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:	Refereed Article
Keywords:	multi-resident activity, pervasive computing, smart homes
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Intelligent robotics
Objective Division:	Health
Objective Group:	Specific population health (excl. Indigenous health)
Objective Field:	Health related to ageing
UTAS Author:	Tran, SN (Dr Son Tran)
ID Code:	138085
Year Published:	2019
Web of Science® Times Cited:	12
Deposited By:	Information and Communication Technology
Deposited On:	2020-03-24
Last Modified:	2020-05-18
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