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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU154754" target="_blank" >RIV/00216305:26220/24:PU154754 - isvavai.cz</a>

Alternative codes found

RIV/68081740:_____/25:00602015

Result on the web

<a href="https://link.springer.com/article/10.1007/s12559-024-10360-7" target="_blank" >https://link.springer.com/article/10.1007/s12559-024-10360-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12559-024-10360-7" target="_blank" >10.1007/s12559-024-10360-7</a>

Result language

angličtina

Original language name

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

Original language description

Children 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Cognitive Computation

ISSN

1866-9956

e-ISSN

1866-9964

Volume of the periodical

13

Issue of the periodical within the volume

17

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

„“-„“

UT code for WoS article

001365485300004

EID of the result in the Scopus database

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