Purpose: To develop and validate an artificial intelligence (AI)-enabled eye rubbing detection tool using sensor data collected from wrist-based wearable devices. Methods: An automated system was designed to detect eye rubbing using wrist-based wearable devices. The system involves 3 components: sensor data acquisition, data preprocessing, and deep learning–based classification model. Six different deep learning architectures were developed, including 1D and 2D CNN-LSTM models and an ensemble, to identify the most effective approach. Two datasets were established in the time and frequency domains: a timeseries dataset contains 8640 recordings and a scalogram dataset 15 comprising 112,320 images from 20 subjects. Results: The proposed system demonstrated strong performance, achieving an F1-score of 95.27 ± 0.87% and AUC of 98.26 ± 0.92% across 5 cross-validation folds when using the 1D CNN-LSTM model to distinguish eye rubbing and noneye rubbing activities. When evaluated on the testing set, the system maintained high performance, with an F1-score of 92.54% and AUC of 96.70%. Model inference required 15.32 milliseconds per segment, supporting real-time operation and practical deployments. Conclusions: The proposed system provides high reliability in detecting eye rubbing behaviors, indicating its potential as a tool to support ophthalmologists and researchers in the rigorous study of the contributions of eye rubbing to the development and progression of keratoconus and other corneal ectasias.
Giap et al. (Thu,) studied this question.