Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage

Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art

Handwriting is a complex perceptual motor task that requires years of training and practice before complete mastery. Its acquisition is crucial, since handwriting is the basis, together with reading, of the acquisition of higher-level skills such as spelling, grammar, syntax, and text composition. Despite the correct learning and practice of handwriting, some children never master this skill to a sufficient level. These handwriting deficits, referred to as developmental dysgraphia, can seriously impact the acquisition of other skills and thus the academic success of the child if they are not diagnosed and handled early. In this review, we present a non-exhaustive listing of the tools that are the most reported in the literature for the analysis of handwriting and the diagnosis of dysgraphia. A variety of tools focusing on either the final handwriting product or the handwriting process are described here. On one hand, paper-and-pen tools are widely used throughout the world to assess handwriting quality and/or speed, but no universal gold-standard diagnostic test exists. On the other hand, several very promising computerized tools for the diagnosis of dysgraphia have been developed in the last decade, but some improvements are required before they can be available to clinicians. Based on these observations, we will discuss the pros and cons of the existing tools and the perspectives related to the development of a universal, standardized test of dysgraphia combining both paper-and-pen and computerized approaches and including different graphomotor and writing tasks.

Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application

Dysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent and not always easily accessible. This work presents a solution able to anticipate dysgraphia screening when handwriting has not been learned yet, in order to prevent negative consequences on the individuals’ academic and daily life. To quantitatively measure handwriting-related characteristics and monitor their evolution over time, we leveraged the Play-Draw-Write iPad application to collect data produced by children from the last year of kindergarten through the second year of elementary school. We developed a meta-model based on deep learning techniques (ensemble techniques and Quasi-SVM) which receives as input raw signals collected after a processing phase based on dimensionality reduction techniques (autoencoder and Time2Vec) and mathematical tools for high-level feature extraction (Procrustes Analysis). The final dysgraphia classifier can identify “at-risk” children with 84.62% Accuracy and 100% Precision more than two years earlier than current diagnostic techniques.