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Mixed-dependency models for multi-resident activity recognition in smart homes

Tran, SN ORCID: 0000-0002-5912-293X, Ngo, T-S, Zhang, Q and Karunanithi, K 2020 , 'Mixed-dependency models for multi-resident activity recognition in smart homes' , Multimedia Tools and Applications, vol. 79 , pp. 23445-23460 , doi: 10.1007/s11042-020-09093-0.

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Abstract

Recent growing interest in ambient intelligent environments has driven a desire for effective models to reason about activities of multiple residents. Such models are the keystone for the future of smart homes where occupants can be assisted with non-intrusive technologies. Much attention has been put on this research, however current works tend to focus on developing statistical algorithms for prediction, whilst there still lacks a study to fully understand the relations of residents’ behaviours and how they are reflected through the sensors’ states. In this paper we investigate the dependencies of the activities from residents and their interaction with the environments. We represent such dependencies in Bayesian networks that leads to construction of six variants of Hidden Markov Models (HMMs). Furthermore, we argue that a complete model should embody more than one type of dependency. Therefore, we propose an ensemble of HMMs, and then generalize it to a novel mixed-dependency model. In the experiments we perform intensive evaluation of our study on multi-resident activity recognition task. The results show that the proposed models outperform other models in three smart home environments, thus asserting our hypothesis.

Item Type: Article
Authors/Creators:Tran, SN and Ngo, T-S and Zhang, Q and Karunanithi, K
Keywords: smart homes, multi-resident activity recognition, Hidden Markov Models, activity recognition, ambient sensors, aged care
Journal or Publication Title: Multimedia Tools and Applications
Publisher: Kluwer Academic Publ
ISSN: 1380-7501
DOI / ID Number: 10.1007/s11042-020-09093-0
Copyright Information:

Copyright 2020 Springer Science+Business Media, LLC, part of Springer Nature

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