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.

翻译：眨眼是保护并维持眼表健康的关键生理过程。由于现有工具的复杂性、高昂成本及有限的临床适用性，眼睑运动的客观评估仍具挑战。本研究介绍了Bapp（Blink Application），这是一款基于Flutter框架开发、集成Google ML Kit以实现设备端实时眼睑运动分析的移动应用程序，并报告了其临床验证结果。验证采用45段患者视频进行，由眼科专家手动标注眨眼动作作为基准真值。Bapp的性能通过标准指标评估，结果显示其精确度为98.4%、召回率为96.9%、整体准确率达98.3%。这些结果证实了Bapp作为便携、易用且客观的眼睑运动监测工具的可靠性。该应用为传统人工眨眼计数提供了有前景的替代方案，可支持临床环境中的持续眼健康监测及术后评估。