Dyslexia affects between 5-17% of all school children, mak-ing it the most common learning disability. It has beenfound to severely affect learning ability in school subjectsas well as limit the choice of further education and occupa-tion. Since research has shown that early intervention andsupport can mitigate the negative effects of dyslexia, it iscrucial that the diagnosis of dyslexia is easily available andaimed at the right children. To make sure children whoare experiencing problems reading and potentially could bedyslectic are investigated for dyslexia an easy access, sys-tematic, and unbiased screening method would be helpful.This thesis therefore investigates the use of machine learn-ing methods to analyze eye movement patterns for dyslexiaclassification.The results showed that it was possible to separatedyslectic from non-dyslectic readers to 83% accuracy, us-ing non-sequential feature based machine learning methods.Equally good results for lower sample frequencies indicatedthat consumer grade eye trackers can be used for the pur-pose. Furthermore a sequential approach using RecurrentNeural Networks was also investigated, reaching an accu-racy of 78%. The thesis is intended to be an introduction to whatmethods could be viable for identifying dyslexia and as aninspiration for researchers aiming to do larger studies in thearea.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-191233 |
| Date | January 2016 |
| Creators | Lustig, Joakim |
| Publisher | KTH, Skolan för datavetenskap och kommunikation (CSC) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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