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

Identifikátory výsledku

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

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

  • Popis výsledku v původním jazyce

    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.

  • Název v anglickém jazyce

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

  • Popis výsledku anglicky

    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.

Klasifikace

  • Druh

    D - Stať ve sborníku

  • CEP obor

  • OECD FORD obor

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

Návaznosti výsledku

Ostatní

  • Rok uplatnění

    2019

  • Kód důvěrnosti údajů

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

Údaje specifické pro druh výsledku

  • Název statě ve sborníku

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

  • ISBN

    9781728157634

  • ISSN

    2157-0221

  • e-ISSN

    2157-023X

  • Počet stran výsledku

    6

  • Strana od-do

    210-215

  • Název nakladatele

    IEEE

  • Místo vydání

    Dublin

  • Místo konání akce

    Dublin

  • Datum konání akce

    1. 1. 2019

  • Typ akce podle státní příslušnosti

    WRD - Celosvětová akce

  • Kód UT WoS článku

    000540651700027

Druh výsledku

D - Stať ve sborníku

D

OECD FORD

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

Rok uplatnění

2019