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Abstract

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.

Item Type:	Article
Authors/Creators:	Tran, SN and Nguyen, D and Ngo, TS and Vu, XS and Hoang, L and Zhang, Q and Karunanithi, M
Keywords:	multi-resident activity, pervasive computing, smart homes
Journal or Publication Title:	Artificial Intelligence Review
Publisher:	Kluwer Academic Publ
ISSN:	0269-2821
DOI / ID Number:	https://doi.org/10.1007/s10462-019-09783-8
Copyright Information:	Copyright 2019 Springer Nature B.V.
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