Object-gaze distance: Quantifying near-peripheral gaze behavior in real-world applications

  • Felix Sihan Wang Product Development Group Zurich, Swiss Federal Institute of Technology (ETH)
  • Julian Wolf Product Development Group Zurich, Swiss Federal Institute of Technology (ETH)
  • Mazda Farshad Department of Orthopaedics, Balgrist University Hospital, Zurich, Switzerland
  • Mirko Meboldt Product Development Group Zurich, Swiss Federal Institute of Technology (ETH)
  • Quentin Lohmeyer Product Development Group Zurich, Swiss Federal Institute of Technology (ETH)
Keywords: mobile eye tracking, peripheral vision, areas of interest, machine learning, object detection, visual expertise

Abstract

Eye tracking (ET) has shown to reveal the wearer’s cognitive processes using the measurement of the central point of foveal vision. However, traditional ET evaluation methods have not been able to take into account the wearers’ use of the peripheral field of vision. We propose an algorithmic enhancement to a state-of-the-art ET analysis method, the Object-Gaze Distance (OGD), which additionally allows the quantification of near-peripheral gaze behavior in complex real-world environments. The algorithm uses machine learning for area of interest (AOI) detection and computes the minimal 2D Euclidean pixel distance to the gaze point, creating a continuous gaze-based time-series. Based on an evaluation of two AOIs in a real surgical procedure, the results show that a considerable increase of interpretable fixation data from 23.8 % to 78.3 % of AOI screw and from 4.5 % to 67.2 % of AOI screwdriver was achieved, when incorporating the near-peripheral field of vision. Additionally, the evaluation of a multi-OGD time series representation has shown the potential to reveal novel gaze patterns, which may provide a more accurate depiction of human gaze behavior in multi-object environments.

Author Biographies

Felix Sihan Wang, Product Development Group Zurich, Swiss Federal Institute of Technology (ETH)

Doctural Student, Chair of Product Dev.& Eng. Design, ETH Zurich

Julian Wolf, Product Development Group Zurich, Swiss Federal Institute of Technology (ETH)

Doctural Student, Chair of Product Dev.& Eng. Design,  ETH Zurich

 

Mazda Farshad, Department of Orthopaedics, Balgrist University Hospital, Zurich, Switzerland

Professor and Chair of Orthopedic Surgery
University Hospital Balgrist, Zürich, Switzerland
Chief of Spine and Scoliosis Surgery

Mirko Meboldt, Product Development Group Zurich, Swiss Federal Institute of Technology (ETH)

Professor and Head of CAS ETH Applied Manufacturing Technology, ETH Zurich

 

Quentin Lohmeyer, Product Development Group Zurich, Swiss Federal Institute of Technology (ETH)

Dr.-Ing., Senir scientist and lecturer,  Chair of Product Dev.& Eng. Design, ETH Zurich

 

Published
2021-05-19
How to Cite
Wang, F., Wolf, J., Farshad, M., Meboldt, M., & Lohmeyer, Q. (2021). Object-gaze distance: Quantifying near-peripheral gaze behavior in real-world applications. Journal of Eye Movement Research, 14(1). https://doi.org/10.16910/jemr.14.1.5
Section
Articles