A comprehensive prediction and evaluation method of pilot workload

Feng, Chuanyan; Wanyan, Xiaoru; Yang, Kun; Zhuang, Damin; Wu, Xu · 2018 · Crossref

DOI: 10.3233/thc-174201

archive: archived pipeline: cataloged verified

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Summary

This study addresses the critical challenge of predicting and evaluating pilot workload, a key factor in human-factor airworthiness and aviation safety. Motivated by the high incidence of human error in aviation accidents and the need for objective assessment methods beyond subjective reports, the researchers developed a comprehensive method combining theoretical workload prediction with physiological data analysis. The study specifically focused on pilots using Head-Up Displays (HUD) during traffic pattern tasks, aiming to validate a prediction model and construct a discriminative evaluation model for different flight phases. The methodology involved a flight simulation experiment with fourteen male flying cadets performing a dynamic traffic pattern task comprising cruise, approach, and landing phases. Workload was theoretically predicted using Multiple Resource Theory combined with the McCrachen-Aldrich scale and Timeline Analysis and Prediction method. Simultaneously, physiological data were collected using eye-tracking (Tobii TX300), electrocardiogram (ECG), and electrodermal activity (EDA) sensors. Statistical analyses, including repeated-measures ANOVA and Pearson correlation, were employed to identify sensitive physiological indices. Finally, a multinomial logistic regression model was constructed to classify workload levels based on selected physiological markers. The results demonstrated that specific physiological indices were significantly sensitive to workload variations. Eye movement metrics such as fixation frequency, mean fixation time, saccade frequency, and mean pupil diameter showed significant changes across flight phases. ECG indices, particularly the mean normal-to-normal (NN) interval and the ratio of low frequency to the sum of low and high frequencies (LF/LHF), also varied significantly with workload. EDA indices, including mean tonic and mean phasic activity, increased with higher workload demands. Correlation analysis confirmed a strong relationship between the theoretically predicted workload values and these physiological measures. The constructed multinomial logistic regression model, utilizing fixation frequency, mean NN, LF/LHF, and mean tonic as inputs, achieved a discrimination accuracy of 84.85% in classifying the three flight phases. The significance of this research lies in its provision of a validated, objective method for pilot workload assessment that integrates theoretical prediction with physiological monitoring. By demonstrating that a combination of eye movement, heart rate variability, and skin conductance data can accurately discriminate between different workload states, the study offers a robust tool for human-factor airworthiness certification. This approach reduces reliance on subjective assessments and provides a physiological basis for evaluating cockpit ergonomics and display systems, particularly HUDs, thereby contributing to enhanced flight safety and operational efficiency.

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Topics

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• workload measurement
• mental demand

Information type

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• Empirical Findings: physiological data
• Methodological Resource: metric or index
• Theoretical Contribution: theory or model