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On multi-resident activity recognition in ambient smart-homes
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0001-8820-2405
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2019 (English)In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462Article in journal (Refereed) Epub ahead of print
Abstract [en]

Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue. Several approaches have been proposed for multi-resident activity recognition, however, there still lacks a comprehensive benchmark for future research and practical selection of models. In this paper, we study different methods for multi-resident activity recognition and evaluate them on the same sets of data. In particular, we explore the effectiveness and efficiency of temporal learning algorithms using sequential data and non-temporal learning algorithms using temporally-manipulated features. In the experiments we compare and analyse the results of the studied methods using datasets from three smart homes.

Place, publisher, year, edition, pages
Springer, 2019.
Keywords [en]
Multiresident activity, Pervasive computing, Smart homes
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-165764DOI: 10.1007/s10462-019-09783-8ISI: 000495967800001OAI: oai:DiVA.org:umu-165764DiVA, id: diva2:1375879
Note

2019-12-06: aheadofprint. Granskad. /ML

Available from: 2019-12-06 Created: 2019-12-06 Last updated: 2019-12-06

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Vu, Xuan-Son

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