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Dynamic accommodation measurement using Purkinje reflections and machine learning.

Faik Ozan Ozhan1, Ugur Aygun1, Afsun Sahin2,3

  • 1Department of Electrical and Electronics Engineering, Koç University, 34450, Istanbul, Turkey.

Scientific Reports
|December 7, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a simple, real-time eye-tracking system using Purkinje reflections. The method accurately measures gaze and accommodation, crucial for diagnosing eye diseases and improving AR/VR displays.

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Area of Science:

  • Ophthalmology and Vision Science
  • Biomedical Engineering
  • Computer Vision

Background:

  • Accurate quantification of eye movement and focus (accommodation) is vital for diagnosing neurological and ocular conditions.
  • Current methods for dynamic gaze tracking and accommodation measurement can be complex or lack real-time capability.
  • Applications in augmented reality (AR) and virtual reality (VR) necessitate precise, real-time eye-tracking solutions.

Purpose of the Study:

  • To develop a simple, real-time system for dynamic gaze tracking and accommodation measurement.
  • To validate the system's accuracy using an eye model and experimental data.
  • To assess the efficacy of using Purkinje reflections with machine learning for precise eye movement prediction.

Main Methods:

  • A novel, simple setup was designed for real-time dynamic gaze tracking and accommodation measurement.
  • Purkinje reflections from the eye's optical surfaces (cornea and lens) were utilized as the basis for measurement.
  • An accurate eye model in ZEMAX simulated Purkinje reflection positions, which were validated against experimental data.
  • A neural network was trained to simultaneously predict vergence and accommodation using data from 9 subjects.

Main Results:

  • The ZEMAX eye model simulations closely matched the experimental data for Purkinje reflection positions.
  • The neural network, utilizing Purkinje reflection coordinates, achieved precise simultaneous prediction of vergence and accommodation.
  • Accommodation was predicted with high accuracy: better than 0.22 D (subject-specific) and 0.40 D (cross-subject) with two-point calibration.

Conclusions:

  • The developed system offers a simple yet effective method for real-time dynamic gaze tracking and accommodation measurement.
  • The integration of Purkinje reflection coordinates with machine learning provides a robust approach for precise eye-tracking estimations.
  • This technology holds significant potential for advancing diagnostic tools for ocular and neurological diseases and enhancing AR/VR display performance.