Accession Number:

AD1151053

Title:

Reducing Aviation Fatalities by Monitoring Pilots' Cognitive States Using Psychophysiological Measurements

Descriptive Note:

[Technical Report, Master's Thesis]

Corporate Author:

Naval Postgraduate School

Personal Author(s):

• Lin, Yi-chung

Report Date:

2021-06-01

Pagination or Media Count:

83

Abstract:

Airplane accidents are usually catastrophic, and the majority of flight-related accidents are caused by a lack of situational awareness during flight. To improve flight safety, we built a model to detect the cognitive states of pilots from their psychophysiological signals so that the aviators can be warned before falling into a dangerous mental state, including channelized attention, diverted attention, and startlesurprise. The research is composed of time series analysis and classification. We used seasonal decomposition, exponential smoothing, and autoregressive integrated moving average models to analyze the numerical psychophysiological measurements of 18 pilots and utilize such measurements to distinguish their cognitive states by classification methods, such as random forest, support vector machine, and logistic regression. The results can be a part of the risk management mechanism to alert pilots when necessary. The deliverables include a classification model of the problem and an analysis of the solutions obtained from the model. These models are written in R so that anyone can run calculations in real time to monitor the cognitive states of pilots and to support follow-onfuture analysis work.

Descriptors:

• supervised machine learning
• machine learning
• data analysis
• flight crews
• accuracy
• identification systems
• information science
• operations research
• aircrafts
• cognitive science
• commercial aviation
• medical personnel
• pattern recognition
• accidents
• algorithms
• california
• data science

Subject Categories:

• Psychology
• Military Aircraft Operations

Distribution Statement:

[A, Approved For Public Release]