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dc.contributor.authorJessop, Elena Naomi
dc.date.accessioned2015-02-11T17:48:52Z
dc.date.available2015-02-11T17:48:52Z
dc.date.issued2015-01
dc.date.submitted2012-08
dc.identifier.issn0024-094X
dc.identifier.issn1530-9282
dc.identifier.urihttp://hdl.handle.net/1721.1/94332
dc.description.abstractPerforming artists have frequently used technology to sense and extend the body’s natural expressivity through live control of multimedia. However, the sophistication, emotional content and variety of expression possible through the original physical channels are often not captured by these technologies and thus cannot be transferred from body to digital media. In this article the author brings together research from expressive performance analysis, machine learning and technological performance extension techniques to define a new framework for recognition and extension of expressive physical performance.en_US
dc.language.isoen_US
dc.publisherMIT Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1162/LEON_a_00935en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceMIT Pressen_US
dc.titleCapturing the Body Live: A Framework for Technological Recognition and Extension of Physical Expression in Performanceen_US
dc.typeArticleen_US
dc.identifier.citationJessop, Elena. “ Capturing the Body Live: A Framework for Technological Recognition and Extension of Physical Expression in Performance .” Leonardo (October 31, 2014): 32–38. © 2015 ISASTen_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.mitauthorJessop, Elena Naomien_US
dc.relation.journalLeonardoen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsJessop, Elenaen_US
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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