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

Argenis Ramirez Gomez, Hans Gellersen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationETRA '18: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Print)9781450357067
Publication statusPublished - 14 Jun 2018
Event2018 ACM Symposium on Eye Tracking Research & Applications: ETRA 2018 - Warsaw, Poland
Duration: 14 Jun 201817 Jun 2018


Conference2018 ACM Symposium on Eye Tracking Research & Applications
Internet address


  • Gaze Calibration
  • Smooth Pursuits
  • Gaze interaction
  • Eye movements
  • Eye tracking


Dive into the research topics of 'Smooth-i: smart re-calibration using smooth pursuit eye movements'. Together they form a unique fingerprint.

Cite this