Publication detail

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

GAVENČIAK, M. MUCHA, J. MEKYSKA, J. GALÁŽ, Z. ŠAFÁROVÁ, K. FAÚNDEZ ZANUY, M.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

Feature extraction; Graphomotor difficulties; Fractional order derivatives; Polar plot; Computer-aided diagnosis·; Machine learning

Authors

GAVENČIAK, M.; MUCHA, J.; MEKYSKA, J.; GALÁŽ, Z.; ŠAFÁROVÁ, K.; FAÚNDEZ ZANUY, M.

Released

27. 11. 2024

Publisher

Springer

ISBN

1866-9964

Periodical

Cognitive Computation

Year of study

13

Number

17

State

United States of America

Pages count

19

URL

https://link.springer.com/article/10.1007/s12559-024-10360-7

BibTex

@article{BUT193421,
  author="Michal {Gavenčiak} and Ján {Mucha} and Jiří {Mekyska} and Zoltán {Galáž} and Katarína {Šafárová} and Marcos {Faúndez Zanuy}",
  title="Computer-Aided Diagnosis of Graphomotor Difficulties Utilizing Direction-Based Fractional Order Derivatives",
  journal="Cognitive Computation",
  year="2024",
  volume="13",
  number="17",
  pages="19",
  doi="10.1007/s12559-024-10360-7

",
  issn="1866-9964",
  url="https://link.springer.com/article/10.1007/s12559-024-10360-7"
}