Detection of Dyslexia using Eye Tracking Measures
Masooda Modak1, Ketan Ghotane2, Siddhanth V,3 Nachiket Kelkar4, Aravind Iyer5, Prachi G6
1Masooda Modak, Department of Computer Engineering SIES Graduate School Of Technology, Nerul, Navi, Mumbai, India.
2Ketan Ghotane, Department of Computer Engineering SIES Graduate School Of Technology, Nerul, Mumbai, India.
3Siddhanth V, Department of Computer Engineering SIES Graduate School Of Technology, Nerul, Mumbai, India.
4Nachiket Kelkar, Department of Computer Engineering SIES Graduate School Of Technology, Nerul, Mumbai, India.
5Aravind Iyer, Department of Computer Engineering SIES Graduate School Of Technology, Nerul, Mumbai, India.
6Prachi G, Department of Computer Engineering Sardar Patel Institute of Technology, Andheri, Mumbai, India.
Manuscript received on 09 August 2019 | Revised Manuscript received on 16 August 2019 | Manuscript Published on 31 August 2019 | PP: 58-61 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I10110789S219/19©BEIESP DOI: 10.35940/ijitee.I1011.0789S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Dyslexia, Eye Tracking, Eye Movements, Diagnosis, Detection, Prediction, Machine Learning, Support Vector Machine.
Scope of the Article: Recent Trends & Developments in Computer Networks