Detection of Dyslexic Children Using Machine Learning

Detection of Dyslexic Children Using Machine Learning

Nandini N S ,  Bhoomika N

Computer Science and Engineering

BGS Institute of Technology, Adichunchanagiri University

Abstract
From a young age, dyslexic children require specialised instruction in spelling and word analysis. It's crucial to give these kids the right technological writing assistance so they can input material and receive numerous feedback at the same time. It's unclear, nevertheless, how dyslexic kids can effectively use a gaze-based virtual keyboard (VK). In this study, we suggest using a multimodal Hindi languageeye-gaze assisted learning system based on a VK to identify dyslexic youngsters and assist in the elimination of tracking errors for people with writing and reading impairments. Eyetracker, eyetracker with softswitch, and touchscreen as a baseline modality were used to evaluate performance using a pre-established copy-typing task. 32 children participated in a series of trials to validate the system (16 dyslexic and 16 control). The outcomes demonstrate how the system's usability and

burden are significantly

Index Terms: machine learning, eye tracking (ET), dyslexia, eye-gaze-assisted learning system, multimodal interaction and interfaces, virtual keyboard (VK).