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dc.creatorManitsaris, S.en
dc.creatorGlushkova, A.en
dc.creatorBevilacqua, F.en
dc.creatorMoutarde, F.en
dc.date.accessioned2015-11-23T10:38:42Z
dc.date.available2015-11-23T10:38:42Z
dc.date.issued2014
dc.identifier10.1145/2627729
dc.identifier.issn15564673
dc.identifier.urihttp://hdl.handle.net/11615/30629
dc.description.abstractThis research has been conducted in the context of the ArtiMuse project that aims at the modeling and renewal of rare gestural knowledge and skills involved in the traditional craftsmanship and more precisely in the art of wheel-throwing pottery. These knowledge and skills constitute intangible cultural heritage and refer to the fruit of diverse expertise founded and propagated over the centuries thanks to the ingeniousness of the gesture and the creativity of the human spirit. Nowadays, this expertise is very often threatened with disappearance because of the difficulty to resist globalization and the fact that most of those "expertise holders" are not easily accessible due to geographical or other constraints. In this article, a methodological framework for capturing and modeling gestural knowledge and skills in wheel-throwing pottery is proposed. It is based on capturing gestures using wireless inertial sensors and statistical modeling. In particular, we used a system that allows for online alignment of gestures using a modified Hidden Markov Model. This methodology is implemented into a human-computer interface, which permits both the modeling and recognition of expert technical gestures. This system could be used to assist in the learning of these gestures by giving continuous feedback in real time by measuring the difference between expert and learner gestures. The system has been tested and evaluated on different potters with rare expertise, which is strongly related to their local identity. © 2014 ACM.en
dc.sourceJournal of Computing and Cultural Heritageen
dc.source.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-84906282244&partnerID=40&md5=ea7104d4ea1f485a9ba3002e10fbd18a
dc.subjectHCIen
dc.subjectInertial sensorsen
dc.subjectMachine learningen
dc.subjectPerceptionen
dc.subjectArtificial intelligenceen
dc.subjectHidden Markov modelsen
dc.subjectHuman computer interactionen
dc.subjectInertial navigation systemsen
dc.subjectSensory perceptionen
dc.subjectWheelsen
dc.subjectHuman computer interfacesen
dc.subjectInertial sensoren
dc.subjectIntangible cultural heritagesen
dc.subjectMethodological frameworksen
dc.subjectModeling and recognitionen
dc.subjectReal timeen
dc.subjectStatistical modelingen
dc.subjectLearning systemsen
dc.titleCapture, modeling, and recognition of expert technical gestures in wheel-throwing art of potteryen
dc.typejournalArticleen


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