Watch out for those bananas! Gaze Based Mario Kart Performance Classification

Wolfgang Fuhl, Björn Severitt, Nora Castner, Babette Bühler, Johannes Meyer, Daniel Weber, Regine Lendway, Ruikun Hou, Enkelejda Kasneci

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

Abstract

This paper is about a small eye tracking study for scan path classification. Seven participants played Mario Kart while wearing a head mounted eye tracker. In total, we had 64 recordings, but one had to be removed (Only 79 gaze samples were recorded). We compared different scan path classification features to estimate the performance of the participants based on the ranking they achieved. The best performing feature was ENCODJI which incooperates saccades and the heatmap in one feature. HOV, which uses saccade angles, performed well for all tasks but was outperformed by the heatmap (HEAT) for the last two groups.

Original languageEnglish
Title of host publicationProceedings - ETRA 2023
Subtitle of host publicationACM Symposium on Eye Tracking Research and Applications
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400701504
DOIs
StatePublished - 30 May 2023
Event15th Annual ACM Symposium on Eye Tracking Research and Applications, ETRA 2023 - Tubingen, Germany
Duration: 30 May 20232 Jun 2023

Publication series

NameEye Tracking Research and Applications Symposium (ETRA)

Conference

Conference15th Annual ACM Symposium on Eye Tracking Research and Applications, ETRA 2023
Country/TerritoryGermany
CityTubingen
Period30/05/232/06/23

Keywords

  • Classification
  • Eye Tracking
  • Gaze
  • Mario Kart
  • Scanpath
  • Score

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