Cognitive Context Detection in UAS Operators Using Eye-Gaze Patterns on Computer Screens

In this paper, we demonstrate the use of eye-gaze metrics of unmanned aerial systems (UAS) operators as effective indices of their cognitive workload. Our analyses are based on an experiment where twenty participants performed pre-scripted UAS missions of three different difficulty levels by interacting with two custom designed graphical user interfaces (GUIs) that are displayed side by side. First, we compute several eye-gaze metrics, traditional eye movement metrics as well as newly proposed ones, and analyze their effectiveness as cognitive classifiers. Most of the eye-gaze metrics are computed by dividing the computer screen into "cells". Then, we perform several analyses in order to select metrics for effective cognitive context classification related to our specific application; the objective of these analyses are to (i) identify appropriate ways to divide the screen into cells; (ii) select appropriate metrics for training and classification of cognitive features; and (iii) identify a suitable classification method.