Identification of the Psychophysiological State of Air Traffic Controllers Based on Eye-Tracking Monitoring Using Fuzzy Logic

DOI

10.2991/978-94-6239-668-5_94How to use a DOI?

Keywords

Air traffic controllers; Eye-tracking monitoring; Psychophysiological state; Fuzzy logic; Cognitive workload and fatigue

Abstract

Human performance monitoring has become a key component of next-generation aviation safety systems, as the human factor remains one of the leading causes of operational incidents. Among various physiological and behavioral indicators, eye-tracking data provide a direct and non-intrusive measure of cognitive workload, attention, and fatigue. This paper presents a fuzzy expert system for real-time identification of the psychophysiological state of air traffic controllers based on monitoring data obtained through the eye-tracking method. The developed expert system operates using four types of oculomotor metrics that characterize fixations, saccades, pupil variations, and blink dynamics.

The fuzzy rule base, which constitutes the core of the system’s knowledge base, was constructed by generalizing expert knowledge and empirical observations obtained during the registration and statistical analysis of eye-tracking parameters of air traffic controllers. Based on the data, multifactor dependencies were identified that describe the dynamic relationship between oculomotor behavior and the operator’s psycho-physiological state. This enabled the implementation of an adaptive fuzzy inference mechanism capable of accounting for individual variability and cognitive response dynamics in real time. The fuzzy logic approach with the Mamdani decision-making procedure effectively addresses uncertainty, individual differences, and the nonlinear nature of human physiological data, outperforming traditional threshold-based methods.

The developed system provides continuous and adaptive assessment of cognitive readiness without interfering with operator performance. Integration of the above-mentioned indicators with other psychophysiological parameters enhances the re-liability of decision-making. The proposed methodology represents a step toward human-centered intelligent aviation systems, combining fuzzy logic, psychophysiological sensing, and ma-chine learning techniques to improve safety and operational efficiency in complex human–machine environments.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - G. Ahmadova
AU  - F. Dadashov
AU  - N. Huseynov
AU  - S. Ashirova
PY  - 2026
DA  - 2026/05/14
TI  - Identification of the Psychophysiological State of Air Traffic Controllers Based on Eye-Tracking Monitoring Using Fuzzy Logic
BT  - Proceedings of the International Conference on Current Problems in Engineering and Applied Sciences (ICCPEAS 2025)
PB  - Atlantis Press
SP  - 893
EP  - 900
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-668-5_94
DO  - 10.2991/978-94-6239-668-5_94
ID  - Ahmadova2026
ER  -