driver focuses on the various tasks he/she is simultaneously engaged in, including the distracting tasks. Measures derived from the driver's scan path have been used as correlates of driver attention. This article presents a methodology for analyzing eye positions, which are discrete samples of a subject's scan path, in order to categorize driver eye movements. Previous methods of analyzing eye positions recorded in a dynamic environment have relied completely on the manual identification of the focus of visual attention from a point of regard superimposed on a video of a recorded scene, failing to utilize information regarding movement structure in the raw recorded eye positions. Although effective, these methods are too time consuming to be easily used when the large data sets that would be required to identify subtle differences between drivers, under different road conditions, and with different levels of distraction are processed. The aim of the methods presented in this article are to extend the degree of automation in the processing of eye movement data by proposing a methodology for eye movement analysis that extends automated fixation identification to include smooth and saccadic movements. By identifying eye movements in the recorded eye positions, a method of reducing the analysis of scene video to a finite search space is presented. The implementation of a software tool for the eye movement analysis is described, including an example from an on-road test-driving sample.

Sleepiness and head movements

Sleepiness in working life is critical and strongly associated to work related accidents. The relationship between sleepiness and head movements is poorly investigated. The pattern of head movements over time was investigated in a laboratory study with ten subjects either sleep-deprived or rested. Head movements were obtained by an inclinometer placed on the subject's forehead, and the recording was continuous. Results show that subjects when sleep-deprived moved their head more and had more extreme head movements compared to when rested. An increase of the velocity and the number of extreme head movements over time were noted when the subjects were sleep-deprived and when rested. The increase of head movements was more linear over time in the rested condition, whereas in sleep-deprived conditions most of the increase appeared during the first hour. No significant differences of between forward-backward movements and left-right movements could be found. When rested, the changes in head movements correlated with ratings of sleepiness, EEG activity, and heart rate variability. Head movements can be a used as an indicator of sleepiness.

Stress, fatigue, health, and risk of road traffic accidents among professional drivers: the contribution of physical inactivity

Strategies to achieve ambitious targets for reducing road accidents ( 34 ) have largely focused on engineering and technological advancements, the modification of occupational demands, and, to a lesser extent, human factors. These factors include stress and psychological states; sleep, fatigue, and alertness; and health status. Physical activity appears to influence all these human factors but has not previously been systematically considered as a direct or indirect risk factor for driver accidents. This chapter provides an overview, within an evidence-based framework, of the impact each of these human factors has on driver performance and risk of at-work road traffic accidents and then examines how physical (in)activity may moderate and mediate these relationships. Finally, we consider practical implications for work site interventions. The review aims to offer an evidence base for the deployment of resources to promote physical activity, manage stress, facilitate sleep, reduce fatigue, and enhance alertness to improve physical and psychological health among professional drivers.

A controlled investigation into the psychological determinants of fatigue

Driver fatigue is associated with risks of road accidents that result in injury and death. Research has been limited by several issues such as confusion over definitions, how best to measure fatigue, and the contribution of psychological factors to fatigue. This study addressed these limitations by investigating the relationship between psychological factors and fatigue. Participants were assessed and were required to perform a monotonous task till they tired. Results found few psychological factors to be related to physiological and performance decrement fatigue outcome measures. However, psychological factors were found to correlate consistently with self-reported fatigue. The results suggest that fatigue is associated with a predisposition to be anxious, depressive, less self-assured, more conscientious (rule bound), less socially bold, less adaptable and low vigour. The results indicate that future research should employ a range of fatigue outcome measures in order to best understand what factors contribute to fatigue.

Effects of oxygen concentrations on driver fatigue during simulated driving

Driver fatigue has been the cause of traffic accidents. Despite this, the amount of time that drivers spend within cars has been increasing due to complex city life, traffic congestion, and particular occupational requirements. Consequently, fatigue and stress cannot be avoided. In present study, in order to find out the possibility for reducing fatigue while driving due to the supply of oxygen, driver fatigue resulting from the passage of time when different oxygen concentrations are supplied has been examined through subjective evaluations and reaction times using driving simulator for 10 male subjects. The results revealed the subjective fatigue feeling was highest in the low rate (18%) oxygen condition, while in the high rate (30%), it decreased to a certain extent. The feeling of sleepiness also showed the tendency to decrease somewhat in the case of the driving time having passed over 1h in the high-rate conditions. Also, the reaction time for braking after being instructed to suddenly stop following more than 2h of driving was reduced in the high-rate oxygen conditions compared to the low-rate oxygen condition. From the above results, it was shown that while driving a car, if the oxygen rate is lowered, fatigue is felt severely, and that in the case of supplying a high-rate of oxygen, the feeling of fatigue is lowered to some extent and the reaction time is shortened. It was suggested that the driver's fatigue can be reduced according to the supply of oxygen.

Reducing the effects of driving fatigue with magnitopuncture stimulation

The purpose of this study was to assess the effects of reducing driving fatigue with magnitopuncture stimuli on Dazhui (DU14) point and Neiguan (PC6) points using heart rate (HR), reaction time (RT) testing, right rate (RR), critical flicker fusion frequency (CFF) and subjective evaluation. Forty healthy subjects were randomly divided into two groups: study and control groups. All subjects were required to be well rested before the experiment. The subjects were engaged in high speed driving at a constant vehicle velocity of 80 km/h continuously for 3h on a test course simulating an expressway. During the driving magnitopunctures (Haci Five Elements Needle, 250 mT, made by Haci Company limited) were applied to the Dazhui (DU14) point and Neiguan (PC6) points for the study group when the subject performed the task for 2.5h, and for the control group magnitopunctures were applied to non-acupuncture points during the same time session. The results of this study show a significant effect of magnitopuncture stimuli on RT, RR and CFF. Subjective evaluation also exhibited significant differences (P < 0.05) between the two groups after the driving task. The findings showed that magnitopuncture stimuli on DU14 point and PC6 points could reduce the effects of driving fatigue.

Monotony of road environment and driver fatigue: a simulator study

Studies have shown that drowsiness and hypovigilance frequently occur during highway driving and that they may have serious implications in terms of accident causation. This paper focuses on the task induced factors that are involved in the development of these phenomena. A driving simulator study was conducted in order to evaluate the impact of the monotony of roadside visual stimulation using a steering wheel movement (SWM) analysis procedure. Fifty-six male subjects each drove during two different 40-min periods. In one case, roadside visual stimuli were essentially repetitive and monotonous, while in the other one, the environment contained disparate visual elements aiming to disrupt monotony without changing road geometry. Subject's driving performance was compared across these conditions in order to determine whether disruptions of monotony can have a positive effect and help alleviate driver fatigue. Results reveal an early time-on-task effect on driving performance for both driving periods and more frequent large SWM when driving in the more monotonous road environment, which implies greater fatigue and vigilance decrements. Implications in terms of environmental countermeasures for driver fatigue are discussed.

Tiredness! a natural explanation to The Grand Rapid "DIP"

The present analysis deals with the presumed improved driving skill of car drivers with low blood alcohol concentrations (BACs) compared to the driving skill of sober drivers. Several roadside surveys indicate such a controversial possibility, which is illustrated graphically in "The Grand Rapid Study" (1964) as a relative accident risk curve for driving while intoxicated (DWI). A curve which shows a "DIP" below a baseline accident risk of one for sober drivers in the interval between BACs of 0.01 and 0.04 g/l. Since then, various attempts have been made to show this "DIP" in the otherwise "exponentially" raising curve to be an artificial distortion based on disproportionate demographic subgroups. It is, however, our thesis that the observations of "The Grand Rapid Study" and other roadside surveys are valid, because the presumed mono-causal traffic accident curve hides "tiredness" as an additional human risk factor. This makes the first part of the night-time relative accident risk curve for impaired drivers artificially augmented by "tired" drivers with insignificant amounts of alcohol in the blood and therefore incorrectly accused of accidents due to DWI. The accident risk curve for higher BACs raises similarly abrupt due to drivers impaired by a combined effect of alcohol and "tiredness". Moreover, to imply an accident risk of one for all sober drivers independent of the time of the day is debatable as an increased accident risk is present in the late night-time hours due to "tired" drivers. This increase in "tired" sober accident drivers suppresses the accident risk curve for DWI with BACs of 0.01-0.04 g/l below the fixed baseline of one for the background population.

A critical review of the psychophysiology of driver fatigue

Driver fatigue is a major cause of road accidents and has implications for road safety. This review discusses the concepts of fatigue and provides a summary on psychophysiological associations with driver fatigue. A variety of psychophysiological parameters have been used in previous research as indicators of fatigue, with electroencephalography perhaps being the most promising. Most research found changes in theta and delta activity to be strongly linked to transition to fatigue. Therefore, monitoring electroencephalography during driver fatigue may be a promising variable for use in fatigue countermeasure devices. The review also identified anxiety and mood states as factors that may possibly affect driver fatigue. Furthermore, personality and temperament may also influence fatigue. Given the above, understanding the psychology of fatigue may lead to better fatigue management. The findings from this review are discussed in the light of directions for future studies and for the development of fatigue countermeasures.