19163064

Source:http://linkedlifedata.com/resource/pubmed/id/19163064

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0015259, umls-concept:C0017510, umls-concept:C0376691, umls-concept:C0524637, umls-concept:C1704675
pubmed:dateCreated	2009-2-16
pubmed:abstractText	This paper describes a gesture recognition system which can recognize seated exercises that will be incorporated into an in-home automated interactive exercise program. Hidden Markov Models (HMMs) are used as a motion classifier, with motion features extracted from the grayscale images and the location of the subject's head estimated at initialization. An overall recognition rate of 94.1% is achieved.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/101243413
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:issn	1557-170X
pubmed:author	pubmed-author:JimisonHolly BHB, pubmed-author:PavelMishaM, pubmed-author:PerkinsJedediahJ, pubmed-author:ScottSusanS
pubmed:issnType	Print
pubmed:volume	2008
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	1915-7
pubmed:meshHeading	pubmed-meshheading:19163064-Biomedical Engineering, pubmed-meshheading:19163064-Exercise, pubmed-meshheading:19163064-Exercise Therapy, pubmed-meshheading:19163064-Gestures, pubmed-meshheading:19163064-Humans, pubmed-meshheading:19163064-Image Interpretation, Computer-Assisted, pubmed-meshheading:19163064-Markov Chains, pubmed-meshheading:19163064-Movement
pubmed:year	2008
pubmed:articleTitle	Gesture recognition for interactive exercise programs.
pubmed:affiliation	Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon 97239, USA. jperkins@bme.ogi.edu
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't