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Handwriting Evaluation Using Deep Learning with SensoGrip

Handwriting learning disabilities, such as dysgraphia, have a serious negative impact on children’s academic results, daily life and overall well-being. Early detection of dysgraphia facilitates an early start of targeted intervention. Several studies have investigated dysgraphia detection using mac...

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Detalles Bibliográficos
Autores principales: Bublin, Mugdim, Werner, Franz, Kerschbaumer, Andrea, Korak, Gernot, Geyer, Sebastian, Rettinger, Lena, Schönthaler, Erna, Schmid-Kietreiber, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255959/
https://www.ncbi.nlm.nih.gov/pubmed/37299942
http://dx.doi.org/10.3390/s23115215
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author Bublin, Mugdim
Werner, Franz
Kerschbaumer, Andrea
Korak, Gernot
Geyer, Sebastian
Rettinger, Lena
Schönthaler, Erna
Schmid-Kietreiber, Matthias
author_facet Bublin, Mugdim
Werner, Franz
Kerschbaumer, Andrea
Korak, Gernot
Geyer, Sebastian
Rettinger, Lena
Schönthaler, Erna
Schmid-Kietreiber, Matthias
author_sort Bublin, Mugdim
collection PubMed
description Handwriting learning disabilities, such as dysgraphia, have a serious negative impact on children’s academic results, daily life and overall well-being. Early detection of dysgraphia facilitates an early start of targeted intervention. Several studies have investigated dysgraphia detection using machine learning algorithms with a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated the fine grading of handwriting capabilities by predicting the SEMS score (between 0 and 12) with deep learning. Our approach provided a root-mean-square error of less than 1 with automatic instead of manual feature extraction and selection. Furthermore, the SensoGrip smart pen SensoGrip was used, i.e., a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.
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spelling pubmed-102559592023-06-10 Handwriting Evaluation Using Deep Learning with SensoGrip Bublin, Mugdim Werner, Franz Kerschbaumer, Andrea Korak, Gernot Geyer, Sebastian Rettinger, Lena Schönthaler, Erna Schmid-Kietreiber, Matthias Sensors (Basel) Article Handwriting learning disabilities, such as dysgraphia, have a serious negative impact on children’s academic results, daily life and overall well-being. Early detection of dysgraphia facilitates an early start of targeted intervention. Several studies have investigated dysgraphia detection using machine learning algorithms with a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated the fine grading of handwriting capabilities by predicting the SEMS score (between 0 and 12) with deep learning. Our approach provided a root-mean-square error of less than 1 with automatic instead of manual feature extraction and selection. Furthermore, the SensoGrip smart pen SensoGrip was used, i.e., a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios. MDPI 2023-05-31 /pmc/articles/PMC10255959/ /pubmed/37299942 http://dx.doi.org/10.3390/s23115215 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bublin, Mugdim
Werner, Franz
Kerschbaumer, Andrea
Korak, Gernot
Geyer, Sebastian
Rettinger, Lena
Schönthaler, Erna
Schmid-Kietreiber, Matthias
Handwriting Evaluation Using Deep Learning with SensoGrip
title Handwriting Evaluation Using Deep Learning with SensoGrip
title_full Handwriting Evaluation Using Deep Learning with SensoGrip
title_fullStr Handwriting Evaluation Using Deep Learning with SensoGrip
title_full_unstemmed Handwriting Evaluation Using Deep Learning with SensoGrip
title_short Handwriting Evaluation Using Deep Learning with SensoGrip
title_sort handwriting evaluation using deep learning with sensogrip
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255959/
https://www.ncbi.nlm.nih.gov/pubmed/37299942
http://dx.doi.org/10.3390/s23115215
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