Literature DB >> 24139714

Signal-to-noise velocity peaks difference: a new method for evaluating the handwriting movement fluency in children with dysgraphia.

Jérémy Danna1, Vietminh Paz-Villagrán, Jean-Luc Velay.   

Abstract

This study evaluated handwriting movement dysfluency related to dysgraphia. A new variable, the Signal-to-Noise velocity peaks difference (SNvpd), was proposed to describe abnormal velocity fluctuations in cursive handwriting. This variable was compared to two variables most frequently used variables for assessing handwriting fluency. This comparison was carried out for three different groups, children with dysgraphia, proficient children, and adults, all of whom wrote the same single word. The adults were taken as the reference. Results revealed that, of the three variables studied, the SNvpd proved most efficient in discriminating children with dysgraphia, and that furthermore, it had the significant advantage of facilitating the localization of dysfluency peaks within a word. Our results also showed that the movement dysfluency of children with dysgraphia was specific to certain letters. In light of these results, we discuss the methodological and theoretical relevance of this new variable to the analysis of handwriting movement with the aim of characterizing dysgraphia.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Diagnosis; Dysgraphia; Handwriting; Learning disabilities; Movement fluency

Mesh:

Year:  2013        PMID: 24139714     DOI: 10.1016/j.ridd.2013.09.012

Source DB:  PubMed          Journal:  Res Dev Disabil        ISSN: 0891-4222


  7 in total

1.  "Let Me Hear Your Handwriting!" Evaluating the Movement Fluency from Its Sonification.

Authors:  Jérémy Danna; Vietminh Paz-Villagrán; Charles Gondre; Mitsuko Aramaki; Richard Kronland-Martinet; Sølvi Ystad; Jean-Luc Velay
Journal:  PLoS One       Date:  2015-06-17       Impact factor: 3.240

2.  On the Auditory-Proprioception Substitution Hypothesis: Movement Sonification in Two Deafferented Subjects Learning to Write New Characters.

Authors:  Jérémy Danna; Jean-Luc Velay
Journal:  Front Neurosci       Date:  2017-03-23       Impact factor: 4.677

3.  Handwriting movements for assessment of motor symptoms in schizophrenia spectrum disorders and bipolar disorder.

Authors:  Yasmina Crespo; Antonio Ibañez; María Felipa Soriano; Sergio Iglesias; Jose Ignacio Aznarte
Journal:  PLoS One       Date:  2019-03-14       Impact factor: 3.240

4.  Phenotyping features in the genesis of pre-scriptural gestures in children to assess handwriting developmental levels.

Authors:  Laurence Vaivre-Douret; Clémence Lopez; Audrey Dutruel; Sébastien Vaivre
Journal:  Sci Rep       Date:  2021-01-12       Impact factor: 4.379

5.  Dysgraphia detection through machine learning.

Authors:  Peter Drotár; Marek Dobeš
Journal:  Sci Rep       Date:  2020-12-09       Impact factor: 4.379

6.  "It Is Not the Robot Who Learns, It Is Me." Treating Severe Dysgraphia Using Child-Robot Interaction.

Authors:  Thomas Gargot; Thibault Asselborn; Ingrid Zammouri; Julie Brunelle; Wafa Johal; Pierre Dillenbourg; Dominique Archambault; Mohamed Chetouani; David Cohen; Salvatore M Anzalone
Journal:  Front Psychiatry       Date:  2021-02-23       Impact factor: 4.157

7.  Learning Handwriting: Factors Affecting Pen-Movement Fluency in Beginning Writers.

Authors:  Camilla L Fitjar; Vibeke Rønneberg; Guido Nottbusch; Mark Torrance
Journal:  Front Psychol       Date:  2021-05-20
  7 in total

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