Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/22019
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dc.contributor.authorGjoreski, Hristijanen_US
dc.contributor.authorStankoski, Simonen_US
dc.contributor.authorKiprijanovska, Ivanaen_US
dc.contributor.authorNikolovska, Anastasijaen_US
dc.contributor.authorMladenovska, Natashaen_US
dc.contributor.authorTrajanoska, Marijaen_US
dc.contributor.authorVelichkovska, Bojanaen_US
dc.contributor.authorGjoreski, Martinen_US
dc.contributor.authorLuštrek, Mitjaen_US
dc.contributor.authorGams, Matjažen_US
dc.date.accessioned2022-08-09T09:16:07Z-
dc.date.available2022-08-09T09:16:07Z-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/22019-
dc.publisherSpringer International Publishingen_US
dc.titleWearable Sensors Data-Fusion and Machine-Learning Method for Fall Detection and Activity Recognitionen_US
dc.relation.conferenceChallenges and Trends in Multimodal Fall Detection for Healthcareen_US
dc.identifier.doi10.1007/978-3-030-38748-8_4-
dc.identifier.urlhttp://link.springer.com/content/pdf/10.1007/978-3-030-38748-8_4-
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:Faculty of Electrical Engineering and Information Technologies: Book Chapters
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