A statistical procedure to assist dysgraphia detection through dynamic modelling of handwriting

TL;DR

This study develops a statistical method using dynamic handwriting modeling to detect dysgraphia in children, leveraging a large dataset of handwritten symbols to improve early diagnosis.

Contribution

It introduces a novel approach applying oscillatory models of handwriting dynamics to identify dysgraphia, enhancing diagnostic accuracy over traditional methods.

Findings

Model fit differences help distinguish dysgraphic handwriting

The classification procedure achieves promising detection accuracy

Dynamic modeling reveals key handwriting features associated with dysgraphia

Abstract

Dysgraphia is a neurodevelopmental condition in which children encounter difficulties in handwriting. Dysgraphia is not a disorder per se, but is secondary to neurodevelopmental disorders, mainly dyslexia, Developmental Coordination Disorder (DCD, also known as dyspraxia) or Attention Deficit Hyperactivity Disorder (ADHD). Since the mastering of handwriting is central for the further acquisition of other skills such as orthograph or syntax, an early diagnosis and handling of dysgraphia is thus essential for the academic success of children. In this paper, we investigated a large handwriting database composed of 36 individual symbols (26 isolated letters of the Latin alphabet written in cursive and the 10 digits) written by 545 children from 6,5 to 16 years old, among which 66 displayed dysgraphia (around 12\%). To better understand the dynamics of handwriting, mathematical models of nonpathological handwriting have been proposed, assuming oscillatory and fluid generation of strokes (Parsimonious Oscillatory Model of Handwriting [Andr\'e, 2014]). The purpose of this work is to study how such models behave when applied to children dysgraphic handwriting, and whether a lack of fit may help in the diagnosis, using a two-layer classification procedure with different compositions of classification algorithms.