Preventing Keratoconus through Eye Rubbing Activity Detection: A Machine Learning Approach

Keratoconus is a non-inflammatory disease of the eyes diagnosed in more than 1/2000 people, making it significantly common. Among others, eye rubbing has been identified as a risk factor for the development of keratoconus. The severity of the disease strongly depends on the frequency and force of eye rubbing. Vast research efforts have focused on diagnosing keratoconus through the application of artificial intelligence techniques over optical coherence tomography images and corneal measurements. However, to the best of the authors' knowledge, no studies have been conducted which provide an eye rubbing detection and alert mechanism for keratoconus prevention. This study intends to help close this research gap. An inertial measurement unit that is dedicated to collecting hand motion data and machine learning techniques are jointly employed for the early detection of potential problems and complications. Four conventional classification methods (support vector machines, decision trees, random forest, and XGBoost) were evaluated and compared. All methods attain high-quality accuracy results, with SVMs, RF, and XGBoost slightly outperforming DTs. As the results reveal, the performance of all methods is remarkable, allowing the integration of such a solution in wearable devices such as smartwatches to be considered for the early detection of eye rubbing and keratoconus prevention.

Automated summary

Keratoconus is a common non-inflammatory disease of the eyes that affects vision. Eye rubbing has been identified as a risk factor for its development. This study aims to use hand motion data and machine learning techniques to detect potential problems early and prevent complications.