Pilot attention allocation model based on fuzzy theory

AbstractQuantitative research into a pilot’s attention allocation mechanism is required in the optimization design of an aircraft human–machine interface and system evaluation. After making a comprehensive consideration of several factors, including the importance of information, information detective efficiency and human errors, a pilot attention allocation model was built on the basis of hybrid entropy. In order to make a verification of the pilot attention allocation model, a simulation model of a head-up display (HUD) used to present flight indicators was developed. After setting the membership degrees of the importance for different indicators according to their priorities, the experiments on the key-press response and eye-movement tracking were designed and carried out under the cruise and hold modes. As the experiment results are in good agreement with the theoretical model, the effectiveness of the pilot attention allocation model based on fuzzy theory is confirmed

EEG Feature Analysis Related to Situation Awareness Assessment and Discrimination

In order to discriminate situation awareness (SA) levels on the basis of SA-sensitive electroencephalography (EEG) features, the high-SA (HSA) group and low-SA (LSA) groups, which are representative of two SA levels, were classified according to the situation awareness global assessment technology (SAGAT) scores measured in the multi-attribute task battery (MATB) II tasks. Furthermore, three types of EEG features, namely, absolute power, relative power, and slow-wave/fast-wave (SW/FW), were explored using spectral analysis. In addition, repeated analysis of variance (ANOVA) was conducted in three brain regions (frontal, central, and parietal) × three brain lateralities (left, middle, and right) × two SA groups (LSA and HSA) to explore SA-sensitive EEG features. The statistical results indicate a significant difference between the two SA groups according to SAGAT scores; moreover, no significant difference was found for the absolute power of four waves (delta (δ), theta (θ), alpha (α), and beta (β)). In addition, the LSA group had a significantly lower β relative power than the HSA group in central and partial regions. Lastly, compared with the HSA group, the LSA group had higher θ/β and (θ + α)/(α + β) in all analyzed brain regions, higher α/β in the parietal region, and higher (θ + α)/β in all analyzed regions except for the left and right laterality in the frontal region. The above SA-sensitive EEG features were fed into principal component analysis (PCA) and the Bayes method to discriminate different SA groups, and the accuracies were 83.3% for the original validation and 70.8% for the cross-validation. The results provide a basis for real-time assessment and discrimination of SA by investigating EEG features, thus contributing to monitoring SA decrement that might lead to threats to flight safety

A model for discrimination and prediction of mental workload of aircraft cockpit display interface

AbstractWith respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals (SDNN) significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future

EEG Feature Analysis Related to Situation Awareness Assessment and Discrimination

In order to discriminate situation awareness (SA) levels on the basis of SA-sensitive electroencephalography (EEG) features, the high-SA (HSA) group and low-SA (LSA) groups, which are representative of two SA levels, were classified according to the situation awareness global assessment technology (SAGAT) scores measured in the multi-attribute task battery (MATB) II tasks. Furthermore, three types of EEG features, namely, absolute power, relative power, and slow-wave/fast-wave (SW/FW), were explored using spectral analysis. In addition, repeated analysis of variance (ANOVA) was conducted in three brain regions (frontal, central, and parietal) × three brain lateralities (left, middle, and right) × two SA groups (LSA and HSA) to explore SA-sensitive EEG features. The statistical results indicate a significant difference between the two SA groups according to SAGAT scores; moreover, no significant difference was found for the absolute power of four waves (delta (δ), theta (θ), alpha (α), and beta (β)). In addition, the LSA group had a significantly lower β relative power than the HSA group in central and partial regions. Lastly, compared with the HSA group, the LSA group had higher θ/β and (θ + α)/(α + β) in all analyzed brain regions, higher α/β in the parietal region, and higher (θ + α)/β in all analyzed regions except for the left and right laterality in the frontal region. The above SA-sensitive EEG features were fed into principal component analysis (PCA) and the Bayes method to discriminate different SA groups, and the accuracies were 83.3% for the original validation and 70.8% for the cross-validation. The results provide a basis for real-time assessment and discrimination of SA by investigating EEG features, thus contributing to monitoring SA decrement that might lead to threats to flight safety

Effects of Air Route Alternation and Display Design on an Operator’s Situation Awareness, Task Performance and Mental Workload in Simulated Flight Tasks

Air route alternation caused by unexpected events in abnormal or emergency situations often produces adverse consequences on an operator’s cognition and behavior in flight tasks. Under such a circumstance, it is especially necessary to examine the utility of the interaction displays usually designed based on the routine environment. This study was aimed to investigate the effects of air route alternation and display design on operators’ situation awareness (SA), task performance and mental workload during simulated flight tasks. Twenty-four participants attended an experiment where they were instructed to perform simulated flight tasks with three types of display designs in both air-route-as-planned and air-route-altered conditions. Subjective measures, behavioral measures and eye movement measures were adopted to assess the participants’ SA, task performance and mental workload. The results show that unexpected air route alternation increases mental workload as well as deteriorates the SA and task performance due to the gap between attention resource demand and supply. Reducing the demand of the operator’s attention resource should be the focus when coping with unexpected events in abnormal situations. In addition, reasonable information layout, such as a center-layout design of the critical decision-making information, is more important than information salience for improving the SA and task performance in abnormal situations. Nevertheless, indicators with a high-salience design, such as a more open window design and immersive design, are still worth recommending