Utilizing Gaze Self Similarity Plots to Recognize Dyslexia when Reading
Paweł Kasprowski · 2024
Dyslexia is a serious problem for society. Not-diagnosed dyslexia may lead to problems with comprehension and result in low self-esteem. Diagnosing dyslexia is difficult for adults as they develop different ways to mask their impairment. One of the possible ways to detect dyslexia is to analyze people’s eye movements while they are reading. Such methods typically convert raw eye movements to events - fixations and saccades - and then try to map fixations to words of the read text. Different statistics are used, like average fixations’ duration, number of refixations, and so on. This paper proposes a technique that diagnoses dyslexia based on raw eye movements without any preprocessing. This method is thus much more straightforward and does not require any parameters to be used to detect fixations and map them to words. Our method converts raw eye movement recordings to Gaze Self Similarity Plots and then trains the Convolutional Neural Network with these plots as input images. The method exhibits a state-of-the-art performance on a public dataset of adult Danish readers with accuracy equal to 89% and AUC equal to 0.93.