Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63906

Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest

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Title:Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest
Authors:Muthumanickam, Prithiviraj
Helske, Jouni
Nordman, Aida
Johansson, Jimmy
Cooper, Matthew
Keywords:Interactive Visual Analytics and Visualization for Decision Making – Making Sense of a Growing Digital World
aoi labelling
eye-tracking
hidden markov model
multiple users
show 1 moresequence mining
show less
Date Issued:07 Jan 2020
Abstract:Eye tracking is used to analyze and compare user behaviour across diverse domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene, while OoI identification uncovers distinct objects in the scene that attract user attention. Using scalable clustering and cluster merging that is not constrained by input parameters, we label AoIs across multiple users in long duration eye tracking experiments. Using the common AoI labels then allows direct comparison of the users as well as the use of such methods as Hidden Markov Models and Sequence mining to uncover interesting behaviour across the users which, until now, has been prohibitively difficult to achieve.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63906
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.167
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Interactive Visual Analytics and Visualization for Decision Making – Making Sense of a Growing Digital World


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