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Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture

View ORCID ProfileScott A. Stone, Quinn A. Boser, T. Riley Dawson, Albert H. Vette, Jacqueline S. Hebert, Patrick M. Pilarski, Craig S. Chapman
doi: https://doi.org/10.1101/2021.10.22.465332
Scott A. Stone
1Department of Psychology, University of Alberta, E-mail:
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  • For correspondence: sastone@ualberta.ca sastone@ualberta.ca
Quinn A. Boser
2Division of Physical Medicine and Rehabilitation, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, E-mail:
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  • For correspondence: boser@ualberta.ca
T. Riley Dawson
3Division of Physical Medicine and Rehabilitation, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, E-mail:
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  • For correspondence: trd@ualberta.ca
Albert H. Vette
4Department of Mechanical Engineering, University of Alberta, E-mail:
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  • For correspondence: vette@ualberta.ca
Jacqueline S. Hebert
5Division of Physical Medicine and Rehabilitation, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, E-mail:
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  • For correspondence: jhebert@ualberta.ca
Patrick M. Pilarski
6Division of Physical Medicine and Rehabilitation, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Alberta Machine Intelligence Institute, E-mail:
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  • For correspondence: pilarski@ualberta.ca
Craig S. Chapman
7Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Neuroscience and Mental Health Institute, University of Alberta, E-mail:
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  • For correspondence: c.s.chapman@ualberta.ca
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Abstract

Assessing gaze behaviour during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make gaze analysis difficult. Current approaches involve laborious coding of pupil positions. In settings where motion capture and mobile eye tracking are used concurrently in naturalistic tasks, it is critical that data collection be simple, efficient, and systematic. One solution is to combine eye tracking with motion capture to generate 3D gaze vectors. When combined with tracked or known object locations, 3D gaze vector generation can be automated. Here we use combined eye and motion capture and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three pupil data inputs: the efficacy of calibration routines were assessed, a validation task requiring short fixations on taskrelevant locations, and a naturalistic object interaction task to bridge the gap between laboratory and “in the wild” studies. Further, we generated and compared models using spherical and cartesian coordinate systems and monocular (Left or Right) or binocular data. All calibration routines performed similarly, with the best performance (i.e., sub-centimetre errors) coming from the naturalistic task trials when the participant is looking at an object in front of them. We found that spherical coordinate systems generate the most accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend one-minute calibration routines using binocular pupil data combined with a spherical world coordinate system to produce the highest quality gaze vectors.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The abstract has been rewritten to better target clinician scientists and those interested in collecting ecologically valid task spaces. The figures have all been remade to be of a higher quality vector format. The results figures have been changed to include the underlying data as points.

  • https://osf.io/znvwb/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 25, 2022.
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Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture
Scott A. Stone, Quinn A. Boser, T. Riley Dawson, Albert H. Vette, Jacqueline S. Hebert, Patrick M. Pilarski, Craig S. Chapman
bioRxiv 2021.10.22.465332; doi: https://doi.org/10.1101/2021.10.22.465332
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Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture
Scott A. Stone, Quinn A. Boser, T. Riley Dawson, Albert H. Vette, Jacqueline S. Hebert, Patrick M. Pilarski, Craig S. Chapman
bioRxiv 2021.10.22.465332; doi: https://doi.org/10.1101/2021.10.22.465332

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