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

Scott A Stone1,2, Quinn A Boser3, T Riley Dawson3

  • 1Department of Psychology, University of Alberta, Edmonton, Alberta, Canada. sastone@ualberta.ca.

Behavior Research Methods
|September 10, 2022
PubMed
Summary

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This summary is machine-generated.

Accurate 3D gaze vectors are crucial for studying real-world behavior. This study found that 1-minute calibrations using binocular eye tracking and spherical coordinates yield the most precise gaze data.

Area of Science:

  • Human-Computer Interaction
  • Biomedical Engineering
  • Neuroscience

Background:

  • Gaze behavior analysis in real-world settings is challenging due to dynamic environments and body movements.
  • Current methods for pupil position coding are laborious and time-consuming.
  • Integrating eye tracking with motion capture offers a path to automated 3D gaze vector generation.

Purpose of the Study:

  • To automate the generation of accurate 3D gaze vectors by combining eye tracking and motion capture data.
  • To evaluate the spatial accuracy of linear regression models for 3D gaze vector generation.
  • To compare the efficacy of different calibration routines and data inputs.

Main Methods:

  • Combined eye tracking and motion capture to record gaze and body movement data.
Keywords:
CalibrationEye trackingGaze vectorLinear regressionMotion capture

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  • Developed and compared linear regression models using various calibration routines and pupil data inputs (monocular/binocular).
  • Evaluated models using a validation task and a naturalistic object interaction task.

    • Main Results:

    • All calibration routines showed similar performance, with naturalistic task trials yielding the best accuracy (sub-centimeter errors).
    • Spherical coordinate systems produced more accurate gaze vectors than Cartesian systems.
    • No significant difference in accuracy was observed between monocular and binocular data.

    Conclusions:

    • A 1-minute calibration routine using binocular pupil data and a spherical world coordinate system is recommended for high-quality 3D gaze vector generation.
    • This approach bridges the gap between laboratory-based and real-world gaze analysis.
    • Automated 3D gaze vector generation enhances the efficiency and systematic nature of data collection in naturalistic tasks.