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Identifying Biomarkers Using a Portable, Home-Based Eye-Tracking System to Predict Short-Term Visual Fatigue

Fan Song1,2, Guangyu Li3, Jian Zhang4

  • 1School of Optometry, Hong Kong Polytechnic University, Hong Kong, China (Hong Kong).

JMIR Human Factors
|April 28, 2026
PubMed
Summary
This summary is machine-generated.

This study developed an at-home system using ocular metrics to objectively predict eye fatigue. The EyeFatigue Tracker system shows potential for early detection and prevention of asthenopia.

Keywords:
asthenopiablinkeye movementeye trackerhome monitoringpredictionpupil dynamicsvisual task

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Computer Science

Background:

  • Screen-related eye fatigue (asthenopia) is a growing global health concern.
  • Current monitoring relies on subjective reports, highlighting the need for objective diagnostic tools.
  • Ocular metrics offer a quantifiable method to assess computer vision syndrome.

Purpose of the Study:

  • To develop and evaluate an integrated at-home system for predicting short-term asthenopia using objective ocular metrics.
  • The system aims to classify asthenopia risk levels for practical monitoring.
  • It automatically generates reports to complement symptom-based evaluations.

Main Methods:

  • Developed the EyeFatigue Tracker: a desktop app with a head-mounted device for eye video recording.
  • Utilized a deep learning model to extract ocular metrics from eye videos.
  • Employed machine learning classifiers (SVM, XGBoost, Random Forest) trained on participant data to predict asthenopia risk based on ocular metrics and CVS-Q scores.

Main Results:

  • 38 participants (aged 19-31) showed increased Computer Vision Syndrome Questionnaire (CVS-Q) scores post-task.
  • Nine predictive indicators were identified, including fissure length variability, blink duration, and pupil size variability.
  • The random forest model achieved high performance, with an accuracy of 0.720 and an AUC of 0.850.

Conclusions:

  • Objective ocular indicators can identify individuals at risk for asthenopia after computer use.
  • Machine learning models utilizing these ocular biomarkers demonstrate effective asthenopia detection.
  • The EyeFatigue Tracker system provides risk prediction and reports for early detection and preventive care.