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AuthorKunhoth, Jayakanth
AuthorAl Maadeed, Somaya
AuthorSaleh, Moutaz
AuthorAkbari, Younus
Available date2023-11-26T12:53:54Z
Publication Date2022-01-01
Publication NameProceedings of the International Conference on Computer and Applications, ICCA 2022 - Proceedings
Identifierhttp://dx.doi.org/10.1109/ICCA56443.2022.10039584
CitationKunhoth, J., Al Maadeed, S., Saleh, M., & Akbari, Y. (2022, December). Machine learning methods for dysgraphia screening with online handwriting features. In 2022 International Conference on Computer and Applications (ICCA) (pp. 1-6). IEEE.‏
ISBN9781665452946
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149279037&origin=inward
URIhttp://hdl.handle.net/10576/49683
AbstractDysgraphia, a major learning disorder that primarily interferes with writing skills can hinder the academic track of children unless recognized in the early stage. The diversity in the symptoms, as well as the emergence in different ages, makes the diagnosis quite an intricate task. This work proposes automated methods that can be used for the diagnosis of dysgraphia by analyzing handwriting. Particularly this work examined the effectiveness of kinematics and dynamics of handwriting for discriminating abnormal writing. Furthermore, this work focused on developing methods that utilize fewer features for classifying dysgraphic and non-dysgraphic subjects. The proposed methods are evaluated in a publicly available online handwritten dataset. Obtained results indicate that the proposed method can diagnose the existence of dysgraphia with an accuracy of 77% with a limited number of features.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDysgraphia
Handwriting
Learning disabilities
Machine learning
Screening tool
TitleMachine Learning Methods for Dysgraphia Screening with Online Handwriting Features
TypeConference Paper
Pagination1-6
dc.accessType Abstract Only


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