Version 1
: Received: 27 September 2024 / Approved: 27 September 2024 / Online: 29 September 2024 (11:02:16 CEST)
How to cite:
Jin, W.; Liu, H.; Shen, F. AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis. Preprints2024, 2024092295. https://doi.org/10.20944/preprints202409.2295.v1
Jin, W.; Liu, H.; Shen, F. AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis. Preprints 2024, 2024092295. https://doi.org/10.20944/preprints202409.2295.v1
Jin, W.; Liu, H.; Shen, F. AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis. Preprints2024, 2024092295. https://doi.org/10.20944/preprints202409.2295.v1
APA Style
Jin, W., Liu, H., & Shen, F. (2024). AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis. Preprints. https://doi.org/10.20944/preprints202409.2295.v1
Chicago/Turabian Style
Jin, W., Haoxing Liu and Fangzhou Shen. 2024 "AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis" Preprints. https://doi.org/10.20944/preprints202409.2295.v1
Abstract
This study explores the use of Generative Artificial Intelligence (GAI) in assessing pilot fatigue risk by integrating facial recognition and physiological signals with Inertial Measurement Units (IMUs). By leveraging IMU technology's precise, real-time data on movement and combining it with GAI's advanced data analysis capabilities, the study aims to enhance the accuracy of fatigue prediction models. The analysis reveals that while traditional classifiers like Extreme Random Trees and Random Forests offer modest performance, advanced models such as Support Vector Machines and Naive Bayes demonstrate superior recall rates, highlighting their potential to identify true positives. This integration of AI and IMUs offers a promising approach to developing comprehensive, real-time fatigue monitoring systems, improving safety and efficiency in aviation by providing actionable insights and facilitating more effective fatigue management.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
