Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia

Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective and dependent to the language besides stepping in...

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Autores principales:	Louis Devillaine, Raphaël Lambert, Jérôme Boutet, Saifeddine Aloui, Vincent Brault, Caroline Jolly, Etienne Labyt
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dysgraphia pre-diagnosis graphomotor machine-learning supervised drawings Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f1560fcdb82746fcb17db41692fc6788
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spelling	oai:doaj.org-article:f1560fcdb82746fcb17db41692fc67882021-11-11T19:03:38ZAnalysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia10.3390/s212170261424-8220https://doaj.org/article/f1560fcdb82746fcb17db41692fc67882021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7026https://doaj.org/toc/1424-8220Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective and dependent to the language besides stepping in late in the schooling. We propose a pre-diagnosis tool for dysgraphia using drawings called graphomotor tests. These tests are recorded using graphical tablets. We evaluate several machine-learning models and compare them to build this tool. A database comprising 305 children from the region of Grenoble, including 43 children with dysgraphia, has been established and diagnosed by specialists using the BHK test, which is the gold standard for the diagnosis of dysgraphia in France. We performed tests of classification by extracting, correcting and selecting features from the raw data collected with the tablets and achieved a maximum accuracy of 73% with cross-validation for three models. These promising results highlight the relevance of graphomotor tests to diagnose dysgraphia earlier and more broadly.Louis DevillaineRaphaël LambertJérôme BoutetSaifeddine AlouiVincent BraultCaroline JollyEtienne LabytMDPI AGarticledysgraphiapre-diagnosisgraphomotormachine-learningsuperviseddrawingsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7026, p 7026 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dysgraphia pre-diagnosis graphomotor machine-learning supervised drawings Chemical technology TP1-1185
spellingShingle	dysgraphia pre-diagnosis graphomotor machine-learning supervised drawings Chemical technology TP1-1185 Louis Devillaine Raphaël Lambert Jérôme Boutet Saifeddine Aloui Vincent Brault Caroline Jolly Etienne Labyt Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia
description	Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective and dependent to the language besides stepping in late in the schooling. We propose a pre-diagnosis tool for dysgraphia using drawings called graphomotor tests. These tests are recorded using graphical tablets. We evaluate several machine-learning models and compare them to build this tool. A database comprising 305 children from the region of Grenoble, including 43 children with dysgraphia, has been established and diagnosed by specialists using the BHK test, which is the gold standard for the diagnosis of dysgraphia in France. We performed tests of classification by extracting, correcting and selecting features from the raw data collected with the tablets and achieved a maximum accuracy of 73% with cross-validation for three models. These promising results highlight the relevance of graphomotor tests to diagnose dysgraphia earlier and more broadly.
format	article
author	Louis Devillaine Raphaël Lambert Jérôme Boutet Saifeddine Aloui Vincent Brault Caroline Jolly Etienne Labyt
author_facet	Louis Devillaine Raphaël Lambert Jérôme Boutet Saifeddine Aloui Vincent Brault Caroline Jolly Etienne Labyt
author_sort	Louis Devillaine
title	Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia
title_short	Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia
title_full	Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia
title_fullStr	Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia
title_full_unstemmed	Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia
title_sort	analysis of graphomotor tests with machine learning algorithms for an early and universal pre-diagnosis of dysgraphia
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f1560fcdb82746fcb17db41692fc6788
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Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia

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