Prolonged computer usage is a common cause of eye strain, leading to discomfort and potential long-term visual issues. This thesis introduces EyeVee, an innovative computer software designed to mitigate eye strain through an intelligent eye blink reminder system. Leveraging computer vision technology, EyeVee monitors user eye behaviour in real-time to detect instances of reduced blink frequency, a key contributor to eye strain.
The software employs a non-intrusive, user-friendly interface that discreetly tracks and analyses blink patterns during computer use. Through machine learning algorithms, EyeVee adapts to individual blink rates, providing personalized reminders to encourage regular blinking and prevent eye strain and dry eyes.
In addition to its preventive capabilities, EyeVee offers features such as user analytics, and customizable settings to enhance user experience. The software is compatible with major operating systems and seamlessly integrates into daily computer routines.
This research contributes to the growing field of digital health by addressing a prevalent concern in our technology-driven society. EyeVee represents a practical solution to alleviate computer-induced eye strain, promoting eye health and overall well-being in the digital era.
Welcome to the repository for my final year project at UiTM, EyeVee. This software is designed to prevent eye strain by monitoring the user's blink rate per minute and providing timely notifications through Windows notifications. EyeVee is developed using Python and leverages libraries such as OpenCV, CV2, and Matplotlib.
Key features of EyeVee include:
Eye blink reminder system 20-20-20 Rule Monitor Settings Blink rate statistics (daily, weekly, monthly, yearly) This repository is dedicated solely to the EyeVee project. Feel free to explore the code to understand how the application works and how it contributes to promoting eye health in the digital age.
--Please note that the function for generating yearly blink rate statistics is still unfinished--
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