2020
Advanced Parametrization of Graphomotor Difficulties in School-Aged Children
GALÁŽ, Zoltán; Ján MUCHA; Vojtěch ZVONČÁK; Jiří MEKYSKA; Zdeněk SMÉKAL et al.Základní údaje
Originální název
Advanced Parametrization of Graphomotor Difficulties in School-Aged Children
Autoři
GALÁŽ, Zoltán; Ján MUCHA; Vojtěch ZVONČÁK; Jiří MEKYSKA; Zdeněk SMÉKAL; Katarína ŠAFÁROVÁ; Anežka ONDRÁČKOVÁ; Tomáš URBÁNEK; Jana Marie HAVIGEROVÁ; Jiřina BEDNÁŘOVÁ a Marcos FAUNDEZ-ZANUY
Vydání
IEEE Access, USA, IEEE Xplore Digital Library, 2020, 2169-3536
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
50102 Psychology, special
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 3.367
Označené pro přenos do RIV
Ano
Kód RIV
RIV/00216224:14210/20:00114245
Organizační jednotka
Filozofická fakulta
UT WoS
EID Scopus
Klíčová slova anglicky
advanced parametrization; computerized analysis; graphomotor difficulties; machine learning; online handwriting
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 29. 4. 2021 10:06, Mgr. Igor Hlaváč
Anotace
V originále
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 Czech Republic. Combining the newly proposed features with the conventionally used ones, we were able to identify GD with 84% accuracy. In this study, we showed that using advanced parametrization of basic graphomotor movements can be potentially used to improve our capabilities of quantifying problems with the development of legible, fast-paced handwriting, and help with the early diagnosis of handwriting difficulties frequently manifested in developmental dysgraphia.
Návaznosti
| GA18-16835S, projekt VaV |
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