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Journal article
Methodologies Using Artificial Intelligence to Detect Cognitive Decrements in Aviation Environments
Aerospace medicine and human performance, Vol.96(4), pp.327-338
04/2025
Web of Science ID: WOS:001467573000008
Abstract
INTRODUCTION: Despite significant advancements in aerospace engineering and safety protocols over the last decade, U.S. Naval mishap rates have remained essentially unchanged. This paper explores how researchers may leverage current artificial intelligence (AI) technologies to enhance aviation safety.
METHODS: A critical review was performed identifying aviation research protocols which have incorporated machine learning (ML) to enhance the accuracy of detecting common aviation hazards leading to cognitive decrements. The review proposes a three-step methodology for creating protocols to identify cognitive decrements in aviators: 1) sensor selection; 2) preprocessing techniques; and 3) ML algorithm development. Natural language processing was utilized to assist with the development of aviation-related denoising and ML algorithm tables.
RESULTS: Several psychophysiological biosensors, enhanced by ML modeling, show promise in identifying cognitive deficits secondary to fatigue, hypoxia, and spatial disorientation. The most cited biosensors integrated with ML models include electroencephalographic, electrocardiographic, and eye-tracking devices. The application of preprocessing techniques to biosensor data is a critical methodological step prior to applying ML algorithms for data training and classification. ML algorithms utilized were categorized into supervised, unsupervised, and semi-supervised types, often used in combination for more accurate predictions.
DISCUSSION: Current literature suggests that AI, when used in conjunction with various psychophysiological sensors, can predict and potentially mitigate common aeromedical hazards such as fatigue, spatial disorientation, and hypoxia in simulated settings. The miniaturization of preprocessing and ML algorithmic hardware is the next phase of transitioning AI to operational environments for real-time continuous monitoring.
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- Title
- Methodologies Using Artificial Intelligence to Detect Cognitive Decrements in Aviation Environments
- Publication Details
- Aerospace medicine and human performance, Vol.96(4), pp.327-338
- Resource Type
- Journal article
- Publisher
- Aerospace Medical Association; ALEXANDRIA
- Number of pages
- 111
- Copyright
- Copyright © by The Authors
- Identifiers
- WOS:001467573000008; 99381327868906600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Division of Academic Affairs
- Language
- English