Publication detail

Identification and Rating of Developmental Dysgraphia by Handwriting Analysis

MEKYSKA, J. FAÚNDEZ ZANUY, M. MŽOUREK, Z. GALÁŽ, Z. SMÉKAL, Z. ROSENBLUM, S.

Original Title

Identification and Rating of Developmental Dysgraphia by Handwriting Analysis

English Title

Identification and Rating of Developmental Dysgraphia by Handwriting Analysis

Type

journal article in Web of Science

Language

en

Original Abstract

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.

English abstract

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.

Keywords

rating, dysgraphia, handwriting analysis, handwriting proficiency screening questionnaire (HPSQ), intrawriter normalization

Released

03.08.2016

Publisher

IEEE Transactions on Human-Machine Systems

Pages from

1

Pages to

14

Pages count

14

URL

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 in Web of Science"
}