Early and Automated Diagnosis of Dysgraphia using Machine learning Approach

Abstract

Dysgraphia is a handwriting problem that impairs a person’s ability to write. Even the diagnosis of this condition is challenging, and there is currently no cure. Researchers from all over the world have studied this issue and offered several solutions. Motivation to work on this problem did arise after meeting with a few students struggling in achieving performance despite putting in sincere efforts. This paper also discusses the various forms of dysgraphia and its associated symptoms and proposes machine-learning models to detect dysgraphia. Unsupervised machine learning techniques are used to detect dysgraphia-related handwriting impairment. In order to accomplish the goal, a fresh handwriting dataset is created by conducting handwriting exercises and a wide variety of features are extracted to represent various handwriting characteristics. Results indicate that random forest returns the best accuracy but scores less while detecting dysgraphic samples correctly. One class SVM has been tried to deal with the issue of the availability of dysgraphic samples required to train machines. Results indicate good hope in identification with a scope of improvement with increase by increase in sample size for machine training. This paper also seeks to raise awareness of the Dysgraphia issue and its effects on society. Keywords: Dysgraphia; One class SVM; Machine; Learning difficulties; OCC; Random Forest; Motor ability