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27 September 2024 A stress classification model based on physiological features in aviation operational tasks
Chunying Qian, Shuang Liu, Yihang Wang, Kun Ji, Xiaoru Wanyan
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Proceedings Volume 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024); 1328410 (2024) https://doi.org/10.1117/12.3049624
Event: Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 2024, Hangzhou, China
Abstract
In high-risk operational environments, pilots are susceptible to stress reactions, which can adversely influence their cognitive process and operational performance. Thus, it is crucial to devise effective methods for assessing pilots' stress levels. This research developed a pilot stress classification model employing eye-tracking and heart rate variability (HRV) features within a simulated aviation task context. Utilizing a neural network algorithm, a three-tier stress classification model was established, demonstrating a five-fold cross-validation accuracy of 80.12%. The results provide methodological support for the evaluation of stress states in pilots. Furthermore, the result can provide a foundation for pilots' training and selection processes, thereby improving their capacity to manage stress and ensure aviation safety.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Chunying Qian, Shuang Liu, Yihang Wang, Kun Ji, and Xiaoru Wanyan "A stress classification model based on physiological features in aviation operational tasks", Proc. SPIE 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 1328410 (27 September 2024); https://doi.org/10.1117/12.3049624
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KEYWORDS
Neural networks
Data modeling
Eye tracking
Machine learning
Performance modeling
Engineering
Safety
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