Eye-Rubbing Detection Using a Smartwatch: A Feasibility Study Demonstrated High Accuracy With Machine Learning.

Abstract

In this work, we present a new machine learning method based on the transformer neural network to detect eye rubbing using a smartwatch in a real-life setting. In ophthalmology, the accurate detection and prevention of eye rubbing could reduce incidence and progression of ectasic disorders, such as keratoconus, and to prevent blindness. Our approach leverages the state-of-the-art capabilities of the transformer network, widely recognized for its success in the field of natural language processing (NLP). We evaluate our method against several baselines using a newly collected dataset, which consist of data from smartwatch sensors associated with various hand-face interactions. The current algorithm achieves an eye-rubbing detection accuracy greater than 80% with minimal (20minutes) and up to 97% with moderate (3hours) user-specific fine-tuning. This research contributes to advancing eye-rubbing detection and establishes the groundwork for further studies in hand-face interactions monitoring using smartwatches. This experiment is a proof-of-concept that eye-rubbing detection is effectively detectable and distinguishable from other similar hand gestures, solely through a wrist-worn device and could lead to further studies and patient education in keratoconus management.