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

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posted on 20.03.2017 by Zelin Wang, Safak Dogan, Milosh Stolikj, Ahmet 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.

Funding

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.

History

School

  • Loughborough University London

Published in

IEEE Intelligent Systems Conference (IntelliSys 2017)

Citation

WANG, Z. ...et 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.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Acceptance date

09/01/2017

Publication date

2017

Notes

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Language

en

Location

London, UK

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