Authors
Zoltan Galaz, Jan Mucha, Vojtech Zvoncak, Jiri Mekyska, Zdenek Smekal, Katarina Safarova, Anezka Ondrackova, Tomas Urbanek, Jana Marie Havigerova, Jirina Bednarova, Marcos Faundez-Zanuy
Publication date
2020/6/17
Journal
IEEE Access
Volume
8
Pages
112883-112897
Publisher
IEEE
Description
School-aged children spend 31–60% of their time at school performing handwriting, which is a complex perceptual-motor skill composed of a coordinated combination of fine graphomotor movements. As up to 30% of them experience graphomotor difficulties (GD), timely diagnosis of these difficulties and therapeutic intervention are of great importance. At present, an objective, computerized decision support system for the identification and assessment of GD in school-aged children is still missing. In this study, we propose three novel advanced handwriting parametrization techniques based on modulation spectra, fractional order derivatives, and tunable Q-factor wavelet transform to improve the identification of GD using online handwriting. For this purpose, we analyzed signals acquired from 7 basic graphomotor tasks performed by 53 children attending 3rd and 4th grade at several primary schools around the …
Total citations
20202021202220232024151295
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