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2019 (English)In: Hawaii International Conference on System Sciences 2020, 2019Conference paper, Published paper (Refereed)
Abstract [en]
Eye tracking is used to analyze and compare user behaviour within numerous 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 techniques that require minimal user input, 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 common and distinct behaviour between the users which, until now, has been prohibitively difficult to achieve.
Series
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), ISSN 1530-1605, E-ISSN 2572-6862
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-161999 (URN)10.24251/HICSS.2020.167 (DOI)978-0-9981331-3-3 (ISBN)
Conference
Hawaii International Conference on System Sciences
2019-11-152019-11-152021-09-30