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

Abstract

With 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. ª 2014 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.

Improving pilot mental workload evaluation with combined measures

“…Scholars generally consider mental workload (MW) one of the most important factors influencing the allocation of pilot's attention resources. For example, in urgent situations, some pilots may forget to perform critical tasks or omit important information due to the 'attention narrowing phenomenon' under high MW conditions [2].Therefore, in the design stage of aircraft cockpit's human-machine interface, the accurate evaluation, quantitative classification and even prediction of the pilot's MW play an essential role in optimizing the mental task design of humanmachine interface and the allocation of human-machine functions, as well as have important practical significance in preventing aviation accident and ensuring aviation safety [3].…”

Mental workload prediction based on attentional resource allocation and information processing

“…The physiological indices' blink frequency and fixation time are sensitive to visual monitoring workload. Related studies found that a higher operator MW will give rise to a lower blink frequency and more fixation time [17][18][19]. The absence of the display interface effect on the experimental indices in the present study may be because interface 1 simulates the B787 PFD interface that has already been put into use, is widely recognized, and meets the airworthiness requirements.…”

“…NASA-TLX is a multi-dimensional MW evaluation scale developed by NASA. Because its validity matches the performance, NASA-TLX is widely harnessed in measuring and diagnosing MW [16,17]; the information cognitive performance is one of the intuitive indices to estimate display interface design. Ideal design should maintain efficient cognition under different information amount conditions.…”

Assessment and monitoring of mental workload in subway train operations using physiological, subjective, and performance measures

“…Since all drivers working in the Tehran subway system are male, 12 healthy male subway train drivers volunteered to participate in this experimental study. Usually, the number of participants in the same experimental studies have been from 9 to 20 subjects (Dorrian, Roach, Fletcher, & Dawson, ; Jay, Dawson, Ferguson, & Lamond, ; Käthner, Wriessnegger, Müller‐Putz, Kübler, & Halder, ; Lamond, Darwent, & Dawson, ; Miller, Rietschel, McDonald, & Hatfield, ; Wei, Zhuang, Wanyan, Liu, & Zhuang, ). One of the participants was excluded due to drug consumption and poor detection of electrophysiological signals.…”