@inproceedings{3025534f08834bf9922426e4acb28026,
title = "A stress classification model based on physiological features in aviation operational tasks",
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.",
keywords = "Classification models, Ergonomics, Flight simulation, Machine learning, Stress",
author = "Chunying Qian and Shuang Liu and Yihang Wang and Kun Ji and Xiaoru Wanyan",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 3rd International Conference on Intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024 ; Conference date: 05-07-2024 Through 07-07-2024",
year = "2024",
doi = "10.1117/12.3049624",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xiangjie Kong and Xingjian Wang",
booktitle = "Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024",
address = "美国",
}