Dyslexia detection in children using eye tracking data based on VGG16 network

Ivan Vajs

Vanja Ković

Tamara Papić

Andrei Savić

Milica Janković

Considering the negative impact dyslexia has on school achievements, dyslexia diagnosis and treatment are found to be of great importance. In this paper, a deep convolutional neural network was developed to detect dyslexia in children ages 7-13, based on gathered eye tracking data. The children read a text written in Serbian on 13 different color configurations (including background and overlay color variations) and the raw gaze coordinates gathered during the trials were formatted into colored images and used to train a deep learning model based on the VGG16 architecture. Several configurations of the convolutional neural network were evaluated, as well as several trial segmentation configurations in order to provide the best overall result. The method was evaluated using subject-wise cross-validation and an accuracy of 87% was achieved. The obtained results show that a combination of convolutional neural network and visual encoding of the eye tracking data shows promising results in dyslexia detection with minimal preprocessing.

This publication uses Eye Tracking which is fully integrated into iMotions Lab

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