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<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd> <responseDate>2018-01-15T18:42:51Z</responseDate> <request identifier=oai:HAL:hal-00601476v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request> <GetRecord> <record> <header> <identifier>oai:HAL:hal-00601476v1</identifier> <datestamp>2017-12-21</datestamp> <setSpec>type:COMM</setSpec> <setSpec>subject:info</setSpec> <setSpec>collection:UNIV-AG</setSpec> <setSpec>collection:BNRMI</setSpec> </header> <metadata><dc> <publisher>HAL CCSD</publisher> <title lang=en>Behavior analysis through games using artificial neural networks</title> <creator>Puzenat, Didier</creator> <creator>Isabelle, Verlut</creator> <contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor> <description>International audience</description> <source>Proceeding of the Third International Conferences on Advances in Computer-Human Interactions</source> <source>Third International Conferences on Advances in Computer-Human Interactions</source> <coverage>St. Maarten, Netherlands Antilles</coverage> <identifier>hal-00601476</identifier> <identifier>https://hal.archives-ouvertes.fr/hal-00601476</identifier> <source>https://hal.archives-ouvertes.fr/hal-00601476</source> <source>Third International Conferences on Advances in Computer-Human Interactions, Feb 2010, St. Maarten, Netherlands Antilles. pp.134-138, 2010</source> <language>en</language> <subject lang=en>Cognitive science</subject> <subject lang=en>Psychology</subject> <subject lang=en>Human factors</subject> <subject lang=en>Neural network applications</subject> <subject lang=en>Games</subject> <subject lang=en>User interfaces</subject> <subject>[INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC]</subject> <subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject> <type>info:eu-repo/semantics/conferenceObject</type> <type>Conference papers</type> <description lang=en>This paper demonstrates that a human being using an interface can be efficiently evaluated - in real time - by embedding basic measurements in the interface and using a suitable trained artificial neural network. The approach is introduced through video games but is suitable for any machine capable of valuable measurements on user actions. Of course, the quality of the "diagnostic" depends of the learnability of the task and of the size and quality of the learning base. Typical applications include the detection of fatigue, stress, emotions, the influence of a drug or of medical treatments ; screening a deficit or adequateness to a task, etc. Two successful prototypes are presented, one to predict the mental age of children through a set of simple basic games, and the other to detect if a subject is right-handed of left-handed through a racing car simulation.</description> <date>2010-02-10</date> </dc> </metadata> </record> </GetRecord> </OAI-PMH>