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dc.contributor.authorSanchez, Veralia Gabriela
dc.contributor.authorSkeie, Nils-Olav
dc.date.accessioned2019-01-23T11:23:26Z
dc.date.available2019-01-23T11:23:26Z
dc.date.created2018-12-04T19:18:18Z
dc.date.issued2018
dc.identifier.citationLinköping Electronic Conference Proceedings. 2018, (153), 222-229.nb_NO
dc.identifier.issn1650-3686
dc.identifier.urihttp://hdl.handle.net/11250/2581914
dc.description.abstractHuman activity recognition in smart house environments is the task of automatic recognition of physical activities of a person to build a safe environment for older adults or any person in their daily life. The aim of this work is to develop a model that can recognize abnormal activities for assisting people living alone in a smart house environment. The idea is based on the assumption that people tend to follow a specific pattern of activities in their daily life. An open source database is used to train the decision trees classifier algorithm. Training and testing of the algorithm is performed using MATLAB. The results show an accuracy rate of 88.02% in the activity detection task.nb_NO
dc.description.abstractDecision Trees for Human Activity Recognition in Smart House Environmentsnb_NO
dc.language.isoengnb_NO
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.no*
dc.titleDecision Trees for Human Activity Recognition in Smart House Environmentsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber222-229nb_NO
dc.source.journalLinköping Electronic Conference Proceedingsnb_NO
dc.source.issue153nb_NO
dc.identifier.doi10.3384/ecp18153222
dc.identifier.cristin1639164
cristin.unitcode222,58,2,0
cristin.unitnameInstitutt for elektro, IT og kybernetikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse-Ikkekommersiell 4.0 Internasjonal
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