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A taxonomic overview and pilot study for evaluation of Augmented Reality based posture matching technique using Technology Acceptance Model

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dc.citedby2
dc.contributor.authorIqbal J.en_US
dc.contributor.authorSidhu M.S.en_US
dc.contributor.authorid57200394167en_US
dc.contributor.authorid56259597000en_US
dc.date.accessioned2023-05-29T07:27:52Z
dc.date.available2023-05-29T07:27:52Z
dc.date.issued2019
dc.descriptionArtificial intelligence; Augmented reality; Dance learning; Dance training systems; Hierarchical structures; Kinect; Learning paradigms; Learning technology; pose matching; Technology acceptance model; Learning systemsen_US
dc.description.abstractMotor skill training, posture matching and Augmented Reality (AR) based learning technology are considered to be the cornerstones of dance learning paradigm. In this paper, an AR based posture matching technique is presented based upon skeletal mapping and movement matching where each posture is modelled by a sequence of pivotal movements and continuous data frames. An extension of the previously published work by the authors, this paper aims to provide a taxonomic overview of dance learning/ training technologies with respect to existing learning theories. Furthermore, the proposed system is evaluated using the Technology Acceptance Model (TAM) and a pilot study is carried out to assess the acceptability of the system. The results of the pilot study are also utilized for identifying flaws and to calculate the sample size for further evaluation using a larger group of subjects. This research also aims at providing a taxonomical knowledge and categorization of the proposed Augmented Reality Dance Training System (ARDTS) in the state-of-art hierarchical structure of learning theories. � 2019 The Authors. Published by Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.procs.2019.12.117
dc.identifier.epage351
dc.identifier.scopus2-s2.0-85081165183
dc.identifier.spage345
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081165183&doi=10.1016%2fj.procs.2019.12.117&partnerID=40&md5=f67297f30e284f07044e5d2c5d4e8fa9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24849
dc.identifier.volume163
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleProcedia Computer Science
dc.titleA taxonomic overview and pilot study for evaluation of Augmented Reality based posture matching technique using Technology Acceptance Modelen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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