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Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices

Jeff J. Macinnes, Shariq Iqbal, John Pearson, Elizabeth N. Johnson
doi: https://doi.org/10.1101/299925
Jeff J. Macinnes
1Institute for Learning and Brain Sciences University of Washington Seattle, WA USA
2Center for Cognitive Neuroscience Duke University Durham, NC USA
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Shariq Iqbal
2Center for Cognitive Neuroscience Duke University Durham, NC USA
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John Pearson
2Center for Cognitive Neuroscience Duke University Durham, NC USA
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Elizabeth N. Johnson
3Wharton Neuroscience Initiative University of Pennsylvania Philadelphia, PA USA
4Department of Neurobiology Duke University Durham, NC USA
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Abstract

Wearable eye-trackers offer exciting advantages over screen-based systems, but their use in research settings has been hindered by significant analytic challenges as well as a lack of published performance measures among competing devices on the market. In this article, we address both of these limitations. We describe (and make freely available) an automated analysis pipeline for mapping gaze data from an egocentric coordinate system (i.e. the wearable eye-tracker) to a fixed reference coordinate system (i.e. a target stimulus in the environment). This pipeline allows researchers to study aggregate viewing behavior on a 2D planar target stimulus without restricting the mobility of participants. We also designed a task to directly compare calibration accuracy and precision across 3 popular models of wearable eye-trackers: Pupil Labs 120Hz Binocular glasses, SMI ETG 2 glasses, and the Tobii Pro Glasses 2. Our task encompassed multiple viewing conditions selected to approximate distances and gaze angles typical for short- to mid-range viewing experiments. This work will promote and facilitate the use of wearable eye-trackers for research in naturalistic viewing experiments.

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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 June 28, 2018.
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Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices
Jeff J. Macinnes, Shariq Iqbal, John Pearson, Elizabeth N. Johnson
bioRxiv 299925; doi: https://doi.org/10.1101/299925
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Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices
Jeff J. Macinnes, Shariq Iqbal, John Pearson, Elizabeth N. Johnson
bioRxiv 299925; doi: https://doi.org/10.1101/299925

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