Automated systems for diagnosis of dysgraphia in children: a survey and novel framework
<p dir="ltr">Learning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a c...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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| _version_ | 1864513543091519488 |
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| author | Jayakanth Kunhoth (14158908) |
| author2 | Somaya Al-Maadeed (5178131) Suchithra Kunhoth (5178125) Younes Akbari (16303286) Moutaz Saleh (14151402) |
| author2_role | author author author author |
| author_facet | Jayakanth Kunhoth (14158908) Somaya Al-Maadeed (5178131) Suchithra Kunhoth (5178125) Younes Akbari (16303286) Moutaz Saleh (14151402) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jayakanth Kunhoth (14158908) Somaya Al-Maadeed (5178131) Suchithra Kunhoth (5178125) Younes Akbari (16303286) Moutaz Saleh (14151402) |
| dc.date.none.fl_str_mv | 2024-04-15T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10032-024-00464-z |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Automated_systems_for_diagnosis_of_dysgraphia_in_children_a_survey_and_novel_framework/29713463 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Neurosciences Education Education systems Health sciences Health services and systems Information and computing sciences Artificial intelligence Dysgraphia diagnosis Handwriting disability Machine learning Automated systems |
| dc.title.none.fl_str_mv | Automated systems for diagnosis of dysgraphia in children: a survey and novel framework |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Learning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a causative factor for the difficulty in learning to write (dysgraphia), hindering the academic track of an individual. The signs and symptoms of dysgraphia include but are not limited to irregular handwriting, improper handling of writing medium, slow or labored writing, unusual hand position, etc. The widely accepted assessment criterion for all types of learning disabilities including dysgraphia has traditionally relied on examinations conducted by medical expert. However, in recent years, artificial intelligence has been employed to develop diagnostic systems for learning disabilities, utilizing diverse modalities of data, including handwriting analysis. This work presents a review of the existing automated dysgraphia diagnosis systems for children in the literature. The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. Apart from that, this article discusses some of the non-artificial intelligence-based automated systems. Furthermore, this article discusses the drawbacks of existing systems and proposes a novel framework for dysgraphia diagnosis and assistance evaluation.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal on Document Analysis and Recognition (IJDAR)<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10032-024-00464-z" target="_blank">https://dx.doi.org/10.1007/s10032-024-00464-z</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4c2f1ae195d215d0a38d0033c9ceffca |
| identifier_str_mv | 10.1007/s10032-024-00464-z |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29713463 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Automated systems for diagnosis of dysgraphia in children: a survey and novel frameworkJayakanth Kunhoth (14158908)Somaya Al-Maadeed (5178131)Suchithra Kunhoth (5178125)Younes Akbari (16303286)Moutaz Saleh (14151402)Biomedical and clinical sciencesNeurosciencesEducationEducation systemsHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceDysgraphia diagnosisHandwriting disabilityMachine learningAutomated systems<p dir="ltr">Learning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a causative factor for the difficulty in learning to write (dysgraphia), hindering the academic track of an individual. The signs and symptoms of dysgraphia include but are not limited to irregular handwriting, improper handling of writing medium, slow or labored writing, unusual hand position, etc. The widely accepted assessment criterion for all types of learning disabilities including dysgraphia has traditionally relied on examinations conducted by medical expert. However, in recent years, artificial intelligence has been employed to develop diagnostic systems for learning disabilities, utilizing diverse modalities of data, including handwriting analysis. This work presents a review of the existing automated dysgraphia diagnosis systems for children in the literature. The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. Apart from that, this article discusses some of the non-artificial intelligence-based automated systems. Furthermore, this article discusses the drawbacks of existing systems and proposes a novel framework for dysgraphia diagnosis and assistance evaluation.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal on Document Analysis and Recognition (IJDAR)<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10032-024-00464-z" target="_blank">https://dx.doi.org/10.1007/s10032-024-00464-z</a></p>2024-04-15T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10032-024-00464-zhttps://figshare.com/articles/journal_contribution/Automated_systems_for_diagnosis_of_dysgraphia_in_children_a_survey_and_novel_framework/29713463CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/297134632024-04-15T09:00:00Z |
| spellingShingle | Automated systems for diagnosis of dysgraphia in children: a survey and novel framework Jayakanth Kunhoth (14158908) Biomedical and clinical sciences Neurosciences Education Education systems Health sciences Health services and systems Information and computing sciences Artificial intelligence Dysgraphia diagnosis Handwriting disability Machine learning Automated systems |
| status_str | publishedVersion |
| title | Automated systems for diagnosis of dysgraphia in children: a survey and novel framework |
| title_full | Automated systems for diagnosis of dysgraphia in children: a survey and novel framework |
| title_fullStr | Automated systems for diagnosis of dysgraphia in children: a survey and novel framework |
| title_full_unstemmed | Automated systems for diagnosis of dysgraphia in children: a survey and novel framework |
| title_short | Automated systems for diagnosis of dysgraphia in children: a survey and novel framework |
| title_sort | Automated systems for diagnosis of dysgraphia in children: a survey and novel framework |
| topic | Biomedical and clinical sciences Neurosciences Education Education systems Health sciences Health services and systems Information and computing sciences Artificial intelligence Dysgraphia diagnosis Handwriting disability Machine learning Automated systems |