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A Comprehensive Framework for Eye Tracking: Methods, Tools, Applications, and Cross-Platform Evaluation.

Govind Ram Chhimpa1, Ajay Kumar2, Sunita Garhwal2

  • 1Department of Internet of Things and Intelligent Systems, Manipal University Jaipur, Jaipur 303007, Rajasthan, India.

Journal of Eye Movement Research
|October 28, 2025
PubMed
Summary
This summary is machine-generated.

This study explores modern eye tracking techniques, including video-oculography and deep learning, for enhanced gaze analysis. It highlights their application in HCI, healthcare, and VR, and discusses future directions for eye tracking technology.

Keywords:
electroencephalographyelectrooculographyeye trackingeye tracking performance parameterseye-tracking toolshuman–computer interactionscleral coilvideo oculography

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Eye tracking is essential for gaze analysis, measuring eye motion for applications in HCI, education, healthcare, and virtual reality.
  • Modern eye tracking has evolved significantly over the past two decades, incorporating advanced technologies.

Purpose of the Study:

  • To provide a comprehensive overview of eye-tracking concepts, terminology, performance parameters, and techniques.
  • To focus on contemporary and efficient eye-tracking methods, including VOG-based systems, deep learning for gaze estimation, and wearable devices.
  • To explore the integration of eye tracking with VR/AR and assistive technologies.

Main Methods:

  • Review of modern eye-tracking techniques, including video-oculography (VOG) and deep learning models for gaze estimation.
  • Analysis of wearable and cost-effective eye-tracking devices.
  • Examination of integration strategies with virtual/augmented reality and assistive technologies.
  • Leveraging machine learning for data insights and reduced manual calibration.

Main Results:

  • Contemporary eye-tracking methods significantly advance application development.
  • Machine learning enhances decision-making and minimizes calibration needs in eye tracking.
  • Diverse eye-tracking techniques are crucial for advancing various applications.

Conclusions:

  • Eye tracking is a vital tool with diverse applications, significantly enhanced by modern techniques.
  • Addressing limitations and challenges in eye-tracking methods is key to further development.
  • Future directions indicate continued evolution and enrichment of user experiences through advanced eye tracking.