Analyzing Eye Tracking Data using Symbolic Aggregate Approximation

Abstract

Oculomotor disturbance (OMD) is a common vision problem, meaning that the left and right eye do not cooperate properly, i.e., by having a common gaze point. Eye tracking technology (ET) promises support for identifying problematic eye coordination. Data from the eye tracker is based on time series of screen positions and the stimuli movements, which are recorded, following a structural pattern. A vision specialist can analyze and interpret the graphical plots of the time series visually to get a better understanding of the problems related to gaze movements. However, this is tedious and time consuming due to the huge amount of data collected by ETs, or the necessity for replaying the tests. This paper explores a method to automatically analyzing the results of a screening to indicate potential OMD problems by applying Symbolic Aggregate Approximation (SAX) and pinpointing relevant features for OMD. The potential benefits of the method are investigated via examples considering the distance between left and right gaze points. This indicates promising results for faster examining large data sets and discussing possibilities for future extensions for considering eye movement parameters based on real-time measurements of the distance between the stimuli and the gaze points.