6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing

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  • @INPROCEEDINGS{10.4108/ICST.MOBIQUITOUS2009.6818,
        author={Tao Gu and Zhanqing Wu and Liang  Wang and Xianping  Tao and Jian  Lu},
        title={Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing},
        proceedings={6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={IEEE},
        proceedings_a={MOBIQUITOUS},
        year={2009},
        month={11},
        keywords={Computer science Humans Intelligent sensors Laboratories Mathematics Pattern recognition Performance analysis Pervasive computing Sensor phenomena and characterization Smart homes},
        doi={10.4108/ICST.MOBIQUITOUS2009.6818}
    }
    
  • Tao Gu
    Zhanqing Wu
    Liang Wang
    Xianping Tao
    Jian Lu
    Year: 2009
    Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing
    MOBIQUITOUS
    IEEE
    DOI: 10.4108/ICST.MOBIQUITOUS2009.6818
Tao Gu1,*, Zhanqing Wu2,*, Liang Wang2,*, Xianping Tao2,*, Jian Lu2,*
  • 1: Department of Mathematics and Computer Science, University of Southern Denmark
  • 2: State Key Laboratory for Novel Software Technology, Nanjing University, China.
*Contact email: gu@imada.sdu.dk, wzq@ics.nju.edu.cn, wangliang@ics.nju.edu.cn, txp@ics.nju.edu.cn, lj@nju.edu.cn

Abstract

Understanding and recognizing human activities from sensor readings is an important task in pervasive computing. Existing work on activity recognition mainly focuses on recognizing activities for a single user in a smart home environment. However, in real life, there are often multiple inhabitants live in such an environment. Recognizing activities of not only a single user, but also multiple users is essential to the development of practical context-aware applications in pervasive computing. In this paper, we investigate the fundamental problem of recognizing activities for multiple users from sensor readings in a home environment, and propose a novel pattern mining approach to recognize both single-user and multi-user activities in a unified solution. We exploit emerging pattern -a type of knowledge pattern that describes significant changes between classes of data - for constructing our activity models, and propose an emerging pattern based multi-user activity recognizer (epMAR) to recognize both single-user and multiuser activities. We conduct our empirical studies by collecting real-world activity traces done by two volunteers over a period of two weeks in a smart home environment, and analyze the performance in detail with respect to various activity cases in a multi-user scenario. Our experimental results demonstrate that our epMAR recognizer achieves an average accuracy of 89.72% for all the activity cases.