Clinically-Validated Innovative Mobile Application for Assessing Blinking and Eyelid Movements

TL;DR

This paper introduces Bapp, a validated mobile app that accurately assesses blinking and eyelid movements in real-time, offering a portable and reliable tool for ocular health monitoring and clinical use.

Contribution

The study presents a novel, clinically-validated mobile application using on-device AI for real-time eyelid movement analysis, improving accessibility and efficiency over traditional methods.

Findings

Achieved 98.4% precision in eyelid movement detection

Demonstrated 96.9% recall and 98.3% overall accuracy

Validated with 45 patient videos against expert annotations

Abstract

Blinking is a vital physiological process that protects and maintains the health of the ocular surface. Objective assessment of eyelid movements remains challenging due to the complexity, cost, and limited clinical applicability of existing tools. This study presents the Bapp (Blink Application), a mobile application developed using the Flutter framework and integrated with Google ML Kit for on-device, real-time analysis of eyelid movements, and its clinical validation. The validation was performed using 45 videos from patients, whose blinks were manually annotated by an ophthalmology specialist as the ground truth. The Bapp's performance was evaluated using standard metrics, with results demonstrating 98.4% precision, 96.9% recall, and an overall accuracy of 98.3%. These outcomes confirm the reliability of the Bapp as a portable, accessible, and objective tool for monitoring eyelid movements. The application offers a promising alternative to traditional manual blink counting, supporting continuous ocular health monitoring and postoperative evaluation in clinical environments.