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dc.contributor.authorHelgesen, Carsten Gunnar
dc.contributor.authorGeitung, Atle Birger
dc.contributor.authorHeldal, Ilona
dc.contributor.authorBorgli, Henrik
dc.identifier.citationCEUR Workshop Proceedings. 2023, 3440 .en_US
dc.description.abstractOculomotor 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.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleAnalyzing Eye Tracking Data using Symbolic Aggregate Approximationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2023 Copyright for this paper by its authorsen_US
dc.source.journalCEUR Workshop Proceedingsen_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal