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
Accepted author manuscript, 546 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Licence: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Smooth-i
T2 - smart re-calibration using smooth pursuit eye movements
AU - Ramirez Gomez, Argenis
AU - Gellersen, Hans
N1 - © 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
PY - 2018/6/14
Y1 - 2018/6/14
N2 - 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.
AB - 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.
KW - Gaze Calibration
KW - Smooth Pursuits
KW - Gaze interaction
KW - Eye movements
KW - Eye tracking
U2 - 10.1145/3204493.3204585
DO - 10.1145/3204493.3204585
M3 - Conference contribution/Paper
SN - 9781450357067
BT - ETRA '18 Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications
PB - ACM
ER -