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© 2023 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

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.

Details

Title
Handwriting Evaluation Using Deep Learning with SensoGrip
Author
Bublin, Mugdim 1 ; Werner, Franz 2   VIAFID ORCID Logo  ; Kerschbaumer, Andrea 2 ; Korak, Gernot 3 ; Geyer, Sebastian 3 ; Rettinger, Lena 2 ; Schönthaler, Erna 4 ; Schmid-Kietreiber, Matthias 1 

 Computer Science and Digital Communication, Department Technics, University of Applied Sciences, FH Campus Wien, 1100 Vienna, Austria; [email protected] 
 Health Assisting Engineering, Department Technics & Health Sciences, University of Applied Sciences, 2FH Campus Wien, 1100 Vienna, Austria; [email protected] (F.W.); [email protected] (A.K.); [email protected] (L.R.) 
 High Tech Manufacturing, Department Technics, University of Applied Sciences, FH Campus Wien, 1100 Vienna, Austria; [email protected] (G.K.); [email protected] (S.G.) 
 Occupational Therapy, Department Health Sciences, University of Applied Sciences, FH Campus Wien, 1100 Vienna, Austria; [email protected] 
First page
5215
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2824056960
Copyright
© 2023 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.