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Acquisition of handwriting in children with and without dysgraphia: A computational approach
Handwriting is a complex skill to acquire and it requires years of training to be mastered. Children presenting dysgraphia exhibit difficulties automatizing their handwriting. This can bring anxiety and can negatively impact education. 280 children were recruited in schools and specialized clinics t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485885/ https://www.ncbi.nlm.nih.gov/pubmed/32915793 http://dx.doi.org/10.1371/journal.pone.0237575 |
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author | Gargot, Thomas Asselborn, Thibault Pellerin, Hugues Zammouri, Ingrid M. Anzalone, Salvatore Casteran, Laurence Johal, Wafa Dillenbourg, Pierre Cohen, David Jolly, Caroline |
author_facet | Gargot, Thomas Asselborn, Thibault Pellerin, Hugues Zammouri, Ingrid M. Anzalone, Salvatore Casteran, Laurence Johal, Wafa Dillenbourg, Pierre Cohen, David Jolly, Caroline |
author_sort | Gargot, Thomas |
collection | PubMed |
description | Handwriting is a complex skill to acquire and it requires years of training to be mastered. Children presenting dysgraphia exhibit difficulties automatizing their handwriting. This can bring anxiety and can negatively impact education. 280 children were recruited in schools and specialized clinics to perform the Concise Evaluation Scale for Children’s Handwriting (BHK) on digital tablets. Within this dataset, we identified children with dysgraphia. Twelve digital features describing handwriting through different aspects (static, kinematic, pressure and tilt) were extracted and used to create linear models to investigate handwriting acquisition throughout education. K-means clustering was performed to define a new classification of dysgraphia. Linear models show that three features only (two kinematic and one static) showed a significant association to predict change of handwriting quality in control children. Most kinematic and statics features interacted with age. Results suggest that children with dysgraphia do not simply differ from ones without dysgraphia by quantitative differences on the BHK scale but present a different development in terms of static, kinematic, pressure and tilt features. The K-means clustering yielded 3 clusters (Ci). Children in C1 presented mild dysgraphia usually not detected in schools whereas children in C2 and C3 exhibited severe dysgraphia. Notably, C2 contained individuals displaying abnormalities in term of kinematics and pressure whilst C3 regrouped children showing mainly tilt problems. The current results open new opportunities for automatic detection of children with dysgraphia in classroom. We also believe that the training of pressure and tilt may open new therapeutic opportunities through serious games. |
format | Online Article Text |
id | pubmed-7485885 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-74858852020-09-21 Acquisition of handwriting in children with and without dysgraphia: A computational approach Gargot, Thomas Asselborn, Thibault Pellerin, Hugues Zammouri, Ingrid M. Anzalone, Salvatore Casteran, Laurence Johal, Wafa Dillenbourg, Pierre Cohen, David Jolly, Caroline PLoS One Research Article Handwriting is a complex skill to acquire and it requires years of training to be mastered. Children presenting dysgraphia exhibit difficulties automatizing their handwriting. This can bring anxiety and can negatively impact education. 280 children were recruited in schools and specialized clinics to perform the Concise Evaluation Scale for Children’s Handwriting (BHK) on digital tablets. Within this dataset, we identified children with dysgraphia. Twelve digital features describing handwriting through different aspects (static, kinematic, pressure and tilt) were extracted and used to create linear models to investigate handwriting acquisition throughout education. K-means clustering was performed to define a new classification of dysgraphia. Linear models show that three features only (two kinematic and one static) showed a significant association to predict change of handwriting quality in control children. Most kinematic and statics features interacted with age. Results suggest that children with dysgraphia do not simply differ from ones without dysgraphia by quantitative differences on the BHK scale but present a different development in terms of static, kinematic, pressure and tilt features. The K-means clustering yielded 3 clusters (Ci). Children in C1 presented mild dysgraphia usually not detected in schools whereas children in C2 and C3 exhibited severe dysgraphia. Notably, C2 contained individuals displaying abnormalities in term of kinematics and pressure whilst C3 regrouped children showing mainly tilt problems. The current results open new opportunities for automatic detection of children with dysgraphia in classroom. We also believe that the training of pressure and tilt may open new therapeutic opportunities through serious games. Public Library of Science 2020-09-11 /pmc/articles/PMC7485885/ /pubmed/32915793 http://dx.doi.org/10.1371/journal.pone.0237575 Text en © 2020 Gargot et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gargot, Thomas Asselborn, Thibault Pellerin, Hugues Zammouri, Ingrid M. Anzalone, Salvatore Casteran, Laurence Johal, Wafa Dillenbourg, Pierre Cohen, David Jolly, Caroline Acquisition of handwriting in children with and without dysgraphia: A computational approach |
title | Acquisition of handwriting in children with and without dysgraphia: A computational approach |
title_full | Acquisition of handwriting in children with and without dysgraphia: A computational approach |
title_fullStr | Acquisition of handwriting in children with and without dysgraphia: A computational approach |
title_full_unstemmed | Acquisition of handwriting in children with and without dysgraphia: A computational approach |
title_short | Acquisition of handwriting in children with and without dysgraphia: A computational approach |
title_sort | acquisition of handwriting in children with and without dysgraphia: a computational approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485885/ https://www.ncbi.nlm.nih.gov/pubmed/32915793 http://dx.doi.org/10.1371/journal.pone.0237575 |
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