Automated dysgraphia detection by deep learning with SensoGrip Article Swipe

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Mugdim Bublin , Franz Werner , Andrea Kerschbaumer , Gernot Korak , Sebastian Geyer , Lena Rettinger , Erna Schoenthaler ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2210.07659

Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using 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 fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

Related Topics

Dysgraphia

Handwriting

Computer Science

Artificial Intelligence

Machine Learning

Deep Learning

Support Vector Machine

Engineering

Dyslexia

Mathematics Education

Concepts

Dysgraphia Handwriting Computer science Artificial intelligence Feature extraction Binary classification Grading (engineering) Machine learning Deep learning Overconfidence effect Writing assessment Speech recognition Pattern recognition (psychology) Psychology Support vector machine Engineering Dyslexia Mathematics education Law Political science Social psychology Civil engineering Reading (process)

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2210.07659
PDF: https://arxiv.org/pdf/2210.07659
OA Status: green
Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2210.07659

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Title: Automated dysgraphia detection by deep learning with SensoGrip

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-10-14

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Authors: Mugdim Bublin, Franz Werner, Andrea Kerschbaumer, Gernot Korak, Sebastian Geyer, Lena Rettinger, Erna Schoenthaler

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Landing page: https://arxiv.org/abs/2210.07659

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Concepts: Dysgraphia, Handwriting, Computer science, Artificial intelligence, Feature extraction, Binary classification, Grading (engineering), Machine learning, Deep learning, Overconfidence effect, Writing assessment, Speech recognition, Pattern recognition (psychology), Psychology, Support vector machine, Engineering, Dyslexia, Mathematics education, Law, Political science, Social psychology, Civil engineering, Reading (process)

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Cited by: 1

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abstract_inverted_index.automatic	107
abstract_inverted_index.classical	50
abstract_inverted_index.detection	20, 37
abstract_inverted_index.dynamics,	130
abstract_inverted_index.learning.	90
abstract_inverted_index.realistic	140
abstract_inverted_index.selection	59
abstract_inverted_index.SensoGrip,	121
abstract_inverted_index.algorithms	41, 53
abstract_inverted_index.children's	11
abstract_inverted_index.dysgraphia	22, 36, 66
abstract_inverted_index.evaluation	137
abstract_inverted_index.extraction	57, 112
abstract_inverted_index.predicting	81
abstract_inverted_index.scenarios.	141
abstract_inverted_index.selection.	114
abstract_inverted_index.wellbeing.	18
abstract_inverted_index.Dysgraphia,	0
abstract_inverted_index.disability,	4
abstract_inverted_index.dysgraphia.	69
abstract_inverted_index.handwriting	2, 78, 129
abstract_inverted_index.Furthermore,	115
abstract_inverted_index.capabilities	79
abstract_inverted_index.investigated	35, 74
abstract_inverted_index.intervention.	31
abstract_inverted_index.classification:	64
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	7
sustainable_development_goals[0].id	https://metadata.un.org/sdg/4
sustainable_development_goals[0].score	0.8999999761581421
sustainable_development_goals[0].display_name	Quality Education
citation_normalized_percentile