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dc.contributor.authorBENSEKKA, Chakib Arslane
dc.contributor.authorGUILLET, Christophe
dc.contributor.authorMERIENNE, Frédéric
dc.contributor.author
 hal.structure.identifier
POZZO, Thierry
178678 Cognition, Action, et Plasticité Sensorimotrice [Dijon - U1093] [CAPS]
dc.date.accessioned2018
dc.date.available2018
dc.date.issued2017
dc.date.submitted2018
dc.identifier.issn0966-6362
dc.identifier.urihttp://hdl.handle.net/10985/12777
dc.description.abstractThe motion capture systems are increasingly used for biomedical purposes. In order to recognize and classify the movements, however whole-body movements using passive markers, generate a huge amount of data. Can topological data analysis methods improve the recognition of movements? Can we use the results of this analysis combined with particular types of neural networks to anticipate the continuation of a movement?
dc.language.isoen
dc.publisherElsevier
dc.rightsPost-print
dc.subjectHuman movement analysis
dc.subjectAnticipation
dc.titleA topological approach for human movement classification and anticipation
ensam.embargo.terms2018-04-07
dc.identifier.doi10.1016/j.gaitpost.2017.06.387
dc.typdocCommunications avec actes
dc.localisationCentre de Châlons-en-Champagne
dc.localisationInstitut de Chalon sur Saône
dc.subject.halMathématique: Topologie géométrique
dc.subject.halInformatique: Intelligence artificielle
dc.subject.halInformatique: Synthèse d'image et réalité virtuelle
ensam.audienceInternationale
ensam.conference.titleESMAC 2017
ensam.conference.date2017-09-06
ensam.countryNorvège
ensam.title.proceedingGait & Posture
ensam.page229-230
ensam.volume57
ensam.cityTrondheim
ensam.peerReviewingOui
ensam.invitedCommunicationNon
ensam.proceedingOui
hal.identifierhal-01588554
hal.version1


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