International Journal of Computer Techniques Volume 12 Issue 4 | Leveraging Machine Learning and Psychophysiology Data for Safer skies
Leveraging Machine Learning and Psychophysiology Data for Safer Skies
Authors:
K. Gayathri – MCA Student, Department of Information Technology, Jawaharlal Nehru Technological University, India
Dr. M. Dhanalakshmi – Professor of IT, Department of Information Technology, Jawaharlal Nehru Technological University, India
Journal: International Journal of Computer Techniques (IJCT)
Volume: 12 | Issue: 4 | Publication Date: July – August 2025
ISSN: 2394-2231 | Journal URL: https://ijctjournal.org/
Abstract
This paper presents a systematic review of machine learning applications in aviation safety, focusing on pilot behavior analysis using psychophysiological data. Analyzing 80 peer-reviewed studies, the review identifies key trends, gaps, and future directions. EEG is the most commonly used modality, while emotional and attentional factors remain underexplored. The study calls for multi-modal data integration, advanced preprocessing, and explainable AI to improve pilot state assessment and air safety.
Keywords
Air Safety, Behavioral Indicators, Machine Learning, Pilot Workload, Psychophysiology Data
Conclusion
The review highlights the dominance of EEG in pilot monitoring and the need to expand behavioral dimensions studied. Emotional responses and attention dynamics are critical yet underrepresented. Future research should adopt integrative approaches using diverse data sources and interpretable ML models to build more human-centric aviation safety systems.
References
Includes 22 references from PRISMA, FAA, IEEE, PLoS Medicine, Scientific Reports, and arXiv covering aviation safety, psychophysiology, and ML-based pilot monitoring.
