Effect of Stroke-level Intra-writer Normalization on Computerized Assessment of Developmental Dysgraphia

Developmental dysgraphia (DD) can negatively influence writing speed, legibility of written product, and even planning and content generation. This could have a detrimental impact on self-expression, and communication in childhood. The goal of this study is to investigate new intra-writer and self-e...

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Published in2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) pp. 1 - 5
Main Authors Zvoncak, Vojtech, Mekyska, Jiri, Safarova, Katarina, Galaz, Zoltan, Mucha, Jan, Kiska, Tomas, Smekal, Zdenek, Losenicka, Barbora, Cechova, Barbora, Francova, Pavlina, Faundez-Zanuy, Marcos, Rosenblum, Sara
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2018
Subjects
Azimuth
developmental dysgraphia
digitizer
Estimation error
intra-writer normalization
kinematic analysis
Kinematics
Machine learning
online handwriting
Planning
Statistical analysis
Telecommunications
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Summary:Developmental dysgraphia (DD) can negatively influence writing speed, legibility of written product, and even planning and content generation. This could have a detrimental impact on self-expression, and communication in childhood. The goal of this study is to investigate new intra-writer and self-esteem normalization methods (IWN) in direction of improving computerized DD assessment based on the quantitative analysis of online handwriting. For this purpose we enrolled 97 children who wrote a paragraph using a digitizing tablet. Their handwriting proficiency was rated by the Handwriting Proficiency Screening Questionnaire (HPSQ). The handwriting was parametrized using a conventional set of features that were consequently normalized by four newly designed IWN. Based on the results we observed that a stroke-level £2 norm normalization decreased the computerized DD assessment error from 23 % to 18 %. This study proves that the stroke-level IWN has the significant potential in the field of computerized DD diagnosis and assessment.
ISSN:2157-023X
DOI:10.1109/ICUMT.2018.8631271