Počet záznamů: 1  

Assessment of Developmental Dysgraphia Utilising a Display Tablet

  1. 1.
    0576325 - PSÚ 2024 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
    Mekyska, J. - Galáž, Z. - Šafárová, Katarína - Zvončák, V. - Čunek, Lukáš - Urbánek, Tomáš - Havigerová, Jana Marie - Bednářová, Jiřina - Mucha, J. - Gavenčiak, M. - Smékal, Z. - Faundez-Zanuy, M.
    Assessment of Developmental Dysgraphia Utilising a Display Tablet.
    Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition. Vol. 14285. Cham: Springer, 2023 - (Parziale, A.; Diaz, M.; Melo, F.), s. 21-35. ISBN 978-3-031-45460-8.
    [International Conference of the International Graphonomics Society /21./. Évora (PT), 16.10.2023-19.10.2023]
    Grant CEP: GA TA ČR(CZ) TL03000287
    Institucionální podpora: RVO:68081740
    Klíčová slova: developmental dysgraphia * handwriting difficulties * handwriting proficiency * online handwriting * computerised assessment * machine learning * display tablet
    Obor OECD: Psychology (including human - machine relations)
    https://link.springer.com/book/10.1007/978-3-031-45461-5

    Even though the computerised assessment of developmental dysgraphia (DD) based on online handwriting processing has increasing popularity, most of the solutions are based on a setup, where a child writes on a paper fixed to a digitizing tablet that is connected to a computer. Although this approach enables the standard way of writing using an inking pen, it is difficult to be administered by children themselves. The main goal of this study is thus to explore, whether the quantitative analysis of online handwriting recorded via a display/screen tablet could sufficiently support the assessment of DD as well. For the purpose of this study, we enrolled 144 children (attending the 3rd and 4th class of a primary school), whose handwriting proficiency was assessed by a special education counsellor, and who assessed themselves by the Handwriting Proficiency Screening Questionnaires for Children (HPSQ–C). Using machine learning models based on a gradient-boosting algorithm, we were able to support the DD diagnosis with up to 83.6% accuracy. The HPSQ–C total score was estimated with a minimum error equal to 10.34%. Children with DD spent significantly higher time in-air, they had a higher number of pen elevations, a bigger height of on-surface strokes, a lower in-air tempo, and a higher variation in the angular velocity. Although this study shows a promising impact of DD assessment via display tablets, it also accents the fact that modelling of subjective scores is challenging and a complex and data-driven quantification of DD manifestations is needed.
    Trvalý link: https://hdl.handle.net/11104/0345914

     
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    0576325 M Šafárová et al_Assessment...pdf0805.8 KBAutorský preprintvyžádat
     
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