Using eye tracking metrics to measure and predict situation awareness of air traffic controllers - Yoong, Valencia Rui Qin (Nanyang Technological University, 2020)
Current human measures of situation awareness (SA) are either intrusive or conducted post-activity. As a result, they are unsuitable for use in the field as they are unable to assess SA in real-time. This project aims to propose a new method of using a physiological measure to replace a human measure of SA in the context of air traffic control (ATC). The chosen physiological measure is eye tracking, which is able to collect data non-intrusively in real-time. The chosen human measure is Situation Awareness Global Assessment Technique (SAGAT), which is the most reliable and validated objective measurement of SA in the domain of ATC. However, it is only able to measure and score SA at discrete points in time. The replacement of human measure by physiological measure is done by conducting ATC simulation experiments to collect SAGAT scores and eye tracking data synchronously. Results from this study show that the combination of two eye tracking metrics—minimum fixation count and minimum fixation duration—is an accurate method to predict SAGAT scores, with an accuracy of more than 70% for each SAGAT query. This leads to the conclusion that the human measure can be replaced by a physiological measure of SA.