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Validation of an Eye-Tracking Algorithm Based on Smartphone Videos: A Pilot Study.

Wanzi Su1, Damon Hoad2, Leandro Pecchia3

  • 1School of Engineering, University of Warwick, Library Road, Coventry CV4 7AL, UK.

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|June 26, 2025
PubMed
Summary
This summary is machine-generated.

A new eye-tracking algorithm using smartphone cameras, Circular Hough Transform with Template Matching (CHT_TM), significantly speeds up analysis and improves accuracy. This advancement aids in tracking eye movement for early neurodegenerative disease detection.

Keywords:
biosignal processingeye movementeye-trackingneurodegenerative condition

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Ophthalmology

Background:

  • Smartphone cameras offer a portable platform for medical image analysis.
  • Efficient eye-tracking algorithms are crucial for diagnosing neurodegenerative diseases.
  • Existing algorithms may lack speed or accuracy for real-world applications.

Purpose of the Study:

  • To develop and validate an efficient eye-tracking algorithm using smartphone-captured images.
  • To compare the performance of two algorithms: CHT_ACM and CHT_TM.
  • To assess the algorithm's potential for early disease screening.

Main Methods:

  • Comparison of Circular Hough Transform with Active Contour Models (CHT_ACM) and Circular Hough Transform with Template Matching (CHT_TM).
  • Algorithm validation using smartphone images in the visible-light spectrum.
  • Analysis of eye movement under various conditions, including manual eyelid manipulation.

Main Results:

  • CHT_TM demonstrated a 79% reduction in execution time compared to CHT_ACM.
  • CHT_TM achieved improved accuracy on the x-axis with an average mean percentage error of 0.34% (x) and 0.95% (y).
  • Resource consumption showed minimal difference between the two algorithms.

Conclusions:

  • Template Matching significantly enhances the performance of the CHT_ACM eye-tracking algorithm.
  • The developed CHT_TM algorithm offers a viable tool for monitoring eye movement.
  • This technology holds promise for the early detection and diagnosis of neurodegenerative conditions.