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Towards adaptive control in smart homes: Overall system design and initial evaluation of activity recognition

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conference contribution
posted on 20.03.2017, 11:11 by Zelin Wang, Safak DoganSafak Dogan, Milosh Stolikj, Ahmet KondozAhmet Kondoz
This paper proposes an approach for adaptive control over devices within a smart home, by learning user behavior and preferences over time. The proposed solution leverages three components: activity recognition for realising the state of a user, ontologies for finding relevant devices within a smart home, and machine learning for decision making. In this paper, the focus is on the first component. Existing algorithms for activity recognition are systematically evaluated on a real-world dataset. A thorough analysis of the algorithms’ accuracy is presented, with focus on the structure of the selected dataset. Finally, further study of the dataset is carried out, aiming at reasoning factors that influence the activity recognition performance.


The work presented in this paper was carried out as part of CLOUDSCREENS, a Marie Curie Initial Training Networks action funded by the European Commission’s 7th Framework Program under the Grant Number 608028.



  • Loughborough University London

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IEEE Intelligent Systems Conference (IntelliSys 2017)


WANG, Z. al., 2017. Towards adaptive control in smart homes: Overall system design and initial evaluation of activity recognition. Presented at the IEEE Intelligent Systems Conference (IntelliSys 2017), London, 7-8th Sept.




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