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dc.contributor.authorPehlivan, Alp Burak
dc.contributor.authorÖztop, Erhan
dc.date.accessioned2016-02-11T14:25:49Z
dc.date.available2016-02-11T14:25:49Z
dc.date.issued2015
dc.identifier.isbn978-147991762-4
dc.identifier.urihttp://hdl.handle.net/10679/2136
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7392424
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractDynamic Movement Primitives (DMPs)-originally a method for movement trajectory generation [1] has been also used for recognition tasks [2, 3]. However there has not been a systematic comparison between other recognition methods and DMPs using human movement data. This paper presents a comparison of commonly used Hidden Markov Model (HMM) based recognition with DMP based recognition using human generated letter trajectories. As the working principles of these two methods are very different, in addition to the performance, the numbers of adaptable parameters that are used in each method and, process time were compared. The results, indicate that HMM gives better results than DMP, with possible noise robustness advantage in DMPs for human movement.
dc.description.sponsorshipMinistry of Internal Affairs and Communications
dc.language.isoengen_US
dc.publisherIEEE
dc.relation.ispartofIndustrial Electronics Society, IECON 2015 - 41st Annual Conference of the IEEE
dc.rightsrestrictedAccess
dc.titleDynamic movement primitives for human movement recognitionen_US
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-3051-6038 & YÖK ID 45227) Öztop, Erhan
dc.contributor.ozuauthorÖztop, Erhan
dc.identifier.startpage002178
dc.identifier.endpage002183
dc.identifier.wosWOS:000382950702044
dc.identifier.doi10.1109/IECON.2015.7392424
dc.subject.keywordsDMP
dc.subject.keywordsHMM
dc.subject.keywordsRecognition
dc.subject.keywordsHuman movement
dc.subject.keywordsKinect
dc.subject.keywordsComparison
dc.identifier.scopusSCOPUS:2-s2.0-84973170739
dc.contributor.ozugradstudentPehlivan, Alp Burak
dc.contributor.authorMale2


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