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Behavioral Activity Recognition Based on Gaze Ethograms.

Javier De Lope1, Manuel Graña2

  • 1Department of Artificial Intelligence, Universidad Politécnica de Madrid (UPM), Madrid, Spain.

International Journal of Neural Systems
|June 12, 2020
PubMed
Summary
This summary is machine-generated.

We developed an open-source gaze tracking system using laptop cameras to create gaze ethograms for user behavior analysis. This system accurately recognizes activities like reading, watching videos, and writing, offering a low-cost, noninvasive alternative.

Keywords:
Neuroethologyactivity recognitiongaze ethogramgaze trackingnoninvasive eye trackerscreen-based eye tracker

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Area of Science:

  • Human-Computer Interaction
  • Behavioral Science
  • Computer Vision

Background:

  • Noninvasive behavior observation enhances ecological validity in experiments.
  • Gaze ethograms, sequences of viewed screen regions, can model user behavior.
  • Existing commercial gaze trackers are often expensive and intrusive.

Purpose of the Study:

  • To propose and validate gaze ethograms for characterizing user behavioral activity during computer interaction.
  • To develop an open-source, low-cost gaze tracking system for behavioral research.
  • To enable recognition of specific user activities (reading, video viewing, writing) using gaze data.

Main Methods:

  • Developed an open-source gaze tracking system utilizing conventional laptop cameras.
  • Proposed texture-based eye features for robust eye tracking in low-quality images.
  • Employed gaze ethograms and classifiers to model and recognize user behavioral activities.

Main Results:

  • Successfully extracted gaze ethograms to discriminate between reading, video viewing, and writing activities.
  • Selected optimal classifier architectures for both gaze target prediction and ethogram classification.
  • Achieved encouraging results in user behavioral activity recognition on an in-house dataset.

Conclusions:

  • Gaze ethograms provide a viable method for noninvasive user behavior assessment.
  • The developed open-source system offers an accessible and effective tool for behavioral research.
  • The proposed texture-based features improve gaze tracking robustness in challenging conditions.