Dysgraphia detection through machine learning

Abstract Dysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early interv...

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Autores principales:	Peter Drotár, Marek Dobeš
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a28e9c0bd8c247c3bbec9d75d7c2e0be
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spelling	oai:doaj.org-article:a28e9c0bd8c247c3bbec9d75d7c2e0be2021-12-02T11:43:43ZDysgraphia detection through machine learning10.1038/s41598-020-78611-92045-2322https://doaj.org/article/a28e9c0bd8c247c3bbec9d75d7c2e0be2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78611-9https://doaj.org/toc/2045-2322Abstract Dysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.Peter DrotárMarek DobešNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peter Drotár Marek Dobeš Dysgraphia detection through machine learning
description	Abstract Dysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.
format	article
author	Peter Drotár Marek Dobeš
author_facet	Peter Drotár Marek Dobeš
author_sort	Peter Drotár
title	Dysgraphia detection through machine learning
title_short	Dysgraphia detection through machine learning
title_full	Dysgraphia detection through machine learning
title_fullStr	Dysgraphia detection through machine learning
title_full_unstemmed	Dysgraphia detection through machine learning
title_sort	dysgraphia detection through machine learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a28e9c0bd8c247c3bbec9d75d7c2e0be
work_keys_str_mv	AT peterdrotar dysgraphiadetectionthroughmachinelearning AT marekdobes dysgraphiadetectionthroughmachinelearning
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Dysgraphia detection through machine learning

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