Detail publikace

Developmental Dysgraphia Diagnosis Based on Quantitative Analysis of Online Handwriting

Originální název

Developmental Dysgraphia Diagnosis Based on Quantitative Analysis of Online Handwriting

Anglický název

Developmental Dysgraphia Diagnosis Based on Quantitative Analysis of Online Handwriting

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@inproceedings{BUT147385,
  author="Vojtěch {Zvončák}",
  title="Developmental Dysgraphia Diagnosis Based on Quantitative Analysis of Online Handwriting",
  annote="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.",
  booktitle="Proceedings of the 24nd Conference STUDENT EEICT 2018",
  chapter="147385",
  howpublished="online",
  year="2018",
  month="april",
  pages="446--450",
  type="conference paper"
}