Eye tracking research and technology: Towards objective measurement of data quality

Abstract

Two methods for objectively measuring eye tracking data quality are explored. The first method works by tricking the eye tracker to detect an abrupt change in the gaze position of an artificial eye that in actuality does not move. Such a device, referred to as an artificial saccade generator, is shown to be extremely useful for measuring the temporal accuracy and precision of eye tracking systems and for validating the latency to display change in gaze contingent display paradigms. The second method involves an artificial pupil that is mounted on a computer controlled moving platform. This device is designed to be able to provide the eye tracker with motion sequences that closely resemble biological eye movements. The main advantage of using artificial motion for testing eye tracking data quality is the fact that the spatiotemporal signal is fully specified in a manner independent of the eye tracker that is being evaluated and that nearly identical motion sequence can be reproduced multiple times with great precision. The results of the present study demonstrate that the equipment described has the potential to become an important tool in the comprehensive evaluation of data quality.

Keywords:

• Eye movements
• Eye tracking
• Data quality

This research was supported by an NSERC grant to ER. The author is grateful to Erich Schneider, Jiye Shen, Sam Hutton, Dave Stampe, Dmitri Ogorodnikov, Klaus Bartl, Albrecht Inhoff, and Keith Rayner for their assistance and/or input. The author would also like to thank Kenneth Holmqvist and Fiona Mulvey for the invitation to participate in a panel discussion on the topic of data quality at the 17th European conference on Eye Movements (ECEM 2013) in Lund, Sweden. Finally, the author is especially indebted to George McConkie for the many discussions concerning data quality over the years.

This research was supported by an NSERC grant to ER. The author is grateful to Erich Schneider, Jiye Shen, Sam Hutton, Dave Stampe, Dmitri Ogorodnikov, Klaus Bartl, Albrecht Inhoff, and Keith Rayner for their assistance and/or input. The author would also like to thank Kenneth Holmqvist and Fiona Mulvey for the invitation to participate in a panel discussion on the topic of data quality at the 17th European conference on Eye Movements (ECEM 2013) in Lund, Sweden. Finally, the author is especially indebted to George McConkie for the many discussions concerning data quality over the years.