Fractional Order Derivatives Evaluation in Computerized Assessment of Handwriting Difficulties in School-aged Children

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14210%2F19%3A00107994" target="_blank" >RIV/00216224:14210/19:00107994 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/ICUMT48472.2019.8970811" target="_blank" >http://dx.doi.org/10.1109/ICUMT48472.2019.8970811</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT48472.2019.8970811" target="_blank" >10.1109/ICUMT48472.2019.8970811</a>

Result language

angličtina

Original language name

Fractional Order Derivatives Evaluation in Computerized Assessment of Handwriting Difficulties in School-aged Children

Original language description

Handwriting difficulties (HD) affects some of the school-aged children and its current prevalence rate is between 5-34%. Children at primary schools have to face rising cognitive demands that the handwriting represents, and some of them are not able to do so. As a result, they tend to make mistakes and their written product is dysfluent and has poor legibility. HD can also lead them to lower self-esteem, learning difficulties and ultimately to less academic achievements. For this reason occupational therapists are trying to identify HD through examination as early as possible. We extracted online handwriting signals of children using digitizing tablets. Handwriting Proficiency Screening Questionnaire for Children (HPSQ-C) was used to score severity of HD in children's written product. To advance current computerized analysis of online handwriting, we employed fractional order derivatives features (FD) together with conventional features. We selected the significant features for HD identification and utilize correlation analysis together with Mann-Whitney U-test to evaluate their discrimination power. We can conclude that FD-based features bring benefits of more robust quantification of in-air movements as opposed to the conventionally used ones. Finally, we have shown that utilization of FD can be beneficial for computerized assessment of HD but should be further optimized and evaluated with advanced statistical or machine learning methods.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

50102 - Psychology, special (including therapy for learning, speech, hearing, visual and other physical and mental disabilities);

Project

<a href="/en/project/GA18-16835S" target="_blank" >GA18-16835S: Research of advanced developmental dysgraphia diagnosis and rating methods based on quantitative analysis of online handwriting and drawing</a><br>

Continuities

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

Publication year

2019

Confidentiality

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

Article name in the collection

11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

9781728157634

ISSN

2157-0221

e-ISSN

2157-023X

Number of pages

6

Pages from-to

210-215

Publisher name

IEEE

Place of publication

Dublin

Event location

Dublin

Event date

Jan 1, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000540651700027