Detection of dyslexia using eye tracking measures

Abstract

Dyslexia is one of the most common and hidden learning disabilities found in people, especially in the young age. It particularly affects reading, where the impaired reader takes a longer time to read and grasp the concept than the non-impaired reader. This further leads to academic failures. So studies to detect such issues have been conducted considering various factors like the reading times, fixation times, number of saccades(sudden movements in the eye), of both the impaired and non-impaired subjects, and give the best possible results. Thus, we plan to use the same eye tracking technique supported with machine learning models to detect and classify the individuals with and without dyslexia. The factors considered during the study are font-size, typeface, frequency of words(fixation times of non-impaired readers are more if frequency of encountered words is less) and age(people with learning disorders tend to enhance their reading skills with age), etc.