Finding the outliers in scanpath data

Michael Burch, Ayush Kumar, Klaus Mueller, Titus Kervezee, Wouter Nuijten, Rens Oostenbach, Lucas Peeters, Gijs Smit

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

1 Citation (Scopus)

Abstract

In this paper, we describe the design of an interactive visualization tool for the comparison of eye movement data with a special focus on the outliers. In order to make the tool usable and accessible to anyone with a data science background, we provide a web-based solution by using the Dash library based on the Python programming language and the Python library Plotly. Interactive visualization is very well supported by Dash, which makes the visualization tool easy to use. We support multiple ways of comparing user scanpaths like bounding boxes and Jaccard indices to identify similarities. Moreover, we support matrix reordering to clearly separate the outliers in the scanpaths. We further support the data analyst by complementary views such as gaze plots and visual attention maps.

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 pages5
ISBN (Electronic)9781450367097
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
CountryUnited States
CityDenver
Period25/06/1928/06/19

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Keywords

  • Eye tracking
  • Information visualization
  • Visual analytics

Cite this

Burch, M., Kumar, A., Mueller, K., Kervezee, T., Nuijten, W., Oostenbach, R., ... Smit, G. (2019). Finding the outliers in scanpath data. In S. N. Spencer (Ed.), Proceedings - ETRA 2019: 2019 ACM Symposium On Eye Tracking Research and Applications [83] New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/3317958.3318225