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Enhancing pilot vigilance assessment: The role of flight data and continuous performance test in detecting random attention loss in short IFR flights
... It is defi ned as a state of mental and physical exhaustion that diminishes an individual's ability to perform eff ectively. Fatigue has been shown to impair cognitive and psychomotor functions essential for safe piloting, including attention [17,25], reaction time [40,41], situational awareness [19,43], and decision-making [9,39]. The eff ects of fatigue are particularly pronounced during long-haul and nocturnal fl ights, where the lack of natural light and extended hours spent in the cockpit exacerbate the cognitive decline associated with prolonged wakefulness. ...
Introduction: In the context of military aviation, aircrew members are required to perform a range of complex and cognitively demanding tasks under a variety of conditions, including irregular work schedules, insufficient rest periods, and disrupted circadian rhythms. This study investigates the impact of sleep deprivation on flight performance in a controlled simulator environment and examines whether the pharmacological agents modafinil and galantamine can restore flight performance to baseline levels following 27 hours of wakefulness. Methods: A group of 12 male volunteers, with a mean age of 24 ± 2.5 years, was tested in three separate sessions during which the participants were randomly assigned to receive either 100 mg of modafinil, 10 mg of galantamine, or a placebo. During the continuous wakefulness period, the participants completed three tests in a flight simulator involving a simple flight control task performed under varied conditions (flight over land and sea). Results: Galantamine showed significant differences across flight conditions, with improved performance in maintaining altitude under the land and sea conditions (p<0.001). Under the same flight conditions, galantamine had a significant effect on speed, resulting in slower speeds compared to placebo. Additionally, it demonstrated a significant improvement in maintaining a stable heading under sea conditions. Across all parameters, the stimulants did not restore flight accuracy to baseline levels under control conditions. Conclusions: The effects of both modafinil and galantamine on sleep deprivation-induced fatigue and flight performance were minimal, with results comparable to those of the placebo in most scenarios. Neither agent was able to restore baseline performance after a single dose administered following 27 hours of wakefulness. However, due to several limitations of the study, further research is warranted, with a focus on physiological assessments to strengthen the evidence base for anti-fatigue guidelines.
Conducting flight operations at the pace of air traffic relies on shift work, overtime work, work at night, work in different and numerous time zones, and unbalanced flight crew schedules. Such working hours and workload settings can cause disturbances of the circadian rhythm and sleep disorders among flight crew members; this can result in fatigue and can have an impact on the safety of flight operations. Fatigue impacts many cognitive abilities such as vigilance, memory, spatial orientation, learning, problem solving, and decision making. In aviation, fatigue has been identified as a hazard to the safety of flight operations. This paper describes objectivation methods for data collecting processes regarding flight crew fatigue, using an electronic system of standardized chronometric cognitive tests and subjective self-assessment surveys on the subjective perception of fatigue. The data collected were analyzed using statistical methods to identify and quantify elements that affect the appearance of fatigue. Finally, causal modeling methods were used to determine correlations among the measured flight crew fatigue indicators, the subjective perception of fatigue, and the defined workload settings. The results of this research reveal which elements strongly impact flight crew fatigue. The detected correlations can help define improved measures for the mitigation of fatigue risk in future flight operations.
Investigative interviews (e.g., interrogations) are a critical component of criminal, military, and civil investigations. However, how levels of alertness (vs. sleepiness) of the interviewer impact outcomes of actual interviews is unknown. To this end, the current study tracked daily fluctuations in alertness among professional criminal investigators to predict their daily experiences with actual field interviews. Fifty law-enforcement investigators wore a sleep-activity tracker for two weeks while keeping a daily-diary of investigative interviews conducted in the field. For each interview, the investigators indicated how well they established rapport with the subject, how much resistance they encountered, how well they maintained their own focus and composure, and the overall utility of intelligence obtained. Daily alertness was biomathematically modeled from actigraphic sleep duration and continuity estimates and used to predict interview characteristics. Investigators consistently reported more difficulties maintaining their focus and composure as well as encountering more subject resistance during interviews on days with lower alertness. Better interview outcomes were also reported on days with subjectively better sleep, while findings were generally robust to inclusion of covariates. The findings implicate adequate sleep as a modifiable fitness factor for collectors of human intelligence.
In aviation, any detail can have massive consequences. Among the potential sources of failure, human error is still the most troublesome to handle. Therefore, research concerning the management of mental workload, attention, and stress is of special interest in aviation. Recognizing conditions in which a pilot is over-challenged or cannot act lucidly could avoid serious outcomes. Furthermore, knowing in depth a pilot’s neurophysiological and cognitive–behavioral responses could allow for the optimization of equipment and procedures to minimize risk and increase safety. In addition, it could translate into a general enhancement of both the physical and mental well-being of pilots, producing a healthier and more ergonomic work environment. This review brings together literature on the study of stress and workload in the specific case of pilots of both civil and military aircraft. The most common approaches for studying these phenomena in the avionic context are explored in this review, with a focus on objective methodologies (e.g., the collection and analysis of neurophysiological signals). This review aims to identify the pros, cons, and applicability of the various approaches, to enable the design of an optimal protocol for a comprehensive study of these issues.
This study aimed to investigate the eye movement characteristics and visual fatigue of virtual reality games with different interaction modes. Eye movement data were recorded using the built-in eye tracker of the VR device and eye movement parameters were calculated from the recorded raw data. The Visual Fatigue Scales and Simulator Sickness Questionnaire were used to subjectively assess visual fatigue and overall discomfort of the VR experience. Sixteen male and 17 female students were recruited for this study. Results showed that both the primary and 360 mode of VR could cause visual fatigue after 30 min of gameplay, with significant differences observed in eye movement behavior between the two modes. The primary mode was more likely to cause visual fatigue, as shown by objective measurements of blinking and pupil diameter. Fixation and saccade parameters also showed significant differences between the two modes, possibly due to the different interaction modes employed in the 360 mode. Further research is required to examine the effects of different content and interactive modes of VR on visual fatigue, as well as to develop more objective measures for assessing it.
Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.
Background
The vigilance fluctuation and decrement of sustained attention have large detrimental consequences to most tasks in daily life, especially among the elderly. Non-invasive brain stimulations (e.g., transcranial direct current stimulation, tDCS) have been widely applied to improve sustained attention, however, with mixed results.
Objective
An infraslow frequency oscillatory tDCS approach was designed to improve sustained attention.
Methods
The infraslow frequency oscillatory tDCS (O-tDCS) over the left dorsolateral prefrontal cortex at 0.05 Hz was designed and compared with conventional tDCS (C-tDCS) to test whether this new protocol improves sustained attention more effectively. The sustained attention was evaluated by reaction time and accuracy.
Results
Compared with the C-tDCS and sham, the O-tDCS significantly enhanced sustained attention by increasing response accuracy, reducing response time, and its variability. These effects were predicted by the evoked oscillation of response time at the stimulation frequency.
Conclusion
Similar to previous studies, the modulation effect of C-tDCS on sustained attention is weak and unstable. In contrast, the O-tDCS effectively and systematically enhances sustained attention by optimizing vigilance fluctuation. The modulation effect of O-tDCS is probably driven by neural oscillations at the infraslow frequency range.
Flight crews are frequently required to work irregular schedules and, as a result, can experience sleep deficiency and fatigue. This study was conducted to determine whether perceived fatigue levels and objective performance varied by time of day, time awake, and prior night's sleep duration. Ninety‐five pilots (86 male, 9 female) aged 33 years (±8) volunteered for the study. Participants completed a daily sleep diary, Samn‐Perelli fatigue scale, and psychomotor vigilance task that were completed before and after each flight duty period and at the top‐of‐descent for each flight. Pilots experienced higher self‐reported fatigue (EMM = 3.92, SE = 0.09, p < 0.001) and worse performance (Response speed: EMM = 4.27, SE = 0.08, p = 0.004) for late‐finishing duties compared with early‐starting duties (Samn‐Perelli: EMM = 3.74, SE = 0.08; Response speed: EMM = 4.37, SE = 0.08), but had shorter sleep before early‐starting duties (early: EMM = 6.94, SE = 0.10; late: EMM = 8.47, SE = 0.14, p < 0.001). However, pre‐duty Samn‐Perelli and response speed were worse (z = 4.18, p < 0.001; z = 3.05, p = 0.03; respectively) for early starts compared with late finishes (EMM = 2.74, SE = 0.19), while post‐duty Samn‐Perelli was worse for late finishes (EMM = 4.74, SE = 0.19) compared with early starts (EMM = 4.05, SE = 0.12). The results confirm that duty time has a strong influence on self‐reported fatigue and performance. Thus, all flights that encroach on a biological night are targets for fatigue risk management oversight.
Objective
Fatigue Risk Management Systems (FRMS) are a data-driven set of management practices for identifying and managing fatigue-related safety risks. This approach also considers sleep and work time, and is based on ongoing risk assessment and monitoring. This narrative review addresses the effectiveness of FRMS, as well as barriers and enablers in the implementation of FRMS. Furthermore, this review draws on the literature to provide evidence-based policy guidance regarding FRMS implementation.
Methods
Seven databases were drawn on to identify relevant peer-reviewed literature. Relevant grey literature was also reviewed based on the authors’ experience in the area. In total, 2129 records were screened based on the search strategy, with 231 included in the final review.
Results
Few studies provide an evidence-base for the effectiveness of FRMS as a whole. However, FRMS components (e.g., bio-mathematical models, self-report measures, performance monitoring) have improved key safety and fatigue metrics. This suggests FRMS as a whole are likely to have positive safety outcomes. Key enablers of successful implementation of FRMS include organisational and worker commitment, workplace culture, and training.
Conclusions
While FRMS are likely to be effective, in organisations where safety cultures are insufficiently mature and resources are less available, these systems may be challenging to implement successfully. We propose regulatory bodies consider a hybrid model of FRMS, where organisations could choose to align with tight hours of work (compliance) controls. Alternatively, where organisational flexibility is desired, a risk-based approach to fatigue management could be implemented.
Fatigue poses an important safety risk to civil and military aviation. In addition to decreasing performance in-flight (chronic) fatigue has negative long-term health effects. Possible causes of fatigue include sleep loss, extended time awake, circadian phase irregularities and work load. Despite regulations limiting flight time and enabling optimal rostering, fatigue cannot be prevented completely. Especially in military operations, where limits may be extended due to operational necessities, it is impossible to rely solely on regulations to prevent fatigue. Fatigue management, consisting of preventive strategies and operational countermeasures, such as pre-flight naps and pharmaceuticals that either promote adequate sleep (hypnotics or chronobiotics) or enhance performance (stimulants), may be required to mitigate fatigue in challenging (military) aviation operations. This review describes the pathophysiology, epidemiology and effects of fatigue and its impact on aviation, as well as several aspects of fatigue management and recommendations for future research in this field.
The Continuous Matching Task (CMT) is a novel paradigm designed to measure sustained attention and alertness. It is a special type of Continuous Performance Task (CPT) that utilizes truly continuous stimulus material. Stimuli are generated in real-time by a procedural algorithm which also enables adaptive testing. The task is highly flexible and can be used in either single or dual-task configurations that also allow for task mixing. The functionality of the algorithm and applications are presented. The viability of the CMT is tested and results are compared with similar tasks, i.e. Stroop-Task and Conner’s CPT (CCPT), as well as self-reports of ADHD in adults in a Multi-Trait-Multi-Method approach in a sample of N = 122 participants. Self-reports and measurements of heart rate variability during testing are analyzed to infer and compare mental workload during tasks. Overall, variants of the CMT induce a higher mental workload than the other tasks, and employing the dual-task CMT with adaptive difficulty resulted in the highest reliability and validity. Results indicate that the CMT is primarily a measure of alertness and processing speed and benefits from adaptive testing.
In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, i.e., Mathematical Models, Rule-Based Implementation, ML and Deep Learning. This study presents, compares and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.
We investigated whether standardized neuropsychological tests and experimental cognitive paradigms measure the same cognitive faculties. Specifically, do neuropsychological tests commonly used to assess attention measure the same construct as attention paradigms used in cognitive psychology and neuroscience? We built on the “general attention factor”, comprising several widely used experimental paradigms (Huang et al., 2012). Participants (n = 636) completed an on-line battery (TestMyBrain.org) of six experimental tests [Multiple Object Tracking, Flanker Interference, Visual Working Memory, Approximate Number Sense, Spatial Configuration Visual Search, and Gradual Onset Continuous Performance Task (Grad CPT)] and eight neuropsychological tests [Trail Making Test versions A & B (TMT-A, TMT-B), Digit Symbol Coding, Forward and Backward Digit Span, Letter Cancellation, Spatial Span, and Arithmetic]. Exploratory factor analysis in a subset of 357 participants identified a five-factor structure: (1) attentional capacity (Multiple Object Tracking, Visual Working Memory, Digit Symbol Coding, Spatial Span), (2) search (Visual Search, TMT-A, TMT-B, Letter Cancellation); (3) Digit Span; (4) Arithmetic; and (5) Sustained Attention (GradCPT). Confirmatory analysis in 279 held-out participants showed that this model fit better than competing models. A hierarchical model where a general cognitive factor was imposed above the five specific factors fit as well as the model without the general factor. We conclude that Digit Span and Arithmetic tests should not be classified as attention tests. Digit Symbol Coding and Spatial Span tap attentional capacity, while TMT-A, TMT-B, and Letter Cancellation tap search (or attention-shifting) ability. These five tests can be classified as attention tests.
This paper discusses the utilization of pilots' physiological indications such as electroencephalographic (EEG) signals, ocular parameters, and pilot performance-based quantitative metrics to estimate cognitive workload. The study aims to derive a non-invasive technique to estimate pilot's cognitive workload and study their correlation with standard physiological parameters. Initially, we conducted a set of user trials using well-established psychometric tests for evaluating the effectiveness of pupil and gaze-based ocular metrics for estimating cognitive workload at different levels of task difficulty and lighting conditions. Later, we conducted user trials with the NALSim flight simulator using a business class Learjet aircraft model. We analyzed participants' ocular parameters, power levels of different EEG frequency bands, and flight parameters for estimating variations in cognitive workload. Results indicate that introduction of secondary task increases pilot's cognitive workload significantly. The beta frequency band of EEG, nearest neighborhood index specifying distribution of gaze fixation, L1 Norm of power spectral density of pupil diameter, and the duty cycle metric indicated variations in cognitive workload.
Fatigue has been a long-standing concern in modern aviation. The duty hours of those who operate (cabin crew and pilots) have increased significantly. In order to combat the effects of fatigue, operators must adhere to Flight Time Limitations (FTLs) strictly set by regulatory bodies. With advances in the science of fatigue, the European Aviation Safety Agency (EASA) in February 2016 altered the duty limits and rest periods. A quantitative self-report survey design using descriptive statistics, chi-square tests of association and probit regressions with marginal effect calculations gathered the crew perceptions about the impact of the FTL regulation change to fatigue levels, reporting and safety/just culture. Participants (n=794) were commercial cabin crew and pilots operating under European Aviation Safety Agency (EASA) regulations. 73.2% said they have not reported fatigue to their airline and 81.7% having operated fatigued. Scales on Fatigue Knowledge and Just/Safety Culture were constructed. Each point increase on the 6-item Just/Safety Culture scale shows that respondents are 8.6 percentage points less likely to operate fatigued. This study signifies that fatigue is under-reported and is a real risk to safety in the aviation industry. A substantial change to the existing safety culture should be encouraged and the fatigue’s safety implications should not be underestimated and safety be compromised for maximum aircraft and labour utilisation.
Fatigue driving has become one of the most common causes for traffic accidents. In this article, we proposed an effective fatigue detection method based on eye status with pupil and iris segmentation. The segmented feature map can guide the detection to focus on pupil and iris. A streamlined network, consisting of a segmentation network and a decision network, is designed, which greatly improves the accuracy and generalization of eye openness estimation. Specifically, the segmentation network that uses light U-Net structure performs a pixel-level classification on the eye images, which can accurately extract pupil and iris features from the video's images. Then, the extracted feature map is used to guide the decision network to estimate eye openness. Finally, the detection method is test by the National Tsing Hua University Drowsy Driver Detection (NTHU-DDD) Video Dataset and the precision of fatigue detection achieves 96.72%. Experimental results demonstrate that the proposed method can accurately detect the driver fatigue in-time and possesses superior accuracy over the state-of-the-art techniques.
Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learning-based model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6–12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.
The aim of this study was to determine the relationship between pilot workload, performance, subjective fatigue, sleep duration, number of sectors and flight duration during short-haul operations. Ninety pilots completed a NASA Task Load Index, Psychomotor Vigilance Task and a Samn-Perelli fatigue scale on top-of-descent of each flight and wore an activity monitor throughout the study. Weak, but significant, correlations were revealed between workload and all factors. Subjective fatigue, number of sectors and lapses were significant predictors of workload. Pilots reported higher workload when fatigue increased, the number of sectors were higher, and objective performance was worse.
The abilities to stabilize the focus of attention, notice attention lapses, and return attention to an intended object following lapses are precursors for mindfulness. Individuals diagnosed with attention-deficit hyperactivity disorder (ADHD) are deficient in the attentional and self-control skills that characterize mindfulness. The present study assessed the relationship between mindfulness and ADHD in young adults using the Mindful Attention and Awareness Scale (MAAS), a computerized Go/No-Go task (the Test of Variables of Attention (TOVA)), the World Health Organization Adult Self-Report Scale (ASRS), a tool used as an adult ADHD screen, the Beck Anxiety Inventory (BAI), and the Beck Depression Inventory-II (BDI-II). We recruited 151 adult volunteers (ages 18 to 40); 100 with confirmed ADHD diagnoses and 51 control participants. Overall, participants with prior diagnoses of ADHD scored lower on the MAAS than controls and ASRS scores were strongly negatively correlated MAAS scores. Attention performance index, response time, and response-time variability subscales of the TOVA were positively correlated with MAAS scores and negatively correlated with ASRS scores. Intrasubject response-time variability on the TOVA, a parameter associated with attention lapses, was also strongly negatively correlated with MAAS scores. Overall, participants’ self-reported mindfulness, as measured by the MAAS, was strongly related to self-reports on a clinical measure of attention disorders, anxiety, depression, and multiple indices of concentration and mind wandering on a standardized Go/No-Go task, the TOVA.
Rationale:
Continuous performance tests (CPTs) are widely used to assess attentional processes in a variety of disorders including Alzheimer's disease and schizophrenia. Common human CPTs require discrimination of sequentially presented, visually patterned 'target' and 'non-target' stimuli at a single location.
Objectives:
The aims of this study were to evaluate the performance of three popular mouse strains on a novel rodent touchscreen test (rCPT) designed to be analogous to common human CPT variants and to investigate the effects of donepezil, a cholinesterase inhibitor and putative cognitive enhancer.
Methods:
C57BL/6J, DBA/2J and CD1 mice (n = 15-16/strain) were trained to baseline performance using four rCPT training stages. Then, probe tests assessed the effects of parameter changes on task performance: stimulus size, duration, contrast, probability, inter-trial interval or inclusion of flanker distractors. rCPT performance was also evaluated following acute administration of donepezil (0-3 mg/kg, i.p.).
Results:
C57BL/6J and DBA/2J mice showed similar acquisition rates and final baseline performance following rCPT training. On probe tests, rCPT performance of both strains was sensitive to alteration of visual and/or attentional demands (stimulus size, duration, contrast, rate, flanker distraction). Relative to C57BL/6J, DBA/2J mice exhibited (1) decreasing sensitivity (d') across the 45-min session, (2) reduced performance on probes where the appearance of stimuli or adjacent areas were changed (size, contrast, flanking distractors) and (3) larger dose- and stimulus duration-dependent changes in performance following donepezil administration. In contrast, CD1 mice failed to acquire rCPT (stage 3) and pairwise visual discrimination tasks.
Conclusions:
rCPT is a potentially useful translational tool for assessing attention in mice and for detecting the effects of nootropic drugs.
This study examined the effects of 35 h of continuous sleep deprivation on performance in a variety of cognitive tasks as well as simulated flight. Ten United States Air Force pilots completed the Multi-Attribute Task Battery (MATB), Psychomotor Vigilance Task (PVT), and Operation Span Task (OSPAN), as well as simulated flight at 3 h intervals over a 35 h sleep deprivation period. Performance declined on all tests after about 18–20 h of continuous sleep deprivation, although the degree to which performance degraded varied. During the second half of the sleep deprivation period, performance on the simulated flight was predicted by PVT and OSPAN reasonably well but much less so by the MATB. Variance from optimal flight performance was predicted by both PVT and OSPAN but each measure added incremental validity to the prediction. The two measures together accounted for 58% of the variance in flight performance in the second half of the sleep deprivation period.
Two models of recently reported high workload associated with vigilance tasks are the direct-cost and indirect-cost views. The former attributes high workload to the need for continuous observation in discriminating signals from neutral events; the latter attributes it to efforts to combat the boredom associated with monotonous vigilance tasks. These opposing views were tested by providing observers with reliable cueing, which rendered observation necessary only when low-probability critical signals were imminent, or with knowledge of results (KR) regarding performance efficiency. On the basis of cue and KR differences in elicited observation activity and motivational value, the direct-cost model led to the anticipation that cueing would result in a high-boredom, low-workload profile and a greater reduction in workload than KR. The indirect-cost model led to the prediction that cueing would result in a high-boredom, high-workload profile and a lesser reduction in workload than KR. The results clearly supported the direct-cost view that the workload of vigilance is task-induced. Consequently, efforts to combat high workload in complex automated systems requiring substantial monitoring by operators should focus specifically upon task-related determinants.
This study evaluated oculometrics as a detector of fatigue in Air Force-relevant tasks after sleep deprivation. Using the metrics of total eye closure duration (PERCLOS) and approximate entropy (ApEn), the relation between these eye metrics and fatigue-induced performance decrements was investigated.
One damaging effect to the successful outcome of operational military missions is that attributed to sleep deprivation-induced fatigue. Consequently, there is interest in the development of reliable monitoring devices that can assess when an operator is overly fatigued.
Ten civilian participants volunteered to serve in this study. Each was trained on three performance tasks: target identification, unmanned aerial vehicle landing, and the psychomotor vigilance task (PVT). Experimental testing began after 14 hr awake and continued every 2 hr until 28 hr of sleep deprivation was reached.
Performance on the PVT and target identification tasks declined significantly as the level of sleep deprivation increased.These performance declines were paralleled more closely by changes in the ApEn compared to the PERCLOS measure.
The results provide evidence that the ApEn eye metric can be used to detect fatigue in relevant military aviation tasks.
Military and commercial operators could benefit from an alertness monitoring device.
Pilot fatigue is a significant problem in modern aviation operations, largely because of the unpredictable work hours, long duty periods, circadian disruptions, and insufficient sleep that are commonplace in both civilian and military flight operations. The full impact of fatigue is often underappreciated, but many of its deleterious effects have long been known. Compared to people who are well-rested, people who are sleep deprived think and move more slowly, make more mistakes, and have memory difficulties. These negative effects may and do lead to aviation errors and accidents. In the 1930s, flight time limitations, suggested layover durations, and aircrew sleep recommendations were developed in an attempt to mitigate aircrew fatigue. Unfortunately, there have been few changes to aircrew scheduling provisions and flight time limitations since the time they were first introduced, despite evidence that updates are needed. Although the scientific understanding of fatigue, sleep, shift work, and circadian physiology has advanced significantly over the past several decades, current regulations and industry practices have in large part failed to adequately incorporate the new knowledge. Thus, the problem of pilot fatigue has steadily increased along with fatigue-related concerns over air safety. Accident statistics, reports from pilots themselves, and operational flight studies all show that fatigue is a growing concern within aviation operations. This position paper reviews the relevant scientific literature, summarizes applicable U.S. civilian and military flight regulations, evaluates various in-flight and pre-/postflight fatigue countermeasures, and describes emerging technologies for detecting and countering fatigue. Following the discussion of each major issue, position statements address ways to deal with fatigue in specific contexts with the goal of using current scientific knowledge to update policy and provide tools and techniques for improving air safety.
Studies suggest some physiologic, cognitive, and behavioral 24h rhythms are generated by cyclic components that are shorter in period than circadian. The aim of this study was (1) to examine the hypothesis that 24h human performance rhythms arise from the integration of high-frequency endogenous components and (2) to quantify the contribution of each higher frequency component to the phenotype of the rhythm. We monitored the performance of 9 experienced pilots by employing an array of cognitive-based tests conducted in a flight simulator so that, over the 6-day experiment, data were obtained for each 2h interval of the 24h. The activity-rest schedule of the subjects, no matter the exact clock time schedule of sleep and activity, always consisted of 14h activity (when they carried out regular professional duties) and 10h rest, with at least 8h of sleep. The simulated combat scenarios consisted of simple and complex tasks associated with target interception, aircraft maneuvering, and target shooting and downing. The results yielded two indices: the number of prominent periodicities in the time series and the relative magnitude of the amplitude of each relative to the construction of the composite 24h waveform. Three cyclic components (8h, 12h, and 24h) composed the observed 24h performance pattern. The dominant period and acrophase (peak time) of the compound output rhythm were determined by the interplay between the amplitudes of the various individual ultradian components. Task complexity (workload) increases the expression of the ultradian entities in the 24h pattern. We constructed a model composed of the multiple ultradian components; the composite output defined a "time span" (of 2h-4h duration) as opposed to an exact "time point" of high and low performance, endowing elevated functional capability.
We analyzed the characteristics of sustained attention changes in flight crews during exempt and non-exempt flights. Fourteen pilots (aged 30-43 y) participated in this study, with seven involved in each flight type, all of which were intercontinental (China to North America). Pilots completed continuous performance tests (CPT) at the required flight stages without compromising safety while on duty. No significant differences in sleep and sustained attention emerged between the exempt and non-exempt flight crews. Pilots' fatigue was highest in the early morning hours. Their general stability of efficiency increased during the day and decreased at night. Non-exempt flight crews appeared to sacrifice reaction rate to improve accuracy. Exempt crews appeared to increase their test proficiency. The task stability time of the non-exempt flight crews was better than that of the exempt ones. Short-term stability was better for exempt inbound flights rather than for outbound ones. Pilots were more prone to error runs as their total time awake increased, especially on non-exempt flights. The addition of crew members to exempt flights, allowance for more in-flight rest shifts, and over-stop rest on non-exempt flights may alleviate pilot fatigue and preserve alertness.
Objective:
To assess the objectivity of measuring the level of sleepiness in the subjects using a monotonous psychomotor bimanual tapping test developed by us, performed on mobile devices running Android OS.
Material and methods:
Four hundred and ninety-four hour-long experiments with the performance of a psychomotor test were conducted on 102 students. Using the method of mixed linear models, correlations between the levels of sleepiness according to the Karolinska Sleepiness Scale (KSS) and the Epworth Sleepiness Scale (ESS) and the behavioral indicators of the test were evaluated.
Results:
Statistically significant correlations between the increase in KSS scores and such indicators as a decrease in the total number of button taps and an increase in the frequency of «microsleep» episodes are shown. Statistically significant correlations of ESS score characteristics with the behavioral indicators of the test were not found.
Conclusion:
A large statistical material shows a reliable correlation of the parameters of the psychomotor test with the level of sleepiness on the Karolinska scale, which allows using the mobile application developed by us to determine the current level of sleepiness /alertness in the field.
Objective:
We aimed to quantify the clinical utility of continuous performance tests (CPTs) for the diagnosis of attention-deficit/hyperactivity disorder (ADHD) compared to a clinical diagnosis in children and adolescents.
Method:
Four databases (MEDLINE, PsycINFO, EMBASE, and PubMed) were screened until January 2023. Risk of bias of included results was judged with the QUADAS-2. We statistically pooled the area under the curve, the sensitivity, and the specificity of three commonly used CPTs subscales: omission/inattention, commission/impulsivity, and total number of errors/ADHD subscales (PROSPERO registration: CRD42020168091).
Results:
19 studies using commercially available CPTs were identified. Results from up to 835 control individuals and 819 cases were combined in the summary receiver operating characteristic (ROC) curve analyses (sensitivity and specificity pooling), and up to 996 cases and 1083 control individuals in the area under the curve (AUC) analyses. Clinical utility as measured by AUCs could be considered as barely acceptable (between 0.7 and 0.8) for the most part, with the best results for the total/ADHD score, followed by omissions/inattention, and poorest for commission/impulsivity scores. A similar pattern was found when pooling sensitivity and specificity: 0.75 (95% CI=0.66 to 0.82) and 0.71 (0.62 to 0.78) for the total/ADHD score; 0.63 (=0.49 to 0.75) and 0.74 (0. 65 to .81) for omissions; and 0.59 (0.38 to 0.77) and .66 (CI=.50, .78) for commissions.
Conclusion:
At the clinical level, CPTs as a stand-alone tool have only a modest to moderate ability to differentiate ADHD from non-ADHD samples. Hence, they should only be used within a more comprehensive diagnostic process.
In commercial aviation, sharing best practices of fatigue risk management (FRM) is important for the industry, its employees, and the community. Chronobiologists and sleep scientists have elucidated the impact of the biological clock and sleep/wake schedules on fatigue and captured their contributions in biomathematical models. The application of these models and other aspects of FRM requires expertise to which not all operators have access. We, therefore, describe some predictive and proactive approaches to FRM, including a collaborative process for evaluating and revising duty schedules to reduce fatigue risk and an innovative wake-up call program to better utilize planned napping opportunities.
Studies have shown that Methylphenidate (MPH) affects cognitive performance on the neuropsychological tests and clinical symptoms of individuals diagnosed with attention deficit/hyperactivity disorder (ADHD). This study investigated the acute effects of MPH on neuropsychological tests to explore the interaction between MPH and test-retest effects. Twenty youths with ADHD were tested before and after MPH intake in a double-blind placebo-controlled crossover design and compared to twenty matched controls. Participants were tested on a range of standardized tasks including sustained attention to response, N-Back, and Word/Color Stroop. Identical tasks were administered twice each testing day, before and 1 hour after MPH/Placebo administration. Healthy controls were tested similarly with no intervention. Decreases in response time (RT) variability across tasks and in commission errors were found in ADHD after MPH. Conversely, a significant increase in RT variability and increase in omission errors were observed after the placebo. In the control group, RT variability and omission errors increased whereas commission errors decreased, suggesting fatigue and practice effects, respectively. Test-retest reliability was higher in controls than ADHD. It is suggested that cognitive tests are sensitive objective measures for the assessment of responses to MPH in ADHD but are also affected by repetition and fatigue.
Objective: Prior research has identified a variety of embedded performance validity indicators on the Conners’ Continuous Performance Test-II (CPT-II). The purpose of this study was to examine embedded validity indicators within the updated third edition of the Conners Continuous Performance Test (CPT-3).
Method: This study used a retrospective chart review from an ADHD evaluation clinic at a Mid-Atlantic VA hospital. Participants were 197 military veterans who completed a clinical assessment for ADHD. All participants were consecutive referrals to the ADHD clinic who completed the CPT-3 and the Test of Memory Malingering, Trial 1 (TOMM1).
Results: Logistic regression analyses indicated that the following five variables were able to significantly predict validity status on the TOMM1: detectability (d’), omissions (OMI), commissions (COM), hit reaction time (HRT) standard deviation (SD), and HRT inter-stimulus interval (ISI) change. Among these measures, HRT SD and HRT ISI change were identified as the scores with the highest AUC values. Optimal cutoffs for all significant predictors were identified. A number of composite EVIs were created using various combinations of CPT-3 scores. All composite EVIs significantly differentiated between pass and fail status on the TOMM1.
Conclusions: Several CPT-3 variables have clinical utility as embedded validity indicators; however, due to low sensitivity, they should not be used in isolation. These scores may be used as indicators of invalid performance but should not be used to rule out invalid performance. Identified CPT-3 scores may be useful as one component in a multivariate, multi-point continuous approach to performance validity sampling.
This work presents a solution for fatigue recognition through a new deep learning model that has a characteristic input of the power spectrum of an electroencephalogram (EEG) signal. Firstly, four rhythms are obtained through the designed FIR filters, and the curve areas of their power spectrum density are coupled into four fatigue indicators. Secondly, a deep sparse contractive autoencoder network is proposed to learn more local fatigue characteristics, and the recognition results of pilots mental fatigue status are given. Compared with the state-of-the-art models, the results show that our model has good learning performance in extracting local features and fatigue status detection.
Pilots' operation has an important effect on flight safety and performance, particularly in the final landing stage when pilots need to deal with complicated operations. This study aims to determine the potential value of flight data and develop a method of evaluating a pilot's performance during landing phase based on flight quick access recorder (QAR) data from the perspective of risk assessment. First, a Landing Operation Performance Evaluation Model was developed based on risk evaluation principles. Three landing parameters, which are touchdown distance, touchdown vertical acceleration and touchdown pitch angle, were selected as indicators to evaluate the pilots' landing operation performance in this model. Second, the flight landing operation performance evaluation system (FLOPES) was set up based on the evaluation model. Test results showed that FLOPES can accomplish all calculation flow of operation performance evaluation. Finally, it concluded that this method is a more accurate and effective way for evaluating the landing operation performance of a flight. It could be as a practical tool for airlines to manage landing risk quantitatively and to provide a more practical support for improving training and design in aviation.Practitioner summary: This study aims to determine the potential value of flight data and to develop a method of evaluating pilot's landing operation performance from the risk evaluation perspective. Test results showed that this method is effective and could be as a practical tool for airlines to manage landing risk and improve training.
Abbreviations:
QAR: Quick Access Recorder; FLOPES: Flight Landing Operation Performance Evaluation System; ICAO: International Civil Aviation Organization; IATA: International Air Transport Association; SMS: Safety Management System; CAAC: Civil Aviation Administration of China; FOQA: Flight Operations Quality Assurance; VBA: Visual Basic for Applications.
Human factors have been defined by the International Civil Aviation Organization (ICAO) as “about people in their living and working situations; about their relationship with machines, with procedures and with the environment about them; and about their relationships with other people (at work)”. Human factors contribute to approximately 75% of aircraft accidents and incidents. As such, understanding their influence is essential to improve safety in the aviation industry. This study examined the different human factors causations in a random sample of over 200 commercial air transport accidents and incidents from 2000 to 2016. The main objective of this study was to identify the principal human factor contributions to aviation accidents and incidents. An exploratory research design was utilised. The qualitative data were recorded in a database, and were coded into categories about the flights (including date, manufacturer, carrier, state of occurrence, etc). These categories were then analysed using Chi-Squared tests to determine which were statistically significant in terms of having an influence on the accidents/incidents. The most significant human factor was found to be situational awareness followed by non-adherence to procedures. In addition, charter operations proved to have a significantly higher rate of human factor related occurrence as compared to other type of operations. A significant finding was that Africa has a high rate of accidents/incidents relative to the amount of traffic and aircraft movements. These findings reflect some of the more noteworthy incidents that have received significant media attention, including Air Asia 8501 on the 28th of December 2014, TransAsia Airways 235 on the 4th of February 2015, and Air France 447 on the 1st of June 2009; these accidents resulted in a significant loss of lives where situational awareness and non-adherence to procedures were significant contributing factors.
Airline pilots’ sleep and on-duty alertness are important focus areas in commercial aviation. Until now, studies pertaining to this topic have mainly focused on specific characteristics of flights and thus a comprehensive picture of the matter is not well established. In addition, research knowledge of what airline pilots actually do to maintain their alertness while being on duty is scarce.
The psychomotor vigilance test (PVT) is widely used to measure reduced alertness due to sleep loss. Here, two newly developed, 3-min versions of the psychomotor vigilance test, one smartphone-based and the other tablet-based, were validated against a conventional 10-min laptop-based PVT. Sixteen healthy participants (ages 22–40; seven males, nine females) completed a laboratory study, which included a practice and a baseline day, a 38-h total sleep deprivation (TSD) period, and a recovery day, during which they performed the three different versions of the PVT every 3 h. For each version of the PVT, the number of lapses, mean response time (RT), and number of false starts showed statistically significant changes across the sleep deprivation and recovery days. The number of lapses on the laptop was significantly correlated with the numbers of lapses on the smartphone and tablet. The mean RTs were generally faster on the smartphone and tablet than on the laptop. All three versions of the PVT exhibited a time-on-task effect in RTs, modulated by time awake and time of day. False starts were relatively rare on all three PVTs. For the number of lapses, the effect sizes across 38 h of TSD were large for the laptop PVT and medium for the smartphone and tablet PVTs. These results indicate that the 3-min smartphone and tablet PVTs are valid instruments for measuring reduced alertness due to sleep deprivation and restored alertness following recovery sleep. The results also indicate that the loss of sensitivity on the 3-min PVTs may be mitigated by modifying the threshold defining lapses.
We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms.
The Continuous Performance Test (CPT) is a widely used computerized test to assess impulsivity. This article proposes the use of a CPT variant based on movement recognition to obtain more accurate measurements of impulsivity. In this pilot study, 22 volunteers participated in a CPT experiment responding to the stimuli by raising his or her dominant hand instead of pressing the space bar in a keyboard. Using this method, correlations of self-reported impulsivity with number of commission errors and average reaction time improved those obtained with standard CPT.
The Karolinska Sleepiness Scale and Samn-Perelli fatigue ratings, and psychomotor vigilance task performance are proposed as measures for monitoring commercial pilot fatigue. In laboratory studies, they are sensitive to sleep/wake history and circadian phase. The present analyses examined whether they reliably reflect sleep/wake history and circadian phase during transmeridian flight operations. Data were combined from four studies (237 pilots, 730 out-and-back flights between 13 city pairs, 1-3-day layovers). Sleep was monitored (wrist actigraphy, logbooks) before, during and after trips. On duty days, sleepiness, fatigue and mean response speed were measured pre-flight and at the top of the descent. Mixed-model analysis of variance examined associations between these measures and sleep/wake history, after controlling for operational factors. Circadian phase was approximated by local (domicile) time in the city where each trip began and ended. More sleep in the 24 h prior to duty was associated with lower pre-flight sleepiness and fatigue and faster response speed. Sleepiness and fatigue were greater before flights departing during the domicile night and early morning. At the top of the descent, pilots felt less sleepy and fatigued after more in-flight sleep and less time awake. Flights arriving in the early-mid-morning (domicile time) had greater sleepiness and fatigue and slower response speeds than flights arriving later. Subjective ratings showed expected associations with sleep/wake history and circadian phase. The response speed showed expected circadian variation but was not associated with sleep/wake history at the top of the descent. This may reflect moderate levels of fatigue at this time and/or atypically fast responses among pilots.
European regulations restrict the duration of the maximum daily flight duty period for pilots as a function of the duty start time and the number of scheduled flights. However, late duty end times that may include long times awake are not specifically regulated. In this study, fatigue levels in pilots finishing their duty late at night (00:00-01:59 hour) were analysed and compared with pilots starting their duty early (05:00-06:59 hour). Fatigue levels of 40 commercial short-haul pilots were studied during a total of 188 flight duty periods, of which 87 started early and 22 finished late. Pilots used a small handheld computer to maintain a duty and sleep log, and to indicate fatigue levels immediately after each flight. Sleep logs were checked with actigraphy. Pilots on late-finishing flight duty periods were more fatigued at the end of their duty than pilots on early-starting flight duty periods, despite the fact that preceding sleep duration was longer by 1.1 h. Linear mixed-model regression identified time awake as a preeminent factor predicting fatigue. Workload had a minor effect. Pilots on late-finishing flight duty periods were awake longer by an average of 5.5 h (6.6 versus 1.1 h) before commencing their duty than pilots who started early in the morning. Late-finishing flights were associated with long times awake at a time when the circadian system stops promoting alertness, and an increased, previously underestimated fatigue risk. Based on these findings, flight duty limitations should consider not only duty start time, but also the time of the final landing.
A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks ( p-value <;0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.
This study evaluated whether pilot fatigue was greater on ultra-long range (ULR) trips (flights >16 h on 10% of trips in a 90-day period) than on long range (LR) trips. The within-subjects design controlled for crew complement, pattern of in-flight breaks, flight direction and departure time. Thirty male Captains (mean age = 54.5 years) and 40 male First officers (mean age = 48.0 years) were monitored on commercial passenger flights (Boeing 777 aircraft). Sleep was monitored (actigraphy, duty/sleep diaries) from 3 days before the first study trip to 3 days after the second study trip. Karolinska Sleepiness Scale, Samn-Perelli fatigue ratings and a 5-min Psychomotor Vigilance Task were completed before, during and after every flight. Total sleep in the 24 h before outbound flights and before inbound flights after 2-day layovers was comparable for ULR and LR flights. All pilots slept on all flights. For each additional hour of flight time, they obtained an estimated additional 12.3 min of sleep. Estimated mean total sleep was longer on ULR flights (3 h 53 min) than LR flights (3 h 15 min; P(F) = 0.0004). Sleepiness ratings were lower and mean reaction speed was faster at the end of ULR flights. Findings suggest that additional in-flight sleep mitigated fatigue effectively on longer flights. Further research is needed to clarify the contributions to fatigue of in-flight sleep versus time awake at top of descent. The study design was limited to eastward outbound flights with two Captains and two First Officers. Caution must be exercised when extrapolating to different operations.
BORGHINI et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness NEUROSCI BIOBEHAV REV 21(1) XXX-XXX, 2012. This paper reviews published papers related to neurophysiological measurements (electroencephalography: EEG, electrooculography EOG; heart rate: HR) in pilots/drivers during their driving tasks. The aim is to summarise the main neurophysiological findings related to the measurements of pilot/driver's brain activity during drive performance and how particular aspects of this brain activity could be connected with the important concepts of "mental workload", "mental fatigue" or "situational awareness". Review of the literature suggest that exists a coherent sequence of changes for EEG, EOG and HR variables during the transition from normal drive, high mental workload and eventually mental fatigue and drowsiness. In particular, increased EEG power in theta band and a decrease in alpha band occurred in high mental workload. Successively, increased EEG power in theta as well as delta and alpha bands characterize the transition between mental workload and mental fatigue. Drowsiness is also characterized by increased blink rate and decreased HR values. The detection of such mental states is actually performed "off-line" with accuracy around 90% but not on-line. A discussion on the possible future applications of findings provided by these neurophysiological measurements in order to improve the safety of the vehicles will be also presented.
The continuous attention task (CAT) is a test designed to assess changes in attention due to a variety of factors, for example,
drugs. Subjects view a series of 3x3 patterns of squares on a monitor screen, each displayed for 100 msec at intervals of
1.5–2.5 sec, and respond whenever two successive patterns are identical. For such a measure to be validr factors other than
attention should be investigated, and shown not to be a factor in performance. Nineteen subjects took part in a study in which
information-processing rate and recall of CAT figures was measured. The results showed that a viewing time of 50–60 msec was
sufficient for 50% correct recognition of CAT figures, and that recognition with a masked presentation of 100 msec did not
differ significantly from an unmaske presentation of 100 msec. Mean recall of CAT figures 2 sec after a 100-msec exposure
was 98.2%. These results suggest that performance on the CAT is not limited by either information-processing speed or memory
capacity, but is a valid measure of the ability to sustain attention.
Driver fatigue is an important factor in a large number of accidents. There has been much work done in driver fatigue detection. This paper presents a comprehensive survey of research on driver fatigue detection and provides structural categories for the methods which have been proposed. The methods of fatigue detection mainly focused on measures of the driver's state, driver performance and the combination of the driver's state and performance. The measures of driver's state included PERCLOS, mouth shape and head position; the measures of driver performance included lane tracking and tracking of distance between vehicles. These approaches are presented and discussed in detail. Some typical driver monitoring systems are also introduced in this paper. Finally, summary and conclusions are presented
Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driver’s alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure.
The Continuous Performance Test has been used for the last 40 years to measure sustained attention or vigilance in many different populations. Different versions of the test have been developed, but little is known about how similar these tests are, and to what extent performance on different versions of these tests overlaps. In order to examine convergence of the different versions of the CPT, three different CPTs were administered in both the Auditory and Visual Sensory Modalities. Subjects were selected from consecutive admissions to adolescent acute care units at a private psychiatric hospital (n=100). Auditory test modalities uniformly elicited poorer performance than visual tests, while each set of task demands consistently elicited differences in performance. Despite the high test-retest reliability of the individual subtests, the average correlation between tests was r=.42, with the average correlation between visual tests at r=.48 and the average correlation between the auditory tests was r=.45. The correlations within task demands across sensory modalities ranged from a low of.37 to a high of.52. Controlling for IQ did not influence the correlations to a substantial degree. These data suggest different versions of the CPT are correlated with each other at a level consistent with construct validity, but that they do not constitute alternate forms of the same test.
A 14-min continuous performance test (CPT) requiring a high rate of responding was administered to a probability-weighted random sample of 816 9-17-year-old children drawn from a population of 17,117 children in an ongoing epidemiological and longitudinal study in Western North Carolina. Systematic main effects of improved performance with older age were found in this age range for all variables, including reaction time (RT), RT standard error, errors of omission, errors of commission, and signal detection parameters (d' and beta). Significant gender main effects included more impulsive errors, less variability, and faster RT by males, with no interactions between age and gender. There were no main effects of ethnicity or interactions of ethnicity with age and/or gender. Large main effects of interstimulus interval (ISI; 1, 2, or 4-s intervals) and time block were present for most CPT performance measures. The normative data from the CPT should provide a useful framework for interpreting similar data in future studies of child and adolescent psychopathology.
The Karolinska sleepiness scale (KSS) is frequently used for evaluating subjective sleepiness. The main aim of the present study was to investigate the validity and reliability of the KSS with electroencephalographic, behavioral and other subjective indicators of sleepiness.
Participants were 16 healthy females aged 33-43 (38.1+/-2.68) years. The experiment involved 8 measurement sessions per day for 3 consecutive days. Each session contained the psychomotor vigilance task (PVT), the Karolinska drowsiness test (KDT-EEG alpha & theta power), the alpha attenuation test (AAT-alpha power ratio open/closed eyes) and the KSS.
Median reaction time, number of lapses, alpha and theta power density and the alpha attenuation coefficients (AAC) showed highly significant increase with increasing KSS. The same variables were also significantly correlated with KSS, with a mean value for lapses (r=0.56).
The KSS was closely related to EEG and behavioral variables, indicating a high validity in measuring sleepiness.
KSS ratings may be a useful proxy for EEG or behavioral indicators of sleepiness.