Developmental Dysgraphia Diagnosis Based on Quantitative Analysis of Online Handwriting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU127891" target="_blank" >RIV/00216305:26220/18:PU127891 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf" target="_blank" >http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Developmental Dysgraphia Diagnosis Based on Quantitative Analysis of Online Handwriting

Popis výsledku v původním jazyce

The prevalence of handwriting difficulties amongs chool-aged children is around 10–30%. Until now, there is no objective method to diagnose and rate developmental dysgraphia (DD) in Czech Republic. The goal of this study is to propose a new method of objective DD diagnosis based on quantitative analysis of online handwriting. For this purpose, we extracted a set of spatial, temporal, kinematic and dynamic features from three handwriting tasks. Consequently, we performed a correlation analysis between these features and score of handwriting proficiency screening questionaire (HPSQ), in order to identify parameters with a good discrimination power. Using random forests classifier in combination with quantification of alphabet writing task, we reached nearly 77% classification accuracy (75% sensitivity, 80% specificity). This pilot study proves the possibility of automatic DD diagnosis in children cohort writing with cursive letters.

Název v anglickém jazyce

Developmental Dysgraphia Diagnosis Based on Quantitative Analysis of Online Handwriting

Popis výsledku anglicky

The prevalence of handwriting difficulties amongs chool-aged children is around 10–30%. Until now, there is no objective method to diagnose and rate developmental dysgraphia (DD) in Czech Republic. The goal of this study is to propose a new method of objective DD diagnosis based on quantitative analysis of online handwriting. For this purpose, we extracted a set of spatial, temporal, kinematic and dynamic features from three handwriting tasks. Consequently, we performed a correlation analysis between these features and score of handwriting proficiency screening questionaire (HPSQ), in order to identify parameters with a good discrimination power. Using random forests classifier in combination with quantification of alphabet writing task, we reached nearly 77% classification accuracy (75% sensitivity, 80% specificity). This pilot study proves the possibility of automatic DD diagnosis in children cohort writing with cursive letters.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 24nd Conference STUDENT EEICT 2018

ISBN

978-80-214-5614-3

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

446-450

Název nakladatele

Neuveden

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

26. 4. 2018

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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