University Of Tasmania

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

journal contribution
posted on 2023-05-20, 17:25 authored by Son TranSon Tran, Ngo, T-S, Zhang, Q, Karunanithi, K
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.


Publication title

Multimedia Tools and Applications








School of Information and Communication Technology


Kluwer Academic Publ

Place of publication

Van Godewijckstraat 30, Dordrecht, Netherlands, 3311 Gz

Rights statement

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

Repository Status

  • Restricted

Socio-economic Objectives

Health related to ageing