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Related Experiment Video

Updated: Sep 14, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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eyeNotate: Interactive Annotation of Mobile Eye Tracking Data Based on Few-Shot Image Classification.

Michael Barz1,2, Omair Shahzad Bhatti1, Hasan Md Tusfiqur Alam1

  • 1Interactive Machine Learning, German Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, Germany; omair_shahzad.bhatti@dfki.de (O.S.B.); hasan_md_tusfiqur.alam@dfki.de (H.M.T.A.); ho_minh_duy.nguyen@dfki.de (D.M.H.N.); daniel.sonntag@dfki.de (D.S.).

Journal of Eye Movement Research
|July 25, 2025
PubMed
Summary
This summary is machine-generated.

We developed eyeNotate, a web-based tool for semi-automatic mobile eye tracking data annotation. It uses machine learning to suggest fixation-to-area mappings, significantly improving annotation efficiency and reliability for researchers.

Keywords:
area of interest (AOI)eye trackingeye tracking data analysisfixation-to-AOI mappinginteractive machine learningmobile eye trackingvisual attention

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Usability Engineering

Background:

  • Mobile eye tracking is crucial for understanding visual attention in psychology and interaction design.
  • Analyzing mobile eye tracking data is currently a manual and time-intensive process.
  • Existing methods lack efficiency and scalability for large datasets.

Purpose of the Study:

  • To develop and evaluate eyeNotate, a novel web-based tool for semi-automatic annotation of mobile eye tracking data.
  • To compare the efficiency, validity, and reliability of a baseline annotation tool versus one enhanced with machine learning (IML-support).
  • To assess the usability and user experience of the eyeNotate tool through expert evaluation.

Main Methods:

  • Development of eyeNotate, a web-based annotation tool with baseline and IML-support versions.
  • An expert study (n=3) comparing the two versions on usability, annotation validity, reliability, and efficiency.
  • Re-annotation of existing mobile eye tracking data (n=48) by trained annotators.
  • Semi-structured interviews to gather qualitative feedback on IML feature integration.
  • A post hoc experiment evaluating image classification models for automated annotation.

Main Results:

  • The IML-support version of eyeNotate demonstrated improved efficiency and comparable annotation validity and reliability compared to the baseline.
  • Expert annotators perceived the IML-support features positively, aiding in the fixation-to-area mapping process.
  • Qualitative feedback highlighted the utility of machine learning suggestions in streamlining the annotation workflow.
  • Post hoc experiments confirmed the potential of image classification models for scalable data annotation.

Conclusions:

  • eyeNotate offers a significant advancement in mobile eye tracking data analysis, reducing manual effort.
  • The integration of few-shot learning models enhances annotation efficiency without compromising data quality.
  • The tool is a valuable asset for researchers in psychology and human-centered design, facilitating more efficient visual attention studies.