Asthenopia Eye Detection using Machine Learning Algorithm

• Published in: 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) pp. 430 - 436
• Main Authors: Nagajothi, S, Srinath, C, Shreeja, K, Thinesh Kumar, S
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.02.2022
• DOI: 10.1109/ICAIS53314.2022.9742879

The main goal is to develop a web and Android application for "Asthenopia eye detection" in a pandemic situation, where working from home and online classes are common. The use of mobile phones, tablets, laptops, and television is excessive these days. There is no enough time to care for our eyes, which can lead to asthenopia eye tiredness, dry and integrated eyes. Eye strain can be caused by spending too much time on technological devices. Blinking of eyes are less when looking at a screen of an electronic device, and the movement of the screen makes eyes work harder to focus. A normal adult eye blinks approximately 15-20 times per minute, yet individuals barely blink 3-8 times per minute while using computers, televisions, and other electronic devices. Distinctive properties of the user's eye are employed to recognize and track eye pictures with complicated backgrounds. Face detection, eye area detection, pupil detection, and eye tracking are the four processes of an eye-tracking and detection system.