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Dempster–Shafer theory-based human activity recognition in smart home environments

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Abstract

Context awareness and activity recognition are becoming a hot research topic in ambient intelligence (AmI) and ubiquitous robotics, due to the latest advances in wireless sensor network research which provides a richer set of context data and allows a wide coverage of AmI environments. However, using raw sensor data for activity recognition is subject to different constraints and makes activity recognition inaccurate and uncertain. The Dempster–Shafer evidence theory, known as belief functions, gives a convenient mathematical framework to handle uncertainty issues in sensor information fusion and facilitates decision making for the activity recognition process. Dempster–Shafer theory is more and more applied to represent and manipulate contextual information under uncertainty in a wide range of activity-aware systems. However, using this theory needs to solve the mapping issue of sensor data into high-level activity knowledge. The present paper contributes new ways to apply the Dempster–Shafer theory using binary discrete sensor information for activity recognition under uncertainty. We propose an efficient mapping technique that allows converting and aggregating the raw data captured, using a wireless senor network, into high-level activity knowledge. In addition, we propose a conflict resolution technique to optimize decision making in the presence of conflicting activities. For the validation of our approach, we have used a real dataset captured using sensors deployed in a smart home. Our results demonstrate that the improvement of activity recognition provided by our approaches is up to of 79 %. These results demonstrate also that the accuracy of activity recognition using the Dempster–Shafer theory with the proposed mappings outperforms both naïve Bayes classifier and J48 decision tree.

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Acknowledgments

The authors would like to thank Dr. Susan McKeever for her help and for providing us source codes that we have used for comparison purposes. Additionally, the authors thank the anonymous reviewers for their useful comments and suggestions, which are very helpful to improve the technical quality of this paper.

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Authors and Affiliations

  1. Informatics Systems Laboratory, Ecole Militaire Polytechnique, BP 17, EMP, Bordj-El-Bahri, 16111, Algiers, Algeria

    Faouzi Sebbak & Farid Benhammadi

  2. LISSI Laboratory, University of Paris Est Créteil (UPEC), 120-122 Paul Armangot, 94400, Vitry-sur-Seine, France

    Abdelghani Chibani & Yacine Amirat

  3. LRIA Laboratory, University of Science and Technology Houari Boumediene (USTHB), BP 32, El-Alia, Bab-Ezzouar, 16111, Algiers, Algeria

    Faouzi Sebbak & Aicha Mokhtari

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  1. Faouzi Sebbak
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  2. Farid Benhammadi
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  3. Abdelghani Chibani
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Correspondence to Faouzi Sebbak.

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Sebbak, F., Benhammadi, F., Chibani, A. et al. Dempster–Shafer theory-based human activity recognition in smart home environments. Ann. Telecommun. 69, 171–184 (2014). https://doi.org/10.1007/s12243-013-0407-2

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