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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10255959 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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