Design of a Mobile Tool for Quantitative Analysis of Visual Processing in Reading and Dyslexia Assessment Using Eye-Tracking.

This study presents the design of a mobile application for quantifying eye movements and assessing signs of dyslexia or reading comprehension deficits using eye-tracking technology. The application captures eye movement data every 0.5 seconds, filters blinks, and considers head position, utilizing image processing algorithms and machine learning techniques with MediaPipe for facial tracking. Tested on Android devices, the system accurately distinguishes typical from atypical eye movement patterns, providing valuable diagnostic insights. This tool offers an accessible and cost-effective solution for the preliminary diagnosis of dyslexia in primary school students, contributing to early detection and improved educational support. A future project will be aimed at validating this APP in primary school students, especially in countries with significant reading comprehension deficits.