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    Rights statement: © ACM, 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications http://dx.doi.org/10.1145/3204493.3204585

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Smooth-i: smart re-calibration using smooth pursuit eye movements

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Publication date14/06/2018
Host publicationETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications
PublisherACM
Number of pages5
ISBN (Print)9781450357067
Original languageEnglish

Abstract

Eye gaze for interaction is dependent on calibration. However, gaze calibration can deteriorate over time affecting the usability of the system. We propose to use motion matching of smooth pursuit eye movements and known motion on the display to determine when there is a drift in accuracy and use it as input for re-calibration. To explore this idea we developed Smooth-i, an algorithm that stores calibration points and updates them incrementally when inaccuracies are identified. To validate the accuracy of Smooth-i, we conducted a study with five participants and a remote eye tracker. A baseline calibration profile was used by all participants to test the accuracy of the Smooth-i re-calibration following interaction with moving targets. Results show that Smooth-i is able to manage re-calibration efficiently, updating the calibration profile only when inaccurate data samples are detected.

Bibliographic note

© ACM, 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications http://dx.doi.org/10.1145/3204493.3204585