Research Output

Power Consuming Activity Recognition in Home Environment

  This work proposed an activity recognition model which focus on the power con-suming activity in home environment, to help residents modify their behavior. We set the IoT system with lower number of sensors. The key data for identifying activity comes from widely used smart sockets. It first took residents’ acceptability into consideration to set the IoT system, then used a seamless indoor position system to get residents’ position to help recognize the undergoing activities. Based on ontology, it made use of domain knowledge in daily activity and built an activity ontology. The system took real home situation into consideration and make full use of both electric and electronic appliances’ data into the context awareness. The knowledge helps improve the performance of the data-driven method. The experiment shows the system can recognize the common activities with a high accuracy and have a good applica-bility to real home scenario.

  • Date:

    31 October 2017

  • Publication Status:

    Published

  • DOI:

    10.1007/978-3-319-68505-2_31

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    European Commission

Citation

Liu, X., & Liu, Q. (2017). Power Consuming Activity Recognition in Home Environment. In X. Sun, H. Chao, X. You, & E. Bertino (Eds.), Cloud Computing and Security. ICCCS 2017, 361-372. https://doi.org/10.1007/978-3-319-68505-2_31

Authors

Keywords

Activity recognition, Ontology, Second-order HMM,

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