Eye Movement Feature Set and Predictive Model for Dyslexia: Feature Set and Predictive Model for Dyslexia

Jothi Prabha Appadurai and Bhargavi R.
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Jothi Prabha Appadurai: Vellore Institute of Technology, Chennai, India
Bhargavi R.: Vellore Institute of Technology, Chennai, India

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2021, vol. 15, issue 4, 1-22

Abstract: Dyslexia is a learning disorder that can cause difficulties in reading or writing. Dyslexia is not a visual problem but many dyslexics have impaired magnocellular system which causes poor eye control. Eye-trackers are used to track eye movements. This research work proposes a set of significant eye movement features that are used to build a predictive model for dyslexia. Fixation and saccade eye events are detected using the dispersion-threshold and velocity-threshold algorithms. Various machine learning models are experimented. Validation is done on 185 subjects using 10-fold cross-validation. Velocity based features gave high accuracy compared to statistical and dispersion features. Highest accuracy of 96% was achieved using the Hybrid Kernel Support Vector Machine- Particle Swarm Optimization model followed by the Xtreme Gradient Boosting model with an accuracy of 95%. The best set of features are the first fixation start time, average fixation saccade duration, the total number of fixations, total number of saccades and ratio between saccades and fixations.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:15:y:2021:i:4:p:1-22

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