Using Gaze Analytics for Predicting Pilot Success and Failure

Abstract

Eye tracking has emerged as a powerful tool with a broad spectrum of applications across various domains. In this study, we harness eye-tracking technology to collect gaze data from participants and explore its potential to predict pilot success or failure during take-off simulations.

We investigate the significance of gaze analytics in predictive analytics, employing a group of participants and recording their eye movements using eye-tracking technology as they engage in take-off simulations using X-Plane 11. The study’s data is meticulously gathered and processed to analyze using off-the-shelf machine-learning algorithms.

Our evaluation of the results highlights the potential and significance of eye gaze and predictive analytics in predicting pilot success and failure. This study can potentially pave the way for more advanced applications in predictive analytics and human performance assessment.