Visually comparing eye movements over space and time

Ayush Kumar, Michael Burch, Klaus Mueller

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

Analyzing and visualizing eye movement data can provide useful insights into the connectivities and linkings of points and areas of interest (POIs and AOIs). Those typically time-varying relations can give hints about applied visual scanning strategies by either individual or many eye tracked people. However, the challenging issue with this kind of data is its spatio-temporal nature requiring a good visual encoding in order to first, achieve a scalable overview-based diagram, and second, to derive static or dynamic patterns that might correspond to certain comparable visual scanning strategies. To reliably identify the dynamic strategies we describe a visualization technique that generates a more linear representation of the spatio-temporal scan paths. This is achieved by applying different visual encodings of the spatial dimensions that typically build a limitation for an eye movement data visualization causing visual clutter effects, overdraw, and occlusions while the temporal dimension is depicted as a linear time axis. The presented interactive visualization concept is composed of three linked views depicting spatial, metrics-related, as well as distance-based aspects over time.

Original languageEnglish
Title of host publicationProceedings - ETRA 2019
Subtitle of host publication2019 ACM Symposium On Eye Tracking Research and Applications
EditorsStephen N. Spencer
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)978-145036709-7
DOIs
Publication statusPublished - 25 Jun 2019
Event11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019 - Denver, United States
Duration: 25 Jun 201928 Jun 2019

Conference

Conference11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019
Country/TerritoryUnited States
CityDenver
Period25/06/1928/06/19

Keywords

  • Eye tracking
  • Information visualization
  • Visual analytics

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