Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Detecting fatigue in car drivers and aircraft pilots by using eye-motion metrics

Fatigue is widely recognised for risking the safety of aviation and ground transportation. To enhance transport safety, fatigue detection systems based on psychophysiological measures have been under development for many years. However, a reliable and robust fatigue detection system is still missing. This thesis starts with a literature review of fatigue concepts in the transportation field and the current psychophysiological measures to fatigue, and narrows down the focus to improving fatigue detection systems using eye-motion measures. A research gap was identified between current fatigue systems only focusing on part of sleepy symptoms and a comprehensive fatigue detection system including mental fatigue needed. To address this gap, four studies were conducted to reshape the understanding of fatigue in transportation and explore effective eye-motion metrics for indicating fatigue considering different causal factors. Studies 1 and 2 investigated the influence of two types of task-related fatigue on eye movement. Twenty participants completed a vigilance task before and after a 1-h simulator-based drive with a secondary task. Forty participants, divided equally into two groups, finished the same task before and after a 1-h and 1.5-h monotonous driving task. The results demonstrated that two types of task-related fatigue caused by cognitive overload and prolonged underload induced different physiological responses to eye-motion metrics. The results also proved that the increased mental fatigue decreased driver’s vigilance. Studies 3 and 4 simulated two hazardous fatigue scenarios for pilots. Study 3 explored the relationship between eye-motion metrics and pilot fatigue in an underload flight condition with sleep deprivation (low workload and sleep pressure). Study 4 explored the effective eye-motion metrics to estimate pilot’s cognitive fatigue imposed by time on task and high workload. The results suggested different eye-motion metrics to indicate sleepiness and mental fatigue. In addition, based on the sleepiness and mental fatigue indicators in Studies 3 and 4, several classifiers were built and evaluated to accurately detect sleepiness and mental fatigue. These findings show that considering casual factors such as sleep pressure, time on task and workload when using eye-motion metrics to detect fatigue can improve the accuracy and face validity of the current fatigue detection systems

Lapses in Responsiveness: Characteristics and Detection from the EEG

Performance lapses in occupations where public safety is paramount can have disastrous consequences, resulting in accidents with multiple fatalities. Drowsy individuals performing an active task, like driving, often cycle rapidly between periods of wake and sleep, as exhibited by cyclical variation in both EEG power spectra and task performance measures. The aim of this project was to identify reliable physiological cues indicative of lapses, related to behavioural microsleep episodes, from the EEG, which could in turn be used to develop a real-time lapse detection (or better still, prediction) system. Additionally, the project also sought to achieve an increased understanding of the characteristics of lapses in responsiveness in normal subjects. A study was conducted to determine EEG and/or EOG cues (if any) that expert raters use to detect lapses that occur during a psychomotor vigilance task (PVT), with the subsequent goal of using these cues to design an automated system. A previously-collected dataset comprising physiological and performance data of 10 air traffic controllers (ATCs) was used. Analysis showed that the experts were unable to detect the vast majority of lapses based on EEG and EOG cues. This suggested that, unlike automated sleep staging, an automated lapse detection system needed to identify features not generally visible in the EEG. Limitations in the ATC dataset led to a study where more comprehensive physiological and performance data were collected from normal subjects. Fifteen non-sleep-deprived male volunteers aged 18-36 years were recruited. All performed a 1-D continuous pursuit visuomotor tracking task for 1 hour during each of two sessions that occurred between 1 and 7 weeks apart. A video camera was used to record head and facial expressions of the subject. EEG was recorded from electrodes at 16 scalp locations according to the 10-20 system at 256 Hz. Vertical and horizontal EOG was also recorded. All experimental sessions were held between 12:30 and 17:00 hours. Subjects were asked to refrain from consuming stimulants or depressants, for 4 h prior to each session. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 ± 12.9 lapses per hour (mean ± SE) and a lapse duration of 3.4 ± 0.5 s. The study also showed that lapsing and tracking error increased during the first 30 or so min of a 1-h session, then decreased during the remaining time, despite the absence of external temporal cues. EEG spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. Thus, complete lapses in responsiveness are a frequent phenomenon in normal subjects - even when not sleep-deprived - undertaking an extended, monotonous, continuous visuomotor task. This is the first study to investigate and report on the characteristics of complete lapses of responsiveness during a continuous tracking task in non-sleep-deprived subjects. The extent to which non-sleep-deprived subjects experience complete lapses in responsiveness during normal working hours was unexpected. Such findings will be of major concern to individuals and companies in various transport sectors. Models based on EEG power spectral features, such as power in the traditional bands and ratios between bands, were developed to detect the change of brain state during behavioural microsleeps. Several other techniques including spectral coherence and asymmetry, fractal dimension, approximate entropy, and Lempel-Ziv (LZ) complexity were also used to form detection models. Following the removal of eye blink artifacts from the EEG, the signal was transformed into z-scores relative to the baseline of the signal. An epoch length of 2 s and an overlap of 1 s (50%) between successive epochs were used for all signal processing algorithms. Principal component analysis was used to reduce redundancy in the features extracted from the 16 EEG derivations. Linear discriminant analysis was used to form individual classification models capable of detecting lapses using data from each subject. The overall detection model was formed by combining the outputs of the individual models using stacked generalization with constrained least-squares fitting used to determine the optimal meta-learner weights of the stacked system. The performance of the lapse detector was measured both in terms of its ability to detect lapse state (in 1-s epochs) and lapse events. Best performance in lapse state detection was achieved using the detector based on spectral power (SP) features (mean correlation of φ = 0.39 ± 0.06). Lapse event detection performance using SP features was moderate at best (sensitivity = 73.5%, selectivity = 25.5%). LZ complexity feature-based detector showed the highest performance (φ = 0.28 ± 0.06) out of the 3 non-linear feature-based detectors. The SP+LZ feature-based model had no improvement in performance over the detector based on SP alone, suggesting that LZ features contributed no additional information. Alpha power contributed the most to the overall SP-based detection model. Analysis showed that the lapse detection model was detecting phasic, rather than tonic, changes in the level of drowsiness. The performance of these EEG-based lapse detection systems is modest. Further research is needed to develop more sensitive methods to extract cues from the EEG leading to devices capable of detecting and/or predicting lapses

Effects of circadian rhythm phase alteration on physiological and psychological variables: Implications to pilot performance (including a partially annotated bibliography)

The effects of environmental synchronizers upon circadian rhythmic stability in man and the deleterious alterations in performance and which result from changes in this stability are points of interest in a review of selected literature published between 1972 and 1980. A total of 2,084 references relevant to pilot performance and circadian phase alteration are cited and arranged in the following categories: (1) human performance, with focus on the effects of sleep loss or disturbance and fatigue; (2) phase shift in which ground based light/dark alteration and transmeridian flight studies are discussed; (3) shiftwork; (4)internal desynchronization which includes the effect of evironmental factors on rhythmic stability, and of rhythm disturbances on sleep and psychopathology; (5) chronotherapy, the application of methods to ameliorate desynchronization symptomatology; and (6) biorythm theory, in which the birthdate based biorythm method for predicting aircraft accident susceptability is critically analyzed. Annotations are provided for most citations

An eeg based study of unintentional sleep onset

Ph.DDOCTOR OF PHILOSOPH

The effects of time of day and circadian rhythm on performance during variable levels of cognitive workload

The present study examined the effects of time of day of testing on a simulated aviation task. The tasks required the participants to engage in multitasking while electroencephalogram (EEG) data was collected to objectively measure participants’ workload. Task demands were altered throughout the testing period to expose participants to both high and low workload conditions. Additionally, individual differences in circadian rhythm were explored by assessing participants’ circadian typology. No significant differences in performance were found resulting from time of day differences. However, performance and EEG differences were found based on phase of testing and workload manipulations. Subjective workload measures were influenced by time of day, with a moderating effect of circadian typology. Implications are discussed

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 133)

This special bibliography lists 276 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in September 1974

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 158, September 1976

This bibliography lists 191 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1976

The Effects Of Modulating Accommodative-Vergence Stress Within The Context Of Operator Performance On Automated System Tasks

Automated systems (e.g., self-driving cars, autopilot) can reduce an operator’s (i.e., driver, pilot, baggage screener) task engagement, which can result in mind wandering, distraction, and loss of concentration. Consequently, unfavorable performance outcomes, such as missed critical signals and slow responses to emergency events, can occur. Because automation reverts the operator to a “visual monitoring” role, the oculomotor accommodative-vergence responses (the oculomotor responses that maintain a single focused image on the retina) may play a vital role in human-automation interactions. Prior research has shown that individuals with deficits in the accommodative-vergence responses can exhibit inattentive symptoms (e.g., poor concentration) characteristic of attention-deficit/hyperactivity disorder (ADHD) while performing prolonged close work (e.g., reading). Given the behavioral symptoms present in those experiencing accommodative-vergence stress, automated systems may exacerbate these negative effects. The current study examined the effects of accommodative-vergence stress in combination with automation on aspects of operator task engagement. Participants (N = 95) under accommodative-vergence stress wearing -2.0 diopter lenses or normal viewing conditions completed a 40 min flight simulation task either with or without automation. Physiological dependent measures included electroencephalographic (EEG) parietal-occipital alpha power spectral density (PSD), an EEG multivariate metric of engagement, and pupil diameter. Self-report measures of task engagement, cognitive fatigue, and visual fatigue symptoms were also collected along with oculomotor measurements (accommodation and convergence) and flight simulation task performance. Multivariate analyses indicated that the application of -2.0 diopter lenses did not significantly alter oculomotor measurements or subjective reports of visual fatigue. Oculomotor stress modestly affected task performance and tended to result in increased EEG measures of engagement, while subsequently increasing feelings of fatigue, potentially indicating a compensatory effort response. Participants performing the simulation with automation exhibited significantly lower task engagement, as indicated by greater parietal-occipital alpha PSD, less multivariate EEG engagement, smaller pupil diameter, and lower self-reported engagement. Overall, oculomotor stress and automation did not interact synergistically to affect task engagement and associated performance outcomes. Automation and time on task were the main determinants of task engagement. These results underscore the negative effects automation can have on underlying operator cognitive states and the associated need to carefully design automation to combat reduced task engagement. Applications for system design and the use of EEG in augmented cognition systems involving automation are discussed