Fatigue Detection of Air Traffic Controllers Through Their Eye Movements
Eye movement patterns have become an essential element in modern approaches for identifying air traffic controller fatigue. By observing eye movements among various individuals and environments, researchers have discovered correlations with multiple physiological metrics and cognitive processing abi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/12/981 |
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| author | Yi Hu Haoran Shen Hui Pan Wenbin Wei |
| author_facet | Yi Hu Haoran Shen Hui Pan Wenbin Wei |
| author_sort | Yi Hu |
| collection | DOAJ |
| description | Eye movement patterns have become an essential element in modern approaches for identifying air traffic controller fatigue. By observing eye movements among various individuals and environments, researchers have discovered correlations with multiple physiological metrics and cognitive processing abilities. This study involved human-in-the-loop simulations to collect eye movement and fatigue data from air traffic controllers and students. The eye movements were classified into three main types: fixation, saccade, and blink. Statistical analyses were performed to determine the most important indicators. Using support vector machine and random forest models for training and prediction, it was found that the fixation characteristic is significantly important for monitoring air traffic controller fatigue. The implementation of this model has the potential to identify forthcoming instances of controller fatigue during their shifts, thereby helping to avert the possibility of unsafe situations. |
| format | Article |
| id | doaj-art-ddd5846f8c1b4b35af4625ff642adda0 |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-ddd5846f8c1b4b35af4625ff642adda02025-08-20T02:00:50ZengMDPI AGAerospace2226-43102024-11-01111298110.3390/aerospace11120981Fatigue Detection of Air Traffic Controllers Through Their Eye MovementsYi Hu0Haoran Shen1Hui Pan2Wenbin Wei3College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaDepartment of Aviation and Technology, San Jose State University, San Jose, CA 95192, USAEye movement patterns have become an essential element in modern approaches for identifying air traffic controller fatigue. By observing eye movements among various individuals and environments, researchers have discovered correlations with multiple physiological metrics and cognitive processing abilities. This study involved human-in-the-loop simulations to collect eye movement and fatigue data from air traffic controllers and students. The eye movements were classified into three main types: fixation, saccade, and blink. Statistical analyses were performed to determine the most important indicators. Using support vector machine and random forest models for training and prediction, it was found that the fixation characteristic is significantly important for monitoring air traffic controller fatigue. The implementation of this model has the potential to identify forthcoming instances of controller fatigue during their shifts, thereby helping to avert the possibility of unsafe situations.https://www.mdpi.com/2226-4310/11/12/981air traffic controlhuman factoreye movementfatigue detectionmachine learning |
| spellingShingle | Yi Hu Haoran Shen Hui Pan Wenbin Wei Fatigue Detection of Air Traffic Controllers Through Their Eye Movements Aerospace air traffic control human factor eye movement fatigue detection machine learning |
| title | Fatigue Detection of Air Traffic Controllers Through Their Eye Movements |
| title_full | Fatigue Detection of Air Traffic Controllers Through Their Eye Movements |
| title_fullStr | Fatigue Detection of Air Traffic Controllers Through Their Eye Movements |
| title_full_unstemmed | Fatigue Detection of Air Traffic Controllers Through Their Eye Movements |
| title_short | Fatigue Detection of Air Traffic Controllers Through Their Eye Movements |
| title_sort | fatigue detection of air traffic controllers through their eye movements |
| topic | air traffic control human factor eye movement fatigue detection machine learning |
| url | https://www.mdpi.com/2226-4310/11/12/981 |
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