Developing a Dyslexia Indicator Using Eye Tracking

TL;DR

This study explores eye-tracking combined with machine learning as a non-invasive, cost-effective method for early dyslexia detection, achieving high accuracy and identifying severity levels across diverse populations.

Contribution

It introduces an innovative approach integrating eye-tracking and machine learning for dyslexia diagnosis, including severity assessment, with promising accuracy.

Findings

Achieved 88.58% accuracy in dyslexia detection.

Identified eye movement patterns associated with dyslexia.

Demonstrated effectiveness across diverse populations.

Abstract

Dyslexia, affecting an estimated 10% to 20% of the global population, significantly impairs learning capabilities, highlighting the need for innovative and accessible diagnostic methods. This paper investigates the effectiveness of eye-tracking technology combined with machine learning algorithms as a cost-effective alternative for early dyslexia detection. By analyzing general eye movement patterns, including prolonged fixation durations and erratic saccades, we proposed an enhanced solution for determining eye-tracking-based dyslexia features. A Random Forest Classifier was then employed to detect dyslexia, achieving an accuracy of 88.58\%. Additionally, hierarchical clustering methods were applied to identify varying severity levels of dyslexia. The analysis incorporates diverse methodologies across various populations and settings, demonstrating the potential of this technology to identify individuals with dyslexia, including those with borderline traits, through non-invasive means. Integrating eye-tracking with machine learning represents a significant advancement in the diagnostic process, offering a highly accurate and accessible method in clinical research.