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An adaptive sensor network for home intrusion detection by human activity profiling

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Abstract

An adaptive sensor network for home intrusion detection is proposed. The sensor network combines profile-based anomaly detection and adaptive information processing based on hidden Markov models (HMM) that allow the system to train and tune the profiles automatically. The trade-off between miss-alarms and false alarms has been studied experimentally. Several types of hypothetical intrusion have been tested and successfully detected. However, hypothetical anomalies such as supposing that a resident has fallen down due to sudden illness have been difficult to detect.

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Authors and Affiliations

  1. Department of Electrical and Information Engineering, Toyohashi University of Technology, Tempaku, Toyohashi, Aichi, 441-8580, Japan

    Masahiro Tokumitsu, Masashi Murakami & Yoshiteru Ishida

Authors
  1. Masahiro Tokumitsu
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  2. Masashi Murakami
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  3. Yoshiteru Ishida
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Corresponding author

Correspondence to Yoshiteru Ishida.

Additional information

This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010

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Tokumitsu, M., Murakami, M. & Ishida, Y. An adaptive sensor network for home intrusion detection by human activity profiling. Artif Life Robotics 16, 36–39 (2011). https://doi.org/10.1007/s10015-011-0872-5

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  • DOI: https://doi.org/10.1007/s10015-011-0872-5

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