Detail publikace

Identification and Rating of Developmental Dysgraphia by Handwriting Analysis

Originální název

Identification and Rating of Developmental Dysgraphia by Handwriting Analysis

Anglický název

Identification and Rating of Developmental Dysgraphia by Handwriting Analysis

Jazyk

en

Originální abstrakt

Developmental dysgraphia, being observed among 10–30% of school-aged children, is a disturbance or difficulty in the production of written language that has to do with the mechanics of writing. The objective of this study is to propose a method that can be used for automated diagnosis of this disorder, as well as for estimation of difficulty level as determined by the handwriting proficiency screening questionnaire. We used a digitizing tablet to acquire handwriting and consequently employed a complex parameterization in order to quantify its kinematic aspects and hidden complexities. We also introduced a simple intrawriter normalization that increased dysgraphia discrimination and HPSQ estimation accuracies. Using a random forest classifier, we reached 96% sensitivity and specificity, while in the case of automated rating by the HPSQ total score, we reached 10% estimation error. This study proves that digital parameterization of pressure and altitude/tilt patterns in children with dysgraphia can be used for preliminary diagnosis of this writing disorder.

Anglický abstrakt

Developmental dysgraphia, being observed among 10–30% of school-aged children, is a disturbance or difficulty in the production of written language that has to do with the mechanics of writing. The objective of this study is to propose a method that can be used for automated diagnosis of this disorder, as well as for estimation of difficulty level as determined by the handwriting proficiency screening questionnaire. We used a digitizing tablet to acquire handwriting and consequently employed a complex parameterization in order to quantify its kinematic aspects and hidden complexities. We also introduced a simple intrawriter normalization that increased dysgraphia discrimination and HPSQ estimation accuracies. Using a random forest classifier, we reached 96% sensitivity and specificity, while in the case of automated rating by the HPSQ total score, we reached 10% estimation error. This study proves that digital parameterization of pressure and altitude/tilt patterns in children with dysgraphia can be used for preliminary diagnosis of this writing disorder.

BibTex


@article{BUT129346,
  author="Jiří {Mekyska} and Marcos {Faúndez Zanuy} and Zdeněk {Mžourek} and Zoltán {Galáž} and Zdeněk {Smékal} and Sara {Rosenblum}",
  title="Identification and Rating of Developmental Dysgraphia by Handwriting Analysis",
  annote="Developmental dysgraphia, being observed among 10–30% of school-aged children, is a disturbance or difficulty in the production of written language that has to do with the mechanics of writing. The objective of this study is to propose a method that can be used for automated diagnosis of this disorder, as well as for estimation of difficulty level as determined by the handwriting proficiency screening questionnaire. We used a digitizing tablet to acquire handwriting and consequently employed a complex parameterization in order to quantify its kinematic aspects and hidden complexities. We also introduced a simple intrawriter normalization that increased dysgraphia discrimination and HPSQ estimation accuracies. Using a random forest classifier, we reached 96% sensitivity and specificity, while in the case of automated rating by the HPSQ total score, we reached 10% estimation error. This study proves that digital parameterization of pressure and altitude/tilt patterns in children with dysgraphia can be used for preliminary diagnosis of this writing disorder.",
  address="IEEE Transactions on Human-Machine Systems",
  chapter="129346",
  doi="10.1109/THMS.2016.2586605",
  howpublished="print",
  institution="IEEE Transactions on Human-Machine Systems",
  number="99",
  volume="PP",
  year="2016",
  month="august",
  pages="1--14",
  publisher="IEEE Transactions on Human-Machine Systems",
  type="journal article"
}