Towards a Robust and Accurate Screening Tool for Dyslexia with Data Augmentation using GANs

Abstract

Eye movements during text reading can provide insights about reading disorders. We developed the DysLexML, a screening tool for developmental dyslexia, based on various ML algorithms that analyze gaze points recorded via eye-tracking during silent reading of children. We comparatively evaluated its performance using measurements collected from two systematic field studies with 221 participants in total. This work presents DysLexML and its performance. It identifies the features with prominent predictive power and performs dimensionality reduction. Specifically, it achieves its best performance using linear SVM, with an accuracy of 97% and 84% respectively, using a small feature set. We show that DysLexML is also robust in the presence of noise. These encouraging results set the basis for developing screening tools in less controlled, larger-scale environments, with inexpensive eye-trackers, potentially reaching a larger population for early intervention. Unlike other related studies, DysLexML achieves the aforementioned performance by employing only a small number of selected features, that have been identified with prominent predictive power. Finally, we developed a new data augmentation/substitution technique based on GANs for generating synthetic data similar to the original distributions.