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An Immersion Practicing and Analyzing System for Amblyopia Recovery using Artificial Intelligence and Machine Learning

Authors

Rory Zhang1 and Ang Li2, 1USA, 2California State University Long Beach, USA

Abstract

This paper presents the design and implementation of an interactive eye-training game system aimed at improving visual acuity in children with amblyopia. The system leverages Unity for game development, Firebase for real-time data storage, and C# for scripting core functionalities. It features three gamified therapeutic activities—Mole Game, Gun Game, and Cup Game—each designed to enhance specific aspects of visual coordination and tracking. A dynamic difficulty adjustment mechanism, powered by player performance metrics, ensures personalized and engaging gameplay. The program also incorporates a comprehensive score management system with real-time UI updates and progress tracking. To validate the system, an experimental study compared adaptive and static difficulty models, highlighting the effectiveness of dynamic scaling in maintaining engagement and accelerating therapeutic outcomes. This research demonstrates the potential of gamified solutions in modern amblyopia therapy, addressing traditional challenges of adherence and motivation.

Keywords

3D Modeling, Amblyopia, Machine Learning