Exploring Latent Biometric Constructs in a Model Predicting Mental States of Aviators

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  • Single-crew aircraft persistently have a high accident rate; these accidents are associated with high mental workload (MWL). The aviation industry would benefit from a passive MWL monitoring system that would predict flight performance. Passive biosensors offer an economical and non-intrusive method for indexing MWL. Many studies have overemphasized tonic data while ignoring phasic data. The present study explores the viability of a phasic data centered model in indexing MWL to predict flight performance. The study had non-pilots fly a simulator. Cardiovascular and epidermal data, objective and subjective MWL states, subjective reports of simulator sickness, and a variety of flight performance indicators were measured. The data were decomposed into several components to build formative latent variables that were pruned based on an objective MWL measure to then predict flight performance measures. The results indicate that phasic components explain more variance in flight performance than objective and subjective MWL and tonic data.

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  • Copyright © 2023 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2023


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