Computer-Aided Diagnosis of Graphomotor Difficulties Utilizing Direction-Based Fractional Order Derivatives

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dc.contributor.authorGavenčiak, Michalcs
dc.contributor.authorMucha, Jáncs
dc.contributor.authorMekyska, Jiřícs
dc.contributor.authorGaláž, Zoltáncs
dc.contributor.authorŠafárová, Katarínacs
dc.contributor.authorFaúndez Zanuy, Marcoscs
dc.coverage.issue11cs
dc.coverage.volume17cs
dc.date.accessioned2025-04-04T11:56:31Z
dc.date.available2025-04-04T11:56:31Z
dc.date.issued2024-11-27cs
dc.description.abstractChildren who do not sufficiently develop graphomotor skills essential for handwriting often develop graphomotor disabilities (GD), impacting the self-esteem and academic performance of the individual. Current examination methods of GD consist of scales and questionaries, which lack objectivity, rely on the perceptual abilities of the examiner, and may lead to inadequately targeted remediation. Nowadays, one way to address the factor of subjectivity is to incorporate supportive machine learning (ML) based assessment. However, even with the increasing popularity of decision-support systems facilitating the diagnosis and assessment of GD, this field still lacks an understanding of deficient kinematics concerning the direction of pen movement. This study aims to explore the impact of movement direction on the manifestations of graphomotor difficulties in school-aged. We introduced a new fractional-order derivative-based approach enabling quantification of kinematic aspects of handwriting concerning the direction of movement using polar plot representation. We validated the novel features in a barrage of machine learning scenarios, testing various training methods based on extreme gradient boosting trees (XGBboost), Bayesian, and random search hyperparameter tuning methods. Results show that our novel features outperformed the baseline and provided a balanced accuracy of 87 % (sensitivity = 82 %, specificity = 92 %), performing binary classification (children with/without graphomotor difficulties). The final model peaked when using only 43 out of 250 novel features, showing that XGBoost can benefit from feature selection methods. Proposed features provide additional information to an automated classifier with the potential of human interpretability thanks to the possibility of easy visualization using polar plots.en
dc.formattextcs
dc.format.extent1-19cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationCognitive Computation. 2024, vol. 17, issue 11, p. 1-19.en
dc.identifier.doi10.1007/s12559-024-10360-7cs
dc.identifier.issn1866-9964cs
dc.identifier.orcid0000-0001-8835-1056cs
dc.identifier.orcid0000-0001-5126-440Xcs
dc.identifier.orcid0000-0002-6195-193Xcs
dc.identifier.orcid0000-0002-8978-351Xcs
dc.identifier.orcid0000-0003-0216-2057cs
dc.identifier.other193421cs
dc.identifier.researcheridT-9091-2019cs
dc.identifier.researcheridK-4001-2015cs
dc.identifier.researcheridT-8761-2019cs
dc.identifier.researcheridI-2403-2017cs
dc.identifier.scopus57201029686cs
dc.identifier.scopus35746344400cs
dc.identifier.scopus56888706700cs
dc.identifier.scopus57207731289cs
dc.identifier.urihttps://hdl.handle.net/11012/250748
dc.language.isoencs
dc.publisherSpringercs
dc.relation.ispartofCognitive Computationcs
dc.relation.urihttps://link.springer.com/article/10.1007/s12559-024-10360-7cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1866-9964/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectFeature extractionen
dc.subjectGraphomotor difficultiesen
dc.subjectFractional order derivativesen
dc.subjectPolar ploten
dc.subjectComputer-aided diagnosis·en
dc.subjectMachine learningen
dc.titleComputer-Aided Diagnosis of Graphomotor Difficulties Utilizing Direction-Based Fractional Order Derivativesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-193421en
sync.item.dbtypeVAVen
sync.item.insts2025.04.04 13:56:31en
sync.item.modts2025.04.03 13:32:13en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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