untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:32:16Z</responseDate>
<request identifier=oai:HAL:hal-01098125v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-01098125v1</identifier>
<datestamp>2017-12-21</datestamp>
<setSpec>type:COMM</setSpec>
<setSpec>subject:scco</setSpec>
<setSpec>subject:info</setSpec>
<setSpec>subject:stat</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:BNRMI</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>On the Use of the Sigma-Lognormal Model to Study Children Handwriting</title>
<creator>Duval, Thérésa</creator>
<creator>Plamondon, Réjean</creator>
<creator>O'Reilly, Chrisitian</creator>
<creator>C., Remi</creator>
<creator>Vaillant, Jean</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Laboratoire scribens ; Ecole Polytechnique de Montréal (EPM)</contributor>
<description>Best paper for innovative research</description>
<description>International audience</description>
<source>Recent Progress in Graphonomics: Learn from the Past. IGS 2013</source>
<coverage>Nara, Japan</coverage>
<contributor>Prof. Masaki Nakagawa</contributor>
<contributor>Masaki Nakagawa, Marcus Liwicki and Bilan Zhu</contributor>
<identifier>hal-01098125</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01098125</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01098125</source>
<source>Masaki Nakagawa, Marcus Liwicki and Bilan Zhu. Recent Progress in Graphonomics: Learn from the Past. IGS 2013, Jun 2013, Nara, Japan. pp.26-30, 2013, Recent Progress in Graphonomics: Learn from the Past. IGS 2013 Proceedings</source>
<language>en</language>
<subject>[SCCO] Cognitive science</subject>
<subject>[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</subject>
<subject>[STAT] Statistics [stat]</subject>
<subject>[STAT.AP] Statistics [stat]/Applications [stat.AP]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>This paper investigates the interest of using the Kinematic Theory of rapid human movements to analyseand evaluate the handwriting produced by young kindergarten children in typical classroom environment. A total of66 children participated in this trial. For the preliminary results reported herein, movements from 15 children, takenevenly from three different levels (3, 4, and 5 years old), were analyzed. Our results confirm that 1) the sigmalognormalequation can model accurately childen’s movement and 2) that this modeling can differentiate betweenchildren of different school levels, hence supporting the relevance of pursuing along this original pathway.</description>
<date>2013-06-13</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
