{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:09:13Z","timestamp":1764842953263,"version":"3.38.0"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002969","name":"Technologick\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["TL03000287"],"award-info":[{"award-number":["TL03000287"]}],"id":[{"id":"10.13039\/501100002969","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001824","name":"Grantov\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["GN23-06074O"],"award-info":[{"award-number":["GN23-06074O"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031478","name":"Next Generation EU","doi-asserted-by":"crossref","award":["X22NPO5107 (MEYS)"],"award-info":[{"award-number":["X22NPO5107 (MEYS)"]}],"id":[{"id":"10.13039\/100031478","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2020-113242RB-I00"],"award-info":[{"award-number":["PID2020-113242RB-I00"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Children who do not sufficiently develop graphomotor skills essential for handwriting often develop graphomotor disabilities\u00a0(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\u00a0(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\u00a0barrage of machine learning scenarios, testing various training methods based on extreme gradient boosting trees\u00a0(XGBboost), Bayesian, and random search hyperparameter tuning methods. Results show that our novel features outperformed the baseline and provided a\u00a0balanced accuracy of 87\u00a0% (sensitivity = 82\u00a0%, specificity = 92\u00a0%), 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.<\/jats:p>","DOI":"10.1007\/s12559-024-10360-7","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T06:30:50Z","timestamp":1732689050000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Computer-Aided Diagnosis of Graphomotor Difficulties Utilizing Direction-Based Fractional Order Derivatives"],"prefix":"10.1007","volume":"17","author":[{"given":"Michal","family":"Gavenciak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Mucha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiri","family":"Mekyska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zoltan","family":"Galaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katarina","family":"Zvoncakova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcos","family":"Faundez-Zanuy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"issue":"3","key":"10360_CR1","first-page":"317","volume":"50","author":"V Matijevi\u0107-Mikeli\u0107","year":"2011","unstructured":"Matijevi\u0107-Mikeli\u0107 V, Ko\u0161i\u010dek T, Crnkovi\u0107 M, Trifunovi\u0107-Ma\u010dek Z, Grazio S. Development of early graphomotor skills in children with neurodevelopmental risks. Acta Clinica Croatica. 2011;50(3):317\u201321.","journal-title":"Acta Clinica Croatica."},{"issue":"4","key":"10360_CR2","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1111\/j.1469-8749.2007.00312.x","volume":"49","author":"KP Feder","year":"2007","unstructured":"Feder KP, Majnemer A. Handwriting development, competency, and intervention. Developmental Medicine & Child Neurology. 2007;49(4):312\u20137.","journal-title":"Developmental Medicine & Child Neurology."},{"issue":"3","key":"10360_CR3","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1016\/j.ridd.2011.01.026","volume":"32","author":"A Kushki","year":"2011","unstructured":"Kushki A, Schwellnus H, Ilyas F, Chau T. Changes in kinetics and kinematics of handwriting during a prolonged writing task in children with and without dysgraphia. Research in developmental disabilities. 2011;32(3):1058\u201364.","journal-title":"Research in developmental disabilities."},{"issue":"4","key":"10360_CR4","doi-asserted-by":"publisher","first-page":"0196098","DOI":"10.1371\/journal.pone.0196098","volume":"13","author":"S Rosenblum","year":"2018","unstructured":"Rosenblum S. Inter-relationships between objective handwriting features and executive control among children with developmental dysgraphia. PloS one. 2018;13(4):0196098.","journal-title":"PloS one."},{"issue":"11","key":"10360_CR5","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1111\/ped.13004","volume":"58","author":"AA Alhusaini","year":"2016","unstructured":"Alhusaini AA, Melam GR, Buragadda S. Short-term sensorimotor-based intervention for handwriting performance in elementary school children. Pediatrics International. 2016;58(11):1118\u201323.","journal-title":"Pediatrics International."},{"issue":"1","key":"10360_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/19404158.2024.2326686","volume":"29","author":"J Mekyska","year":"2024","unstructured":"Mekyska J, Safarova K, Urbanek T, Bednarova J, Zvoncak V, Havigerova JM, Cunek L, Galaz Z, Mucha J, Klauszova C, et al. Graphomotor and handwriting disabilities rating scale (GHDRS): towards complex and objective assessment. Australian J Learn Difficulties. 2024;29(1):1\u201334. https:\/\/doi.org\/10.1080\/19404158.2024.2326686.","journal-title":"Australian J Learn Difficulties."},{"issue":"2","key":"10360_CR7","doi-asserted-by":"publisher","first-page":"623","DOI":"10.2466\/pms.2002.94.2.623","volume":"94","author":"R Karlsdottir","year":"2002","unstructured":"Karlsdottir R, Stefansson T. Problems in developing functional handwriting. Perceptual and motor skills. 2002;94(2):623\u201362.","journal-title":"Perceptual and motor skills."},{"issue":"5","key":"10360_CR8","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1542\/peds.2008-2098","volume":"123","author":"SK Katusic","year":"2009","unstructured":"Katusic SK, Colligan RC, Weaver AL, Barbaresi WJ. The forgotten learning disability: epidemiology of written-language disorder in a population-based birth cohort (1976\u20131982), Rochester. Minn Pediatr. 2009;123(5):1306\u201313.","journal-title":"Minn Pediatr."},{"issue":"10","key":"10360_CR9","doi-asserted-by":"publisher","first-page":"898","DOI":"10.5014\/ajot.46.10.898","volume":"46","author":"K McHale","year":"1992","unstructured":"McHale K, Cermak SA. Fine motor activities in elementary school: preliminary findings and provisional implications for children with fine motor problems. Am J Occup Ther. 1992;46(10):898\u2013903.","journal-title":"Am J Occup Ther."},{"issue":"3","key":"10360_CR10","doi-asserted-by":"publisher","first-page":"298","DOI":"10.5014\/ajot.62.3.298","volume":"62","author":"S Rosenblum","year":"2008","unstructured":"Rosenblum S. Development, reliability, and validity of the handwriting proficiency screening questionnaire (HPSQ). Am J Occup Ther. 2008;62(3):298\u2013307.","journal-title":"Am J Occup Ther."},{"issue":"3","key":"10360_CR11","doi-asserted-by":"publisher","first-page":"306","DOI":"10.3109\/01942638.2012.678971","volume":"32","author":"H Van Waelvelde","year":"2012","unstructured":"Van Waelvelde H, Hellinckx T, Peersman W, Smits-Engelsman BC. SOS: a screening instrument to identify children with handwriting impairments. Phys Occup Ther Pediatr. 2012;32(3):306\u201319.","journal-title":"Phys Occup Ther Pediatr"},{"key":"10360_CR12","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.ridd.2017.11.013","volume":"72","author":"AL Barnett","year":"2018","unstructured":"Barnett AL, Prunty M, Rosenblum S. Development of the handwriting legibility scale (HLS): a preliminary examination of reliability and validity. Res Dev Disabil. 2018;72:240\u20137.","journal-title":"Res Dev Disabil"},{"issue":"2","key":"10360_CR13","first-page":"114","volume":"1","author":"L Deschamps","year":"2021","unstructured":"Deschamps L, Devillaine L, Gaffet C, Lambert R, Aloui S, Boutet J, Brault V, Labyt E, Jolly C. Development of a pre-diagnosis tool based on machine learning algorithms on the BHK test to improve the diagnosis of dysgraphia. Adv Artif Intell Mach Learn. 2021;1(2):114\u201335.","journal-title":"Adv Artif Intell Mach Learn"},{"key":"10360_CR14","unstructured":"Kunhoth J, Al-Maadeed S, Kunhoth S, Akbari Y. Automated systems for diagnosis of dysgraphia in children: a survey and novel framework 2022."},{"key":"10360_CR15","doi-asserted-by":"crossref","unstructured":"Mekyska J, Faundez-Zanuy M, Mzourek Z, Galaz Z, Smekal Z, Rosenblum S. Identification and rating of developmental dysgraphia by handwriting analysis. IEEE Trans Human-Mach Syst. 2016;47(2):235\u201348.","DOI":"10.1109\/THMS.2016.2586605"},{"issue":"1","key":"10360_CR16","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1038\/s41746-018-0049-x","volume":"1","author":"T Asselborn","year":"2018","unstructured":"Asselborn T, Gargot T, Kidzi\u0144ski \u0141, Johal W, Cohen D, Jolly C, Dillenbourg P. Automated human-level diagnosis of dysgraphia using a consumer tablet. NPJ Digit Med. 2018;1(1):42.","journal-title":"NPJ Digit Med."},{"key":"10360_CR17","doi-asserted-by":"crossref","unstructured":"Mittal D, Yadav V, Sangwan A. Identification of dysgraphia: a comparative review. In: International Conference on Emerging Technologies in Computer Engineering. 2022. pp. 52\u201362. Springer.","DOI":"10.1007\/978-3-031-07012-9_5"},{"issue":"2","key":"10360_CR18","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/THMS.2016.2628799","volume":"47","author":"S Rosenblum","year":"2017","unstructured":"Rosenblum S, Dror G. Identifying developmental dysgraphia characteristics utilizing handwriting classification methods. IEEE Trans Human-Mach Syst. 2017;47(2):293\u20138.","journal-title":"IEEE Trans Human-Mach Syst."},{"issue":"1","key":"10360_CR19","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1038\/s41746-018-0049-x","volume":"1","author":"T Asselborn","year":"2018","unstructured":"Asselborn T, Gargot T, Kidzi\u0144ski \u0141, Johal W, Cohen D, Jolly C, Dillenbourg P. Automated human-level diagnosis of dysgraphia using a consumer tablet. Npj Digital Medicine. 2018;1(1):42.","journal-title":"Npj Digital Medicine."},{"key":"10360_CR20","doi-asserted-by":"crossref","unstructured":"Zvoncak V, Mekyska J, Safarova K, Galaz Z, Mucha J, Kiska T, Smekal Z, Losenicka B, Cechova B, Francova P, et al. Effect of stroke-level intra-writer normalization on computerized assessment of developmental dysgraphia. In: 2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). 2018. pp. 1\u201350.","DOI":"10.1109\/ICUMT.2018.8631271"},{"key":"10360_CR21","doi-asserted-by":"crossref","unstructured":"Zvoncak V, Mekyska J, Safarova K, Smekal Z, Brezany P. New approach of dysgraphic handwriting analysis based on the tunable Q-Factor wavelet transform. In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). 2019. pp. 289\u2013294.","DOI":"10.23919\/MIPRO.2019.8756872"},{"key":"10360_CR22","doi-asserted-by":"publisher","first-page":"112883","DOI":"10.1109\/ACCESS.2020.3003214","volume":"8","author":"Z Galaz","year":"2020","unstructured":"Galaz Z, Mucha J, Zvoncak V, Mekyska J, Smekal Z, Safarova K, Ondrackova A, Urbanek T, Havigerova JM, Bednarova J, Faundez-Zanuy M. Advanced parametrization of graphomotor difficulties in school-aged children. IEEE Access. 2020;8:112883\u201397.","journal-title":"IEEE Access."},{"key":"10360_CR23","doi-asserted-by":"crossref","unstructured":"Mekyska J, Galaz Z, Safarova K, Zvoncak V, Mucha J, Smekal Z, Ondrackova A, Urbanek T, Havigerova JM, Bednarova J, Faundez-Zanuy M. Computerised assessment of graphomotor difficulties in a cohort of school-aged children. In: 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). 2019. pp. 1\u20136.","DOI":"10.1109\/ICUMT48472.2019.8970767"},{"key":"10360_CR24","doi-asserted-by":"publisher","first-page":"218234","DOI":"10.1109\/ACCESS.2020.3042591","volume":"8","author":"J Mucha","year":"2020","unstructured":"Mucha J, Mekyska J, Galaz Z, Faundez-Zanuy M, Zvoncak V, Safarova K, Urbanek T, Havigerova JM, Bednarova J, Smekal Z. Analysis of various fractional order derivatives approaches in assessment of graphomotor difficulties. IEEE Access. 2020;8:218234\u201344.","journal-title":"IEEE Access."},{"issue":"1","key":"10360_CR25","doi-asserted-by":"publisher","first-page":"21541","DOI":"10.1038\/s41598-020-78611-9","volume":"10","author":"P Drot\u00e1r","year":"2020","unstructured":"Drot\u00e1r P, Dobe\u0161 M. Dysgraphia detection through machine learning. Scientific reports. 2020;10(1):21541.","journal-title":"Scientific reports."},{"key":"10360_CR26","doi-asserted-by":"crossref","unstructured":"Kunhoth J, Al\u00a0Maadeed S, Saleh M, Akbari Y. CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children. Expert Syst Appl. 2023;120740.","DOI":"10.1016\/j.eswa.2023.120740"},{"issue":"1","key":"10360_CR27","doi-asserted-by":"publisher","first-page":"21624","DOI":"10.1038\/s41598-022-26038-9","volume":"12","author":"LG Dui","year":"2022","unstructured":"Dui LG, Lomurno E, Lunardini F, Termine C, Campi A, Matteucci M, Ferrante S. Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage. Scientific Reports. 2022;12(1):21624.","journal-title":"Scientific Reports."},{"key":"10360_CR28","doi-asserted-by":"crossref","unstructured":"Vilasini V, Rekha BB, Sandeep V, Venkatesh VC. Deep learning techniques to detect learning disabilities among children using handwriting. In: 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). 2022. pp. 1710\u20131717. IEEE.","DOI":"10.1109\/ICICICT54557.2022.9917890"},{"key":"10360_CR29","doi-asserted-by":"crossref","unstructured":"Ghouse, F., Paranjothi, K., Vaithiyanathan, R.: Dysgraphia classification based on the non-discrimination regularization in rotational region convolutional neural network. Int J Intell Engineer Syst. 2022;15(1).","DOI":"10.22266\/ijies2022.0228.06"},{"issue":"1","key":"10360_CR30","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/0167-9457(89)90021-3","volume":"8","author":"K Newell","year":"1989","unstructured":"Newell K, Van Emmerik R. The acquisition of coordination: preliminary analysis of learning to write. Hum Mov Sci. 1989;8(1):17\u201332.","journal-title":"Hum Mov Sci."},{"issue":"1\u20132","key":"10360_CR31","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0001-6918(98)00031-6","volume":"100","author":"CE Van Den Heuvel","year":"1998","unstructured":"Van Den Heuvel CE, Galen GP, Teulings H-L, Gemmert AW. Axial pen force increases with processing demands in handwriting. Acta Psychol. 1998;100(1\u20132):145\u201359.","journal-title":"Acta Psychol."},{"key":"10360_CR32","doi-asserted-by":"crossref","unstructured":"Van Galen GP. Handwriting: issues for a psychomotor theory. Hum Mov Sci. 1991;10(2\u20133):165\u201391.","DOI":"10.1016\/0167-9457(91)90003-G"},{"issue":"9","key":"10360_CR33","doi-asserted-by":"publisher","first-page":"0237575","DOI":"10.1371\/journal.pone.0237575","volume":"15","author":"T Gargot","year":"2020","unstructured":"Gargot T, Asselborn T, Pellerin H, Zammouri I, Anzalone MS, Casteran L, Johal W, Dillenbourg P, Cohen D, Jolly C. Acquisition of handwriting in children with and without dysgraphia: a computational approach. Plos one. 2020;15(9):0237575.","journal-title":"Plos one."},{"issue":"1","key":"10360_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1006\/jvci.1993.1001","volume":"4","author":"J Blom","year":"1993","unstructured":"Blom J, Haar Romeny BM, Bel A, Koenderink JJ. Spatial derivatives and the propagation of noise in gaussian scale space. J Vis Commun Image Represent. 1993;4(1):1\u201313.","journal-title":"J Vis Commun Image Represent."},{"key":"10360_CR35","doi-asserted-by":"crossref","unstructured":"Mucha J, Faundez-Zanuy M, Mekyska J, Zvoncak V, Galaz Z, Kiska T, Smekal Z, Brabenec L, Rektorova I, Lopez-de-Ipina K. Analysis of Parkinson\u2019s disease dysgraphia based on optimized fractional order derivative features. In: 2019 27th European Signal Processing Conference (EUSIPCO). 2019. pp. 1\u20135.","DOI":"10.23919\/EUSIPCO.2019.8903088"},{"key":"10360_CR36","doi-asserted-by":"crossref","unstructured":"Zvoncak V, Mucha J, Galaz Z, Mekyska J, Safarova K, Faundez-Zanuy M, Smekal Z. Fractional order derivatives evaluation in computerized assessment of handwriting difficulties in school-aged children. In: 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). 2019. pp. 1\u20136.","DOI":"10.1109\/ICUMT48472.2019.8970811"},{"key":"10360_CR37","doi-asserted-by":"crossref","unstructured":"Mucha J, Galaz Z, Mekyska J, Faundez-Zanuy M, Zvoncak V, Smekal Z, Brabenec L, Rektorova I. Exploration of various fractional order derivatives in Parkinson\u2019s disease dysgraphia analysis. In: International Graphonomics Conference. 2022. pp. 308\u2013321. Springer.","DOI":"10.1007\/978-3-031-19745-1_23"},{"key":"10360_CR38","volume-title":"Fractional differential equations an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications","author":"I Podlubny","year":"1999","unstructured":"Podlubny I. Fractional differential equations an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications. San Diego: Academic Press; 1999."},{"key":"10360_CR39","unstructured":"Lazarevi\u0107 M. Further results on fractional order control of a mechatronic system. Scientific Technical Review, ISSN. 2013;206."},{"key":"10360_CR40","doi-asserted-by":"crossref","unstructured":"Uchaikin VV. Fractional derivatives for physicists and engineers vol. 2. Springer, ???. 2013.","DOI":"10.1007\/978-3-642-33911-0"},{"key":"10360_CR41","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.cnsns.2018.04.019","volume":"64","author":"H Sun","year":"2018","unstructured":"Sun H, Zhang Y, Baleanu D, Chen W, Chen Y. A new collection of real world applications of fractional calculus in science and engineering. Communications in Nonlinear Science and Numerical Simulation. 2018;64:213\u201331. https:\/\/doi.org\/10.1016\/j.cnsns.2018.04.019.","journal-title":"Communications in Nonlinear Science and Numerical Simulation."},{"issue":"1","key":"10360_CR42","doi-asserted-by":"publisher","first-page":"19","DOI":"10.7716\/aem.v9i1.1192","volume":"9","author":"A Persechino","year":"2020","unstructured":"Persechino A. An introduction to fractional calculus. Advanced Electromagnetics. 2020;9(1):19\u201330.","journal-title":"Advanced Electromagnetics."},{"key":"10360_CR43","doi-asserted-by":"crossref","unstructured":"Joshi M, Bhosale S, Vyawahare VA. A survey of fractional calculus applications in artificial neural networks. Artif Intell Rev. 2023;1\u201354.","DOI":"10.1007\/s10462-023-10474-8"},{"key":"10360_CR44","first-page":"82","volume":"22","author":"CJ Zu\u00f1iga-Aguilar","year":"2020","unstructured":"Zu\u00f1iga-Aguilar CJ, Gomez-Aguilar J, Franc S, Charpentier G, Doron M, Benhamou P, Romero-ugalde H. Blood glucose prediction with a fractional order neural network. Diabetes Technol Ther. 2020;22:82\u201382.","journal-title":"Diabetes Technol Ther."},{"key":"10360_CR45","doi-asserted-by":"publisher","first-page":"2474","DOI":"10.1109\/TNSRE.2022.3201197","volume":"30","author":"S Nagar","year":"2022","unstructured":"Nagar S, Kumar A. Orthogonal features based EEG signals denoising using fractional and compressed one-dimensional CNN autoencoder. IEEE Trans Neural Syst Rehabil Eng. 2022;30:2474\u201385.","journal-title":"IEEE Trans Neural Syst Rehabil Eng."},{"issue":"1","key":"10360_CR46","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1186\/s13662-017-1143-0","volume":"2017","author":"S Arshad","year":"2017","unstructured":"Arshad S, Baleanu D, Bu W, Tang Y. Effects of HIV infection on cd4+ t-cell population based on a fractional-order model. Adv Difference Equ. 2017;2017(1):92. https:\/\/doi.org\/10.1186\/s13662-017-1143-0.","journal-title":"Adv Difference Equ."},{"key":"10360_CR47","doi-asserted-by":"publisher","unstructured":"Pinto CMA, Machado JAT. Fractional model for malaria transmission under control strategies. Computers & Mathematics with Applications. 2013;66(5):908\u201316. https:\/\/doi.org\/10.1016\/j.camwa.2012.11.017. Fractional Differentiation and its Applications.","DOI":"10.1016\/j.camwa.2012.11.017"},{"key":"10360_CR48","doi-asserted-by":"publisher","unstructured":"Herrera-Alc\u00e1ntara O, Castel\u00e1n-Aguilar JR. Fractional gradient optimizers for PyTorch: enhancing GAN and BERT. Fractal and Fractional. 2023;7(7). https:\/\/doi.org\/10.3390\/fractalfract7070500.","DOI":"10.3390\/fractalfract7070500"},{"issue":"4","key":"10360_CR49","doi-asserted-by":"publisher","first-page":"3101","DOI":"10.1007\/s00521-022-07728-x","volume":"35","author":"G Altan","year":"2023","unstructured":"Altan G, Alkan S, Baleanu D. A novel fractional operator application for neural networks using proportional Caputo derivative. Neural Comput Appl. 2023;35(4):3101\u201314.","journal-title":"Neural Comput Appl."},{"issue":"3","key":"10360_CR50","doi-asserted-by":"publisher","first-page":"6903220030","DOI":"10.5014\/ajot.2015.014761","volume":"69","author":"S Rosenblum","year":"2015","unstructured":"Rosenblum S, Gafni-Lachter L. Handwriting proficiency screening questionnaire for children (HPSQ-C): development, reliability, and validity. The American Journal of Occupational Therapy. 2015;69(3):6903220030\u2013169032200309.","journal-title":"The American Journal of Occupational Therapy."},{"key":"10360_CR51","doi-asserted-by":"publisher","first-page":"2937","DOI":"10.3389\/fpsyg.2019.02937","volume":"10","author":"K \u0160af\u00e1rov\u00e1","year":"2020","unstructured":"\u0160af\u00e1rov\u00e1 K, Mekyska J, Zvon\u010d\u00e1k V, Gal\u00e1\u017e Z, Francov\u00e1 P, \u010cechov\u00e1 B, Losenick\u00e1 B, Sm\u00e9kal Z, Urb\u00e1nek T, Havigerov\u00e1 JM, et al. Psychometric properties of screening questionnaires for children with handwriting issues. Front Psychol. 2020;10:2937.","journal-title":"Front Psychol."},{"issue":"12","key":"10360_CR52","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1037\/0003-066X.57.12.1060","volume":"57","author":"AP Association","year":"2002","unstructured":"Association AP, et al. Ethical principles of psychologists and code of conduct. American Psychol. 2002;57(12):1060\u201373.","journal-title":"American Psychol."},{"key":"10360_CR53","doi-asserted-by":"crossref","unstructured":"Mucha J, Zvoncak V, Galaz Z, Faundez-Zanuy M, Mekyska J, Kiska T, Smekal Z, Brabenec L, Rektorova I, Lopez-de-Ipina K. Fractional derivatives of online handwriting: a new approach of Parkinsonic dysgraphia analysis. In: 2018 41st International Conference on Telecommunications and Signal Processing (TSP). 2018. pp. 214\u2013217. IEEE","DOI":"10.1109\/TSP.2018.8441293"},{"key":"10360_CR54","doi-asserted-by":"crossref","unstructured":"Mucha J, Mekyska J, Faundez-Zanuy M, Lopez-de-Ipina K, Zvoncak V, Galaz Z, Kiska T, Smekal Z, Brabenec L, Rektorova I. Advanced Parkinson\u2019s disease dysgraphia analysis based on fractional derivatives of online handwriting. In: 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). 2018. pp. 158\u2013165","DOI":"10.1109\/ICUMT.2018.8631265"},{"issue":"12","key":"10360_CR55","doi-asserted-by":"publisher","first-page":"2566","DOI":"10.3390\/app8122566","volume":"8","author":"J Mucha","year":"2018","unstructured":"Mucha J, Mekyska J, Galaz Z, Faundez-Zanuy M, Lopez-de-Ipina K, Zvoncak V, Kiska T, Smekal Z, Brabenec L, Rektorova I. Identification and monitoring of Parkinson\u2019s disease dysgraphia based on fractional-order derivatives of online handwriting. Appl Sci. 2018;8(12):2566.","journal-title":"Appl Sci."},{"key":"10360_CR56","doi-asserted-by":"publisher","unstructured":"Caputo M. Linear models of dissipation whose Q is almost frequency independent\u2014II. Geophys J Int. 1967;13(5):529\u201339. https:\/\/doi.org\/10.1111\/j.1365-246X.1967.tb02303.x. https:\/\/academic.oup.com\/gji\/article-pdf\/13\/5\/529\/1600098\/13-5-529.pdf.","DOI":"10.1111\/j.1365-246X.1967.tb02303.x"},{"issue":"2","key":"10360_CR57","first-page":"207","volume":"24","author":"Y Luchko","year":"1999","unstructured":"Luchko Y, Gorenflo R. An operational method for solving fractional differential equations with the Caputo derivatives. Acta Mathematica Vietnamica. 1999;24(2):207\u201333.","journal-title":"Acta Mathematica Vietnamica."},{"key":"10360_CR58","doi-asserted-by":"publisher","first-page":"575","DOI":"10.3758\/s13428-011-0159-8","volume":"44","author":"G Luria","year":"2012","unstructured":"Luria G, Rosenblum S. A computerized multidimensional measurement of mental workload via handwriting analysis. Behav Res Methods. 2012;44:575\u201386.","journal-title":"Behav Res Methods."},{"key":"10360_CR59","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. 2016. pp. 785\u2013794. ACM","DOI":"10.1145\/2939672.2939785"},{"issue":"6","key":"10360_CR60","doi-asserted-by":"publisher","first-page":"155013292211069","DOI":"10.1177\/15501329221106935","volume":"18","author":"P Zhang","year":"2022","unstructured":"Zhang P, Jia Y, Shang Y. Research and application of XGboost in imbalanced data. Int J Distrib Sensor Netw. 2022;18(6):15501329221106936.","journal-title":"Int J Distrib Sensor Netw."},{"key":"10360_CR61","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.neuroimage.2013.03.039","volume":"77","author":"MT Todd","year":"2013","unstructured":"Todd MT, Nystrom LE, Cohen JD. Confounds in multivariate pattern analysis: theory and rule representation case study. Neuroimage. 2013;77:157\u201365.","journal-title":"Neuroimage."},{"key":"10360_CR62","doi-asserted-by":"publisher","unstructured":"Rashmi KV, Gilad-Bachrach R. DART: dropouts meet multiple additive regression trees. arXiv 2015. https:\/\/doi.org\/10.48550\/ARXIV.1505.01866. arxiv:1505.01866","DOI":"10.48550\/ARXIV.1505.01866"},{"issue":"1","key":"10360_CR63","doi-asserted-by":"publisher","first-page":"5516","DOI":"10.1038\/s41598-017-05105-6","volume":"7","author":"E Pagliarini","year":"2017","unstructured":"Pagliarini E, Scocchia L, Vernice M, Zoppello M, Balottin U, Bouamama S, Guasti MT, Stucchi N. Children\u2019s first handwriting productions show a rhythmic structure. Scientific reports. 2017;7(1):5516.","journal-title":"Scientific reports."},{"key":"10360_CR64","doi-asserted-by":"publisher","unstructured":"Pagliarini E, Guasti MT, Toneatto C, Granocchio E, Riva F, Sarti D, Molteni B, Stucchi N. Dyslexic children fail to comply with the rhythmic constraints of handwriting. Hum Mov Sci. 2015;42. https:\/\/doi.org\/10.1016\/j.humov.2015.04.012.","DOI":"10.1016\/j.humov.2015.04.012"},{"key":"10360_CR65","doi-asserted-by":"publisher","unstructured":"Gavenciak M, Zvoncak V, Mekyska J, Safarova K, Cunek L, Urbanek T, Havigerova JM, Bednarova J, Galaz Z, Mucha J. Exploring the contribution of isochrony-based features to computerized assessment of handwriting disabilities. In: 2022 45th International Conference on Telecommunications and Signal Processing (TSP). 2022. pp. 355\u2013359. https:\/\/doi.org\/10.1109\/TSP55681.2022.9851254.","DOI":"10.1109\/TSP55681.2022.9851254"},{"key":"10360_CR66","unstructured":"Mucha J, Mekyska J, Zvoncak V, Galaz Z, Smekal Z. HandAQUS \u2013 handwriting acquisition software. GitHub 2022."},{"key":"10360_CR67","unstructured":"Galaz Z, Mucha J, Zvoncak V, Mekyska J. Handwriting features. GitHub 2022."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10360-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-024-10360-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-024-10360-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T07:33:44Z","timestamp":1740814424000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-024-10360-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":67,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10360"],"URL":"https:\/\/doi.org\/10.1007\/s12559-024-10360-7","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"type":"print","value":"1866-9956"},{"type":"electronic","value":"1866-9964"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"3 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was reviewed and approved by the Ethical Board of the Department of Psychology of the Masaryk University.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"13"}}