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dc.contributor.authorKumar, R.-
dc.contributor.authorQamar, I.-
dc.contributor.authorVirdi, J.S.-
dc.contributor.authorKrishnan, N.C.-
dc.date.accessioned2016-11-18T05:00:33Z-
dc.date.available2016-11-18T05:00:33Z-
dc.date.issued2016-11-18-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/403-
dc.description.abstractAdvances in pervasive and ubiquitous computing have resulted in the development of sensors that can be easily deployed in the natural habitat of a human to acquire activity related data. However, inferring meaningful activity information from sensor data is still a challenging problem. This paper addresses the problem of inferring activities that are simultaneously performed by multiple residents in a smart home or single resident performing multiple activities concurrently. The paper formulates this problem as learning multiple activity labels from a sequence of sensor data. It investigates the suitability of multi-label learning algorithms inspired by decision trees as a proposed solution to the problem. The results obtained from the experiments on four benchmarking multi-resident activity datasets clearly indicate the superiority of decision tree ensemble (random forests) based approaches for multi-label learningen_US
dc.language.isoen_USen_US
dc.subjectHuman activity recognition (HAR)en_US
dc.subjectMulti-label learningen_US
dc.titleMulti-label learning for activity recognitionen_US
dc.typeArticleen_US
Appears in Collections:Year-2015

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