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Glassware Calibration01:11

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Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
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Related Experiment Video

Updated: Nov 9, 2025

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
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Uncertainty visualization of gaze estimation to support operator-controlled calibration.

Almoctar Hassoumi1, Vsevolod Peysakhovich2, Christophe Hurter1

  • 1École Nationale de l'Aviation Civile , France.

Journal of Eye Movement Research
|April 8, 2021
PubMed
Summary

This study introduces novel visualization techniques to assess gaze estimation uncertainty. Our methods improve accuracy, reducing mean angular error in eye-tracking data for better qualitative evaluation.

Keywords:
accuracyaccuracy improvementeye movementeye trackinggaze estimationhead movementsmooth pursuituncertaintyusability

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Data Visualization

Background:

  • Eye tracking data is widely used, but its inherent uncertainty is often overlooked.
  • Visualizing gaze estimation uncertainty is crucial for reliable qualitative analysis.

Purpose of the Study:

  • To develop and evaluate visualization assets for assessing gaze estimation uncertainty.
  • To introduce innovative methods for uncertainty computation and visualization in eye-tracking.

Main Methods:

  • Developed a custom gaze data processing pipeline for detailed uncertainty computation.
  • Estimated gaze position in the world camera, tracking data transformations.
  • Designed an experiment with 12 participants to validate the pipeline and correction methods.

Main Results:

  • Proposed correction methods significantly reduced Mean Angular Error (MAE) by approximately 1.32 cm across participants.
  • Achieved a corrected MAE of 0.25° (SD=0.15°).
  • Introduced a user-centric uncertainty map for qualitative assessment.

Conclusions:

  • The developed visualization assets effectively support the qualitative evaluation of gaze estimation uncertainty.
  • The proposed methods enhance the accuracy and reliability of eye-tracking data.
  • Uncertainty visualization provides valuable insights into user point-of-view data.