A controlled investigation into the psychological determinants of fatigue

Research on drivers' color preference for alert zones in extra long tunnels and its effect on fatigue relief

PURPOSE

To address driver fatigue caused by the monotonous visual environment in long tunnels in mountainous areas, this study aims to investigate the effect of tunnel alert segment lighting (red, blue, yellow, and white) on alleviating driver physiological fatigue and enhancing alertness and provide theoretical and empirical evidence for the optimal design of tunnel lighting.

METHODS

A driving simulation test was conducted to simulate a long tunnel environment. Four background luminous colors (red, blue, yellow, and white) were tested. Drivers' physiological indicators (HRV) and Karolinska Sleepiness Scale (KSS) were recorded. The HRV indicators changes under each lighting condition were analyzed using the non-parametric Wilcoxon test to quantitatively assess the effects of the lighting colors on physiological fatigue reduction and alertness enhancement.

RESULTS

The driving simulation test revealed that blue background light significantly enhanced driver alertness, followed by red and yellow background lights, which showed the second most pronounced effects. In contrast, white background light did not significantly improve fatigue indicators. The highest levels of fatigue were observed in the non-alert zone of the tunnel, highlighting the necessity for background color interventions.

CONCLUSION

Blue background light proved to be the most effective in alleviating driver fatigue in the alert zones of long tunnels. Therefore, it is recommended that blue background light be prioritized in tunnel safety lighting design. The study provides scientific evidence for optimizing human factors in tunnel lighting environments. Future research should verify the long-term effectiveness of these interventions in real-world road conditions.

Origins and consequences of cognitive fatigue

Everybody knows intuitively what mental fatigue is. However, we poorly understand why fatigue emerges with time spent on demanding cognitive work and how such 'cognitive fatigue' impacts neural processing and behavioral guidance. Here, we review experimental investigations that induced cognitive fatigue and recorded its potential markers, including self-report, behavioral performance, economic choice, physiological and neural activity. We then review theoretical models of cognitive fatigue, classically divided into biological and motivational accounts. To explain key observations and reconcile debated theories, we finally propose a conceptual model (dubbed MetaMotiF), in which cognitive fatigue would emerge for biological reasons and yet affect motivational processes that regulate the behavior. More precisely, fatigue would arise from metabolic alterations in cognitive control brain regions, following their excessive mobilization. In turn, these metabolic alterations would increase the cost of cognitive control, which would shift decisions towards actions that require little effort and yield immediate rewards.

Mental fatigue causes significant activation of the prefrontal cortex: A systematic review and meta-analysis of fNIRS studies

Mental fatigue, a psychobiological prevalent and underestimated condition, is defined by increased lethargy and impaired concentration. This condition is not restricted by age and is exacerbated by various predisposing factors. Prolonged mental fatigue in occupational environments raises the probability of accidents or fatalities. Its fundamental mechanism is largely obscure and inherently subjective, thus there is no universally accepted parameter for its detection. Recently, there has been an increase in research that focuses on the use of functional near-infrared spectroscopy (fNIRS) to observe changes in brain hemoglobin during mental fatigue. Thus, this study assessed the reliability of oxygenhemoglobin and deoxyhemoglobin as fatigue biomarkers and conducted a systematic review and meta-analysis of studies which used fNIRS to monitor mental fatigue. The findings revealed significant activation of the prefrontal lobe under mental fatigue, and its activation level is intricately associated with the monitoring of diverse states during mental fatigue. Importantly, the type of induced mental fatigue and whether pre-trial training was provided to subjects were independent of the prefrontal lobe activation level. Overall, fNIRS proves to be an effective tool in tracking brain activity during mental fatigue, with a highly active prefrontal cortex acting as a dependable indicator for early identification of mental fatigue.

Methodology and Experimental Protocol for Fatigue Analysis in Suggestopedia Teachers

BACKGROUND

Among all professions, teaching is significantly affected by psycho-social risks with approximately 33.33% of educators reporting work-related fatigue. Suggestopedia, an effective pedagogical approach developed in Bulgaria, claims to induce positive psychological and cognitive benefits in both teachers and students. In order to gather scientific evidence, given the above statement, we designed a methodology to detect fatigue in Suggestopedia teachers based on neurocognitive analysis and psychological assessment.

METHODS

An increase in the EEG theta and alpha band powers is considered among the most reliable markers of fatigue. The proposed methodology introduces a robust framework for fatigue analysis. Initially, the changes in EEG band powers using the resting state EEG activity before and after teaching are measured. Subsequently, validated psychological questionnaires are used to gain subjective feedback on fatigue. The study participants include a control group (traditional teachers) and the test group (suggestopedia teachers) to assess whether suggestopedia practice mitigates fatigue among teachers.

OBSERVATIONS

In a pilot study, the EEG data was analyzed by evaluating the interrelations between EEG bands and the alpha-beta ratio. The results of the proposed study are expected to provide comprehensive analysis for the fatigue levels of teachers. In future research, our goal is to position the described methodology as a robust approach for evaluating cognitive and emotional states.

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features

Objective.While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings.Approach.In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states.Main results. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process.Significance.Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.

Road safety: The influence of vibration frequency on driver drowsiness, reaction time, and driving performance

Driver drowsiness is a factor in at least 20% of serious motor vehicle accidents. Although research has shown that Whole-Body Vibration (WBV) can induce drowsiness in drivers, it is unknown whether particular frequencies are more problematic. The present study systematically investigated the influence of WBV frequency on driver drowsiness. Fifteen participants each undertook six 1-h sessions of simulated driving while being subjected to WBV of either 0 Hz (no vibration), 1-4 Hz, 4-8 Hz, 8-16 Hz, 16-32 Hz or 32-64 Hz. Subjective sleepiness, as measured by the Karolinska Sleepiness Scale (KSS), confirmed that drivers felt drowsier when exposed to the two lowest frequency ranges (1-4 Hz and 4-8 Hz). Reaction time, which measures attention and alertness, was significantly impaired by the two lowest frequency ranges. Objective driving performance measures (Standard Deviation of Lane Position (SDLP), Standard Deviation of (SD) Steering Angle, Time in Unsafe Zone) also showed significant degradation due to exposure to the two lowest frequency ranges. Exposure to 1-4 Hz or 4-8 Hz vibration caused attention to become significantly impaired within 15-20 min and driving performance to be significantly impaired by 30-35 min. The other frequency ranges had little or no effect. These findings point to a need to develop equivalent vibration-induced drowsiness contours that can be adopted as transportation safety standards.

Analysis of Driving Fatigue Characteristics in Cold and Hypoxia Environment of High-Altitude Areas

The cold and hypoxic environment at high altitudes can easily lead to driving fatigue. For improving highway safety in high-altitude areas, a driver fatigue test is conducted using the Kangtai PM-60A car heart rate and oxygen tester to collect drivers' heart rate oximetry in National Highway 214 in Qinghai Province. Standard deviation (SDNN), mean (M), coefficient of RR (two R heart rate waves), RR interval coefficient of variation (RRVC), and cumulative rate of driving fatigue based on the driver's heart rate RR interval are calculated using SPSS. This study aims to derive degree of driving fatigue (DFD) in high-altitude areas when driving from lower to higher altitude. The analysis shows that the DFD growth trend of different altitude ranges presents an S-shaped curve. The driving fatigue thresholds in the altitude range of 3000-3500, 3500-4000, 4000-4500, and 4500-5000 m are 2.86, 3.82, 4.54, and 10.2, which are significantly higher than that of ordinary roads in plain areas. The start times of severe fatigue in the four altitude ranges are 35, 34, 32, and 25 minutes. The start time of driving fatigue continued to advance with the increase of age, and the DFD continued to increase with the increase of age. Results provide an empirical basis for the design of the horizontal alignment index system and antifatigue strategies to improve highway safety in high-altitude areas.

Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification

This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

Effects of Mental Fatigue on Strength Endurance: A Systematic Review and Meta-Analysis

The purpose of the present systematic review and meta-analysis was to explore the effects of mental fatigue on upper and lower body strength endurance. Searches for studies were performed in the PubMed/MEDLINE and Web of Science databases. We included studies that compared the effects of a demanding cognitive task (set to induce mental fatigue) with a control condition on strength endurance in dynamic resistance exercise (i.e., expressed as the number of performed repetitions at a given load). The data reported in the included studies were pooled in a random-effects meta-analysis of standardized mean differences. Seven studies were included in the review. We found that mental fatigue significantly reduced the number of performed repetitions for upper body exercises (standardized mean difference: -0.41; 95% confidence interval [-0.70, -0.12]; p = .006; I2 = 0%). Mental fatigue also significantly reduced the number of performed repetitions in the analysis for lower body exercises (standardized mean difference: -0.39; 95% confidence interval [-0.75, -0.04]; p = .03; I2 = 0%). Our results showed that performing a demanding cognitive task-which induces mental fatigue-impairs strength endurance performance. Collectively, our findings suggest that exposure to cognitive tasks that may induce mental fatigue should be minimized before strength endurance-based resistance exercise sessions.

Hand Motor Fatigability Induced by a Simple Isometric Task in Spinal Cord Injury

This study aimed: (1) to evaluate the hand motor fatigability in people with spinal cord injury (SCI) and compare it with measurements obtained form an able-bodied population; (2) to compare the hand motor fatigability in people with tetraplegia and in people with paraplegia; and (3) to analyse if motor fatigability is different in people with SCI with and without clinical significant perceived fatigability. Materials and Methods: 96 participants with SCI (40 cervical and 56 thoracolumbar) and 63 able-bodied controls performed a simple hand isometric task to assess motor fatigability. The Fatigue Severity Scale was used for perceived fatigability evaluation. Results: The main results of this study can be summarized as follows: (1) the waning in muscle force (motor fatigability) during a fatiguing task is similar in controls and participants with SCI; (2) the motor fatigability is influenced by the maximal muscle force (measured at the beginning of the task); and (3) the perceived fatigability and the motor fatigability are largely independent in the individuals with SCI. Conclusion: Our findings suggest that the capability to maintain a prolonged effort is preserved in SCI, and this capacity depends on the residual maximal muscle force in people with SCI.

Relationship Between Personality Measures and Perceived Mental Fatigability

OBJECTIVE

Examine the association between personality measures and perceived mental fatigability.

METHODS

We performed a cross-sectional analysis in N=1670 men, age 84.3±4.1 years. Multivariable linear regression models were used to examine the covariate adjusted association between personality measures (conscientiousness, optimism, goal reengagement, and goal disengagement) and perceived mental fatigability (measured with the validated 10-item Pittsburgh Fatigability Scale, PFS).

RESULTS

One standard deviation lower conscientiousness (β=-0.91, p<.0001) and optimism (β=-0.63, p<.0001), and higher goal reengagement (β=0.51, p=.01) scores were independently associated with higher PFS Mental scores adjusted for age, cognitive function, self-reported health status, depressive symptoms, sleep disturbance, physical activity, and goal disengagement.

DISCUSSION

Lower conscientiousness, optimism, and higher goal reengagement were linked with more severe perceived mental fatigability in older men. Personality traits may potentially contribute to early risk assessment for fatigability in later life. Future work should be longitudinal in nature and include personality assessments to confirm the temporality of the relationships observed.

Effects of whole-body vibration on driver drowsiness: A review

INTRODUCTION

Whole-body vibration has direct impacts on driver vigilance by increasing physical and cognitive stress on the driver, which leads to drowsiness, fatigue and road traffic accidents. Although sleep deprivation, sleep apnoea and alcohol consumption can also lead to driver drowsiness, exposure to steady vibration is the factor most readily controlled by changes to vehicle design, yet it has received comparatively less attention.

METHODS

This review investigated interrelationships between the various components of whole-body vibration and the physiological and cognitive parameters that lead to driver drowsiness, as well as the effects of vibration parameters (frequency, amplitude, waveform and duration). Vibrations transmitted to the driver body from the vehicle floor and/or seat have been considered for this review, whereas hand-arm vibration, shocks, acute or transient vibration were excluded from consideration.

RESULTS

Drowsiness is affected by interactions between the frequency, amplitude, waveform and duration of the vibration. Under optimal conditions, whole-body vibration can induce significant drowsiness within 30 min. Low frequency whole-body vibrations, particularly vibrations of 4-10 Hz, are most effective at inducing drowsiness. This review notes some limitations of current studies and suggests directions for future research.

CONCLUSIONS

This review demonstrated a strong causal link exists between whole-body vibration and driver drowsiness. Since driver drowsiness has been established to be a significant contributor to motor vehicle accidents, research is needed to identify ways to minimise the components of whole-body vibration that contribute to drowsiness, as well as devising more effective ways to counteract drowsiness.

PRACTICAL APPLICATIONS

By raising awareness of the vibrational factors that contribute to drowsiness, manufacturers will be prompted to design vehicles that reduce the influence of these factors.

Impact of Light Environment on Driver's Physiology and Psychology in Interior Zone of Long Tunnel

In tunnels, lighting not only affects visual performance, but also non-visual aspects such as drivers' physiological fatigue and mental stress. The non-visual impacts in the interior zone of long tunnels are particularly prominent as drivers are confined for a long time. To alleviate this problem, this study aims to investigate the relationship between drivers' physiological and psychological states and lighting environments. The physiological signal test system (MP150) breathing belt was used to record the changes of heart rate variability (HRV) of drivers when passing through the interior zone of a long tunnel under various lighting conditions. In particular, sympathetic indicators of physiological fatigues and the ratio of low frequency and high frequency (LF/HF) representing mental load were obtained. By analyzing the temporal variation in these two indicators, it is found that environmental luminance perception can more accurately reflect drivers' physiological and psychological states in the long tunnel than road luminance. An increase in road luminance or background luminance will result in a decrease in the mental stress, thereby reducing fatigue sense. Compared to simply increasing road luminance, mental stress of drivers decreased more obviously when the background luminance of long tunnel increased. Based on this, this paper proposed a method to regulate non-visual effect by adding contour markers without increasing light source intensity for the improvement in lighting performance, driving safety, and energy efficiency in long tunnels.

Central and Peripheral Fatigue in Physical Exercise Explained: A Narrative Review

The study of the origin and implications of fatigue in exercise has been widely investigated, but not completely understood given the complex multifactorial mechanisms involved. Then, it is essential to understand the fatigue mechanism to help trainers and physicians to prescribe an adequate training load. The present narrative review aims to analyze the multifactorial factors of fatigue in physical exercise. To reach this aim, a consensus and critical review were performed using both primary sources, such as scientific articles, and secondary ones, such as bibliographic indexes, web pages, and databases. The main search engines were PubMed, SciELO, and Google Scholar. Central and peripheral fatigue are two unison constructs part of the Integrative Governor theory, in which both psychological and physiological drives and requirements are underpinned by homeostatic principles. The relative activity of each one is regulated by dynamic negative feedback activity, as the fundamental general operational controller. Fatigue is conditioned by factors such as gender, affecting men and women differently. Sleep deprivation or psychological disturbances caused, for example, by stress, can affect neural activation patterns, realigning them and slowing down simple mental operations in the context of fatigue. Then, fatigue can have different origins not only related with physiological factors. Therefore, all these prisms must be considered for future approaches from sport and clinical perspectives.

Raw scores on subjective sleepiness, fatigue, and vigor metrics consistently define resilience and vulnerability to sleep loss

STUDY OBJECTIVES

Although trait-like individual differences in subjective responses to sleep restriction (SR) and total sleep deprivation (TSD) exist, reliable characterizations remain elusive. We comprehensively compared multiple methods for defining resilience and vulnerability by subjective metrics.

METHODS

41 adults participated in a 13-day experiment:2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue (POMS-F) and Vigor (POMS-V) were administered every 2h. Three approaches (Raw Score [average SR score], Change from Baseline [average SR minus average baseline score], and Variance [intraindividual SR score variance]), and six thresholds (±1 standard deviation, and the highest/lowest scoring 12.5%, 20%, 25%, 33%, 50%) categorized Resilient/Vulnerable groups. Kendall's tau-b correlations compared the group categorization's concordance within and between KSS, POMS-F, and POMS-V scores. Bias-corrected and accelerated bootstrapped t-tests compared group scores.

RESULTS

There were significant correlations between all approaches at all thresholds for POMS-F, between Raw Score and Change from Baseline approaches for KSS, and between Raw Score and Variance approaches for POMS-V. All Resilient groups defined by the Raw Score approach had significantly better scores throughout the study, notably including during baseline and recovery, whereas the two other approaches differed by measure, threshold, or day. Between-measure correlations varied in strength by measure, approach, or threshold.

CONCLUSION

Only the Raw Score approach consistently distinguished Resilient/Vulnerable groups at baseline, during sleep loss, and during recovery‒‒we recommend this approach as an effective method for subjective resilience/vulnerability categorization. All approaches created comparable categorizations for fatigue, some were comparable for sleepiness, and none were comparable for vigor. Fatigue and vigor captured resilience/vulnerability similarly to sleepiness but not each other.

Evidence on definitions, concepts, outcome instruments, and interventions for chronic fatigue in spinal cord injury: a scoping review protocol

OBJECTIVE

The objective of this review is to review the existing evidence on definitions, concepts, measurement instruments, and interventions for chronic fatigue in spinal cord injury.

INTRODUCTION

Chronic fatigue in people with spinal cord injury is an under-studied issue that affects between 25% and 56.6% of people with spinal cord injury. There are questions about how it is defined and managed due to its complex, multifactorial nature and relationship with related conditions. No overview of chronic fatigue in spinal cord injury exists and we are in need of a shared definition of chronic fatigue, as well as a comprehensive review of concepts and evidence supporting outcome instruments and interventions.

INCLUSION CRITERIA

This review will include empirical and non-empirical studies that focus on definitions, concepts, measurement instruments, and interventions for chronic fatigue in spinal cord injury. Studies that focus on peripheral muscle fatigue will only be included if they include chronic fatigue as a secondary outcome.

METHODS

This review will be done in three phases. Phase I will provide an overview of definitions of chronic fatigue in spinal cord injury and will include a qualitative analysis of concept attributes and characteristics. Phase II will focus on factors related to chronic fatigue and measurement instruments used to measure chronic fatigue, and phase III will focus on interventions. Full texts will be screened by two independent reviewers against inclusion criteria. Results will be presented in tabular form with a narrative summary.

Towards better microsleep predictions in fatigued drivers: exploring benefits of personality traits and IQ

Fatigued driving is one of the main contributors to road traffic accidents. Poor sleep quality and lack of sleep negatively affect driving performance, and extreme states of fatigue can cause microsleep (i.e., short episodes of sleep with complete loss of awareness). Driver monitoring systems analyse biosignals (e.g., gaze, blinking, heart rate) and vehicle data (e.g., steering wheel movements, lane holding, acceleration) to detect states of fatigue and prevent accidents. We argue that inter-individual differences in personality, sensation seeking behaviour, and intelligence could improve microsleep prediction, in addition to sleepiness. We tested 144 male participants in a supervised driving track after 27 hours of sleep deprivation. More than 74% of drivers experienced microsleep, after an average driving time of 52 min. Overall, prediction models for microsleep vulnerability and driving time before microsleep were significantly improved by conscientiousness, sensation seeking and non-verbal IQ, in addition to situational sleepiness, as individual risk factors. Practitioner summary: This study offers valuable insights for the design of driver monitoring systems. The use of individual risk factors such as conscientiousness, sensation seeking, and non-verbal IQ can increase microsleep prediction. These findings may improve monitoring systems based solely on physiological signals (e.g., blinking, heart rate) and vehicle data (e.g., steering wheel movements, acceleration, cornering). Abbreviations: ADAC: Allgemeiner Deutscher Automobil Club; ANOVA: analysis of variance; AIC: Akaike information criteria; CI: confidence interval; GPS: global positioning system; IQ: intelligence quotient; IQR: inter quartile range; KSS: Karolinska sleepiness scale; NEO-PI-R: revised NEO personality inventory; OLS: ordinary least squares; PSQI: Pittsburgh sleep quality index; SPM: standard progressive matrices; SSS: sensation seeking scale; WHO: World Health Organization.

Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males

In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue.

Detailed Multi-Dimensional Assessment of Fatigue in Inflammatory Bowel Disease

Background

Fatigue is a symptom commonly reported by patients with inflammatory bowel disease (IBD). Treating any underlying inflammation in active disease improves the health outcomes and decreases fatigue, but fatigue still persists in remission, negatively affecting patients' quality of life and posing a challenge for the treating physician. The aim of this study was to describe the prevalence of fatigue in patients with IBD and investigate possible contributing factors.

Methods

Recruited IBD patients from the Otago region in southern New Zealand were asked to complete demographic, physical activity (IPAQ) and fatigue questionnaires (Brief Fatigue Inventory, Multidimensional Fatigue Inventory). Disease activity and factors contributing to fatigue were assessed through self-reporting and laboratory biomarkers.

Results

One hundred and thirteen of the contacted 469 IBD patients participated in the study. Depending on the questionnaire used, the prevalence of fatigue in IBD was high in remission (39.5-44.2%) but significantly higher (p < 0.001) in active disease (80.0-82.9%). Several factors such as age, disease duration, level of physical activity, gender and diet were found to be associated with increased fatigue and were attributed to either mental or physical fatigue categories. Multifactorial Fatigue Inventory provided insights into different types of fatigue, and revealed a significant mental fatigue component in both active and remission disease patients. Iron deficiency was not associated with fatigue levels.

Conclusions

Fatigue in IBD is multi-faceted and highly prevalent in both active and remission IBD. Further investigations, addressing the complexity of the symptom and its reporting are needed.

The impact of indoor air temperature on the executive functions of human brain and the physiological responses of body

Background: This study aimed to investigate the effect size (ES) of air temperature on the executive functions of human brain and body physiological responses. Methods: In this empirical study, the participants included 35 male students who were exposed to 4 air temperature conditions of 18°C, 22°C, 26°C and 30°C in 4 separate sessions in an air conditioning chamber. The participants were simultaneously asked to take part in the N-back test. The accuracy, electrocardiogram (ECG) signals and the respiration rate were recorded to determine the effect of air temperature. Results: Compared to moderate air temperatures (22°C), high (30°C) and low (18°C) air temperatures had a much more profound effect on changes in heart beat rate, the accuracy of brain executive functions and the response time to stimuli. There were statistically significant differences in the accuracy by different workload levels and various air temperature conditions(P<0.05). Although the heart beat rate index, the ratio between low frequency and high frequency (LF/HF), and the respiratory rate were more profoundly affected by the higher and lower air temperatures than moderate air temperatures (P<0.05), this effect was not statistically significant, which may be due to significant reduction in the standard deviation of normal-to normal intervals (SNND) and the root of mean squared difference between adjacent normal heart beat (N-N) intervals (RMSSD) (P>0.05). Conclusion: The results confirmed that the unfavorable air temperatures may considerably affect the physiological responses and the cognitive functions among indoor employees.Therefore, providing them with thermal comfort may improve their performance within indoor environments.

An Adaptive SSVEP-Based Brain-Computer Interface to Compensate Fatigue-Induced Decline of Performance in Practical Application

Brain-computer interfaces based on steady-state visual evoked potentials are promising communication systems for people with speech and motor disabilities. However, reliable SSVEP response requires user's attention, which degrades over time due to significant eye-fatigue when low-frequency visual stimuli (5-15 Hz) are used. Previous studies have shown that eye-fatigue can be reduced using high-frequency flickering stimuli (>25 Hz). Here, it is quantitatively demonstrated that the performance of a high-frequency SSVEP BCI decreases over time, but this amount of decrease can be compensated effectively by using two proposed adaptive algorithms. This leaded to a robust alternative communication system for practical applications. The asynchronous spelling system implemented in this study uses a threshold-based version of LASSO algorithm for frequency recognition. In long online experiments, when participants typed a sentence with the BCI system for 16 times, accuracy of the system was close to its maximum along the experiment. However, regression analysis on typing speed of each sentence demonstrated a significant decrease in all 7 subjects ( ) when thresholds obtained from a calibration test were kept fixed over the experiment. In comparison, no significant change in typing speed was observed when the proposed adaptive algorithms were used. The analysis of variances revealed that the average typing speed of the last four sentences when using adaptive relational algorithm (8.7 char/min) was significantly higher than the tolerance-based algorithm (8.1 char/min) and significantly above 6 char/min when the fixed thresholds were used. Therefore, the relational algorithm proposed in this paper could successfully compensate for the effect of fatigue on performance of the SSVEP BCI system.

Daytime sleepiness and its relationships to fatigue and autonomic dysfunction in adults with spinal cord injury

OBJECTIVE

To determine the extent of daytime sleepiness in adults with spinal cord injury (SCI) and investigate the contribution of fatigue and autonomic function to sleepiness status.

METHODS

Participants included 45 adults with SCI attending outpatient services or living in the community and 44 able-bodied controls. The Oxford Sleep Resistance Test (OSLER) was used to assess daytime sleepiness, while eye blink rate duration (electrooculography) and the Iowa Fatigue Scale assessed fatigue. Heart rate variability (HRV) was used to assess autonomic function. Survival analysis (Kaplan Meier) was used to estimate the rate of loss in participation in the OSLER task, as a measure of daytime sleepiness. Repeated measures ANOVA was used to determine HRV differences between groups. Regression analysis was used to establish factors that contributed to daytime sleepiness.

RESULTS

Participants with high lesions ("T3 and above") had significantly increased daytime sleepiness. OSLER results revealed only 33% of those with high lesions remained awake during the task. Those with high lesions also had significantly reduced sympathetic activity while no differences in parasympathetic activity were found between groups. Lesion completeness had no effect. Standardized variation in heart rate, slow eye blinks, low frequency HRV and self-reported fatigue contributed to daytime sleepiness.

CONCLUSION

Neurological lesions at "T3 or above" have an increased risk of daytime sleepiness, impacting on independence in daily functional tasks and work performance. Autonomic imbalance alters cardiovascular control, affecting health and wellbeing. The interaction of these factors requires further investigation.

Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks

This paper proposes a comprehensive approach to explore whether functional brain network (FBN) changes from the alert state to the drowsy state and to find out ideal neurophysiology indicators able to detect driver drowsiness in terms of FBN. A driving simulation experiment consisting of two driving tasks is designed and conducted using fifteen participant drivers. Collected EEG signals are then decomposed into multiple frequency bands by wavelet packet transform (WPT). Based on this, two novel FBN approaches, synchronization likelihood (SL) and minimum spanning tree (MST) are combined and applied to feature recognition and classification system. Unlike other methods, our approaches focus on the interaction and correlation between different brain regions. Statistical analysis of network features indicates that the difference between alert state and drowsy state are significant and further confirmed that brain network configuration should be related to drowsiness. For classification, these brain network features are selected and then fed into four classifiers considered namely Support Vector Machines (SVM), K Nearest Neighbors classifier (KNN), Logistic Regression (LR) and Decision Trees (DT). It is found that combining MST method and SL method is actually increasing the classification accuracy with all classifiers considered in this work especially the KNN classifier from 95.4% to 98.6%. Moreover, KNN classifier also gives the highest precision of 98.3%, sensitivity of 98.8% and specificity of 98.9%. Thus this kind of methodology might be a useful tool for further understanding the neurophysiology mechanisms of driver drowsiness, and as a reference work for future studies or future 'systems'.

Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness

A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states. The filtered EEG signals are then decomposed into multiple frequency bands by wavelet packet transform. Functional connectivity between all pairs of channels for multiple frequency bands is assessed using the phase lag index (PLI). Based on this, PLI-weighted networks are subsequently calculated, from which minimum spanning trees are constructed-a graph method that corrects for comparison bias. Statistical analyses are performed on graph-derived metrics as well as on the PLI connectivity values. The major finding is that significant differences in the delta frequency band for three graph metrics and in the theta frequency band for five graph metrics suggesting network integration and communication between network nodes are increased from alertness to drowsiness. Together, our findings also suggest a more line-like configuration in alert states and a more star-like topology in drowsy states. Collectively, our findings point to a more proficient configuration in drowsy state for lower frequency bands. Graph metrics relate to the intrinsic organization of functional brain networks, and these graph metrics may provide additional insights on driver drowsiness detection for reducing and preventing traffic accidents and further understanding the neural mechanisms of driver drowsiness.

EEG-based driver fatigue detection using hybrid deep generic model

Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

The interconnection of mental fatigue and aging: An EEG study

Mental fatigue, a state of reduced alertness and decreased overall performance due to prolonged cognitive activity, is a major cause for a large number of accidents in traffic and industry. Against the background of an aging workforce, the investigation of the interconnection of mental fatigue and aging is of great practical relevance. In the present study, a group of younger and a group of older adults performed a cognitive task for 3h. The experimental design also comprised breaks with various durations. Beside behavioral data, the spectral properties of the ongoing EEG with respect to time on task and breaks were analyzed. No differences between the age groups were found in behavior, but electrophysiological measures provide some evidence that older adults in our study were differentially affected by time on task. In the later course of the experiment modulations in frontal theta power became larger for older, compared to younger adults. This may indicate strain due to task demands, eventually resulting from the deployment of compensatory processes. Occipital alpha, which has been linked to internally oriented brain states, saturates faster in younger adults. It thus maybe, that especially the younger participants' performance deteriorated due to the monotonous nature of the task itself. Both mechanisms, an increased consumption of cognitive resources in older adults and a decrease of motivation in younger adults, could mask differences in performance decrements between the age groups due to mental fatigue.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Time-frequency distribution properties of event-related potentials in mental fatigue induced by visual memory tasks

Prolonged periods of demanding cognitive tasks lead to an exhausted feeling known as mental fatigue. The neural underpinnings of mental fatigue are still under exploration. In the present study, we aimed to identify neurophysiological indicators of mental fatigue by studying the time-frequency distribution of the event-related potentials (ERPs) measured in N=26 adults in nonfatigued versus fatigued states. We were interested in the frontal theta and occipital alpha variations, which have shown consistent relationships with mental fatigue in previous studies. Furthermore, we expected differential changes in left and right electrodes, in line with previously detected lateralization effects in cognitive tasks. Mental fatigue was induced by a sustained two-back verbal visual memory task for 125 min and assessed using the Chalder Fatigue Scale. We applied a high-resolution time-frequency analysis method called smoothed pseudo Wigner Ville distribution and used regional integrals as indicators for changing trends of signal energy. Results showed an increase in ERP frontal theta energy (P=0.03) and a decrease in occipital alpha energy (P=0.028) when participants became mentally fatigued. The change in frontal theta was more pronounced in left electrode sites (P=0.032), hinting toward a differential fatigue effect in the two hemispheres. The results were discussed on the basis of previous lateralization studies with memory tasks and interpreted as an indicator of a causal relationship between the sustained task execution and the physiological changes. Our findings also suggest that the ERP signal energy variations in frontal theta and occipital alpha might be used as neural biomarkers to assess mental fatigue.

Single-channel EEG-based mental fatigue detection based on deep belief network

Mental fatigue has a pernicious influence on road and work place safety as well as a negative symptom of many acute and chronic illnesses, since the ability of concentrating, responding and judging quickly decreases during the fatigue or drowsiness stage. Electroencephalography (EEG) has been proven to be a robust physiological indicator of human cognitive state over the last few decades. But most existing EEG-based fatigue detection methods have poor performance in accuracy. This paper proposed a single-channel EEG-based mental fatigue detection method based on Deep Belief Network (DBN). The fused nonliear features from specified sub-bands and dynamic analysis, a total of 21 features are extracted as the input of the DBN to discriminate three classes of mental state including alert, slight fatigue and severe fatigue. Experimental results show the good performance of the proposed model comparing with those state-of-art methods.

Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system

A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required.

Influence of neurological lesion level on heart rate variability and fatigue in adults with spinal cord injury

STUDY DESIGN

Group cohort design.

OBJECTIVES

To determine the influence of spinal cord injury (SCI) and neurological level on heart rate variability (HRV) and associations with fatigue.

SETTING

SCI rehabilitation outpatient and community settings in New South Wales, Australia.

METHODS

Participants included 45 adults with SCI living in the community and 44 able-bodied controls. Socio-demographic, neurological injury, psychological, HRV and eye blink variables were assessed. Multivariate analysis of variance and post hoc protected t-tests were used to determine differences in HRV and fatigue as a function of the neurological level. Pearson's correlation analysis was used to determine the relationships between these factors.

RESULTS

Participants with SCI had significantly reduced sympathetic activity. Those with tetraplegia had lowered sympathetic activity compared with those with paraplegia and able-bodied controls. Neither were differences in parasympathetic activity found between groups nor were there any significant differences found for the time domain or non-linear domains. Higher levels of fatigue were found in the SCI sample, and participants with tetraplegia had higher fatigue levels compared with those with paraplegia. Fatigued participants were more likely to have altered autonomic function-that is, reduced sympathetic activity.

CONCLUSIONS

Higher levels of neurological impairment in people with SCI are more likely to result in disordered cardiovascular control involving reduced sympathetic activity, whereas elevated fatigue was found to be associated with increased sympathetic dysfunction. Findings highlight the need to address risks associated with this dysfunction, such as improved HRV and fatigue screening for people with SCI and improved education on cardiovascular risk factors.

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.

Using S-transform in EEG analysis for measuring an alert versus mental fatigue state

This paper presents research that investigated the effects of mental fatigue on brain activity using electroencephalogram (EEG) signals. Since EEG signals are considered to be non-stationary, time-frequency analysis has frequently been used for analysis. The S-transform is a time-frequency analysis method and is used in this paper to analyze EEG signals during alert and fatigue states during a driving simulator task. Repeated-measure MANOVA results show significant differences between alert and fatigue states within the alpha (8-13Hz) frequency band. The two sites demonstrating the greatest increases in alpha activity during fatigue were the Cz and P4 sites. The results show that S-transform analysis can be used to distinguish between alert and fatigue states in the EEG and also supports the use of the S-transform for EEG analysis.

Hypovigilance detection for UCAV operators based on a hidden Markov model

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators' dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed.

Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces

Background

The fatigue that users suffer when using steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can cause a number of serious problems such as signal quality degradation and system performance deterioration, users’ discomfort and even risk of photosensitive epileptic seizures, posing heavy restrictions on the applications of SSVEP-based BCIs. Towards alleviating the fatigue, a fundamental step is to measure and evaluate it but most existing works adopt self-reported questionnaire methods which are subjective, offline and memory dependent. This paper proposes an objective and real-time approach based on electroencephalography (EEG) spectral analysis to evaluate the fatigue in SSVEP-based BCIs.

Methods

How the EEG indices (amplitudes in δ, θ, α and β frequency bands), the selected ratio indices (θ/α and (θ + α)/β), and SSVEP properties (amplitude and signal-to-noise ratio (SNR)) changes with the increasing fatigue level are investigated through two elaborate SSVEP-based BCI experiments, one validates mainly the effectiveness and another considers more practical situations. Meanwhile, a self-reported fatigue questionnaire is used to provide a subjective reference. ANOVA is employed to test the significance of the difference between the alert state and the fatigue state for each index.

Results

Consistent results are obtained in two experiments: the significant increases in α and (θ + α)/β, as well as the decrease in θ/α are found associated with the increasing fatigue level, indicating that EEG spectral analysis can provide robust objective evaluation of the fatigue in SSVEP-based BCIs. Moreover, the results show that the amplitude and SNR of the elicited SSVEP are significantly affected by users’ fatigue.

Conclusions

The experiment results demonstrate the feasibility and effectiveness of the proposed method as an objective and real-time evaluation of the fatigue in SSVEP-based BCIs. This method would be helpful in understanding the fatigue problem and optimizing the system design to alleviate the fatigue in SSVEP-based BCIs.

Fatigue severity and factors associated with high fatigue levels in Korean patients with inflammatory bowel disease

Background/Aims

Many patients with inflammatory bowel disease (IBD) often complain of fatigue. To date, only a few studies in Western countries have focused on fatigue related to IBD, and fatigue has never been specifically studied in Asian IBD patients. The aim of the present study was to investigate the fatigue level and fatigue-related factors among Korean IBD patients.

Methods

Patients in remission or with mild to moderate IBD were included. Fatigue was assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue and the Brief Fatigue Inventory. Corresponding healthy controls (HCs) also completed both fatigue questionnaires.

Results

Sixty patients with Crohn disease and 68 patients with ulcerative colitis (UC) were eligible for analysis. The comparison group consisted of 92 HCs. Compared with the HCs, both IBD groups were associated with greater levels of fatigue (p<0.001). Factors influencing the fatigue score in UC patients included anemia and a high erythrocyte sedimentation rate (ESR).

Conclusions

Greater levels of fatigue were detected in Korean IBD patients compared with HCs. Anemia and ESR were determinants of fatigue in UC patients. Physicians need to be aware of fatigue as one of the important symptoms of IBD to better understand the impact of fatigue on health-related quality of life.

Modeling offenses among motorcyclists involved in crashes in Spain

In relative terms, Spanish motorcyclists are more likely to be involved in crashes than other drivers and this tendency is constantly increasing. The objective of this study is to identify the factors that are related to being an offender in motorcycle accidents. A binary logit model is used to differentiate between offender and non-offender motorcyclists. A motorcyclist was considered to be offender when s/he had committed at least one traffic offense at the moment previous to the crash. The analysis is based on the official accident database of the Spanish general directorate of traffic (DGT) for the 2003-2008 time period. A number of explanatory variables including motorcyclist characteristics and environmental factors have been evaluated. The results suggest that inexperienced, older females, not using helmets, absent-minded and non-fatigued riders are more likely to be offenders. Moreover, riding during the night, on weekends, for leisure purposes and along roads in perfect condition, mainly on curves, predict offenses among motorcyclists. The findings of this study are expected to be useful in developing traffic policy decisions in order to improve motorcyclist safety.

Developing a model of associations between chronic pain, depressive mood, chronic fatigue, and self-efficacy in people with spinal cord injury

Chronic pain, chronic fatigue, and depressive mood are prevalent conditions in people with spinal cord injury (SCI). The objective of this research was to investigate the relationship between these conditions in adults with SCI. Multivariate analysis of variance, contingency analyses, and hierarchical regression were used to determine the nature of the relationship, as well as the contribution to this relationship of self-efficacy, a potential mediator variable. Seventy participants with SCI living in the community completed an assessment regimen of demographic and psychometric measures, including validated measures of pain, fatigue, depressive mood, and self-efficacy. Results indicated that participants with high levels of chronic pain had clinically elevated depressive mood, confusion, fatigue, anxiety and anger, low vigor, and poor self-efficacy. Participants with high chronic pain had 8 times the odds of having depressive mood and 9 times the odds of having chronic fatigue. Regression analyses revealed that chronic pain contributed significantly to elevated depressive mood and that self-efficacy mediated (cushioned) the impact of chronic pain on mood. Furthermore, both chronic pain and depressive mood were shown to contribute independently to chronic fatigue. Implications of these results for managing chronic pain in adults with SCI are discussed.

PERSPECTIVE

The relationship between pain, negative mood, fatigue, and self-efficacy in adults with SCI was explored. Results support a model that proposes that chronic pain lowers mood, which is mediated (lessened) by self-efficacy, whereas pain and mood independently increase chronic fatigue. Results provide direction for treating chronic pain in SCI.