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This study developed an AI tool using wearable sensors to accurately detect eye rubbing. The reliable detection system aids research into eye rubbing

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Eye rubbing is a significant factor in the development and progression of corneal ectasias like keratoconus.
  • Objective and reliable methods for quantifying eye rubbing are crucial for research and clinical practice.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-enabled tool for detecting eye rubbing using sensor data from wrist-based wearable devices.
  • To assess the performance of various deep learning models for accurate eye rubbing classification.

Main Methods:

  • An automated system was designed incorporating sensor data acquisition, preprocessing, and a deep learning classification model.
  • Six deep learning architectures, including 1D and 2D CNN-LSTM models and an ensemble, were evaluated.
  • Two datasets (time and frequency domains) comprising 8,640 time-series recordings and 112,320 scalogram images from 20 subjects were utilized.

Main Results:

  • The 1D CNN-LSTM model achieved high performance, with an F1-score of 95.27% and AUC of 98.26% during cross-validation.
  • On the testing set, the system maintained strong performance with an F1-score of 92.54% and AUC of 96.70%.
  • The model demonstrated real-time capabilities, requiring only 15.32 milliseconds for inference per segment.

Conclusions:

  • The AI-powered system reliably detects eye rubbing behaviors.
  • This tool has significant potential to assist ophthalmologists and researchers in studying the role of eye rubbing in keratoconus and other corneal diseases.