AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis

Weibo Jin; Haoxing Liu; Fangzhou Shen

doi:10.62051/ijcsit.v4n1.38

Authors

Weibo Jin
Haoxing Liu
Fangzhou Shen

DOI:

https://doi.org/10.62051/ijcsit.v4n1.38

Keywords:

Generative AI, Inertial Measurement Units (IMUs), Fatigue Risk Assessment, Facial Recognition

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.

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