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dc.contributor.authorAtrash, Amin
dc.contributor.authorStarner, Thad
dc.date.accessioned2004-09-23T19:12:09Z
dc.date.available2004-09-23T19:12:09Z
dc.date.issued2003
dc.identifier.urihttp://hdl.handle.net/1853/3243
dc.description.abstractMost gesture recognition systems analyze gestures intended for communication (e.g. sign language) or for command (e.g. navigation in a virtual world). We attempt instead to recognize gestures made in the course of performing everyday work activities. Specifically, we examine activities in a wood shop, both in isolation as well as in the context of a simulated assembly task. We apply linear discriminant analysis (LDA) and hidden Markov model (HMM) techniques to features derived from body-worn accelerometers and microphones. The resulting system can successfully segment and identify most shop activities with zero false positives and 83.5% accuracy.en
dc.format.extent1134065 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesGVU Technical Report;GIT-GVU-03-32
dc.subjectGesture recognitionen
dc.subjectAccelerometersen
dc.subjectHMMsen
dc.subjectLDAen
dc.subjectContext recognitionen
dc.subjectWearable computersen
dc.titleRecognizing Workshop Activity Using Body Worn Microphones and Accelerometersen
dc.typeTechnical Reporten


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