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Assessment of Developmental Dysgraphia Utilising a Display Tablet

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Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition (IGS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14285))

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Abstract

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.

This study was supported by a project of the Technology Agency of the Czech Republic no. TL03000287 (Software for advanced diagnosis of graphomotor disabilities) and by Spanish grant of the Ministerio de Ciencia e Innovación no. PID2020-113242RB-I00.

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Author information

Authors and Affiliations

  1. Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic

    Jiri Mekyska, Zoltan Galaz, Vojtech Zvoncak, Ján Mucha, Michal Gavenciak & Zdenek Smekal

  2. Department of Research Methodology, Institute of Psychology, The Czech Academy of Sciences, Brno, Czech Republic

    Katarina Safarova, Lukas Cunek, Tomas Urbanek, Jana Marie Havigerova & Jirina Bednarova

  3. Tecnocampus, Universitat Pompeu Fabra, Mataro, Barcelona, Spain

    Marcos Faundez-Zanuy

Authors
  1. Jiri Mekyska
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  2. Zoltan Galaz
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  3. Katarina Safarova
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  4. Vojtech Zvoncak
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  5. Lukas Cunek
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  7. Jana Marie Havigerova
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  8. Jirina Bednarova
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  9. Ján Mucha
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  10. Michal Gavenciak
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  11. Zdenek Smekal
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  12. Marcos Faundez-Zanuy
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Correspondence to Jiri Mekyska .

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Editors and Affiliations

  1. University of Salerno, Fisciano, Italy

    Antonio Parziale

  2. Universidad de las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Moises Diaz

  3. University of Lisbon, Lisbon, Portugal

    Filipe Melo

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Mekyska, J. et al. (2023). Assessment of Developmental Dysgraphia Utilising a Display Tablet. In: Parziale, A., Diaz, M., Melo, F. (eds) Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition. IGS 2023. Lecture Notes in Computer Science, vol 14285. Springer, Cham. https://doi.org/10.1007/978-3-031-45461-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-45461-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45460-8

  • Online ISBN: 978-3-031-45461-5

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