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Related Experiment Video

Updated: Jan 9, 2026

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

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AI-Powered Detection of Eye Rubbing Behavior for Keratoconus Analysis Using Wearable Devices.

Binh Duong Giap, Jefferson Lustre, Joshua Ong

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

Summary

This summary is machine-generated.

Quantifying eye rubbing is crucial for understanding keratoconus progression. A new deep learning system using wearable sensors accurately detects eye rubbing, aiding in patient care.

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Area of Science:

Ophthalmology
Biomedical Engineering
Artificial Intelligence

Background:

Eye rubbing is a significant risk factor for keratoconus, a progressive corneal disease.
Current methods for assessing eye rubbing are subjective and insufficient for accurate quantification.
Keratoconus can lead to severe vision loss and necessitate corneal transplantation.

Purpose of the Study:

To develop and validate a deep learning framework for automatic, real-time detection of eye rubbing.
To provide a reliable tool for quantifying eye rubbing behavior in individuals at risk for keratoconus.

Main Methods:

Utilized sensor data from wrist-based wearable devices.
Developed a deep learning-based analysis framework for eye rubbing detection.
Validated the system using a dataset of 1,068 recordings from 9 subjects.

Main Results:

The framework achieved high accuracy in detecting eye rubbing behaviors during daily activities.
Achieved an average F1-score of 93.55% and an Area Under the Curve (AUC) of 96.23%.
Demonstrated effective identification of eye rubbing patterns.

Conclusions:

The developed deep learning system offers a novel and effective method for quantifying eye rubbing.
This tool can significantly aid ophthalmologists and researchers in managing keratoconus.
Provides objective, real-time data crucial for understanding keratoconus progression.