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Mining Regularities in Body Sensor Network Data

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Internet and Distributed Computing Systems (IDCS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9258))

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Abstract

The recent emergence of body sensor networks (BSNs) has made it easy to continuously collect and process various health-oriented data related to temporal, spatial and vital sign monitoring of patient. As such, discovering or mining interesting knowledge from the BSN data stream is becoming an important issue to promote and assist important decision making in healthcare. In this paper, we focus on mining the inherent regularity of different parameter readings obtained from different body sensors related to vital sign data of a patent for the purpose of following up health condition to prevent some kinds of chronic diseases. Specifically we design and develop an efficient and scalable regular pattern mining technique that can mine the complete set of periodically/regularly occurring patterns in BSN data stream based on a user-specified periodicity/regularity threshold for the data and the subject. Various experiments were carried on both real and synthetic data to validate the efficiency of the proposed regular pattern mining technique as compared to state-of-the-art approaches.

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Acknowledgement

This project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (12-INF2885-02).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Kyung Hee University, Seoul, South Korea

    Syed Khairuzzaman Tanbeer & Byeong-Soo Jeong

  2. College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Mohammad Mehedi Hassan & Majed Alrubaian

Authors
  1. Syed Khairuzzaman Tanbeer
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  2. Mohammad Mehedi Hassan
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  3. Majed Alrubaian
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  4. Byeong-Soo Jeong
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Corresponding author

Correspondence to Mohammad Mehedi Hassan .

Editor information

Editors and Affiliations

  1. School of Systems Engineering, University of Reading, Reading, Berkshire, United Kingdom

    Giuseppe Di Fatta

  2. Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, University of Calabria Dipartimento di Ingegneria Informat, Rende, Italy

    Giancarlo Fortino

  3. School of Logistics and Engineer, University of Technology Wuhan, Wuhan, China

    Wenfeng Li

  4. CSIRO ICT, Acton, Australia

    Mukaddim Pathan

  5. School of Systems Engineering, University of Reading, Whiteknights, Reading, United Kingdom

    Frederic Stahl

  6. Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, University of Calabria, Rende, Italy

    Antonio Guerrieri

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Tanbeer, S.K., Hassan, M.M., Alrubaian, M., Jeong, BS. (2015). Mining Regularities in Body Sensor Network Data. In: Di Fatta, G., Fortino, G., Li, W., Pathan, M., Stahl, F., Guerrieri, A. (eds) Internet and Distributed Computing Systems. IDCS 2015. Lecture Notes in Computer Science(), vol 9258. Springer, Cham. https://doi.org/10.1007/978-3-319-23237-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-23237-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23236-2

  • Online ISBN: 978-3-319-23237-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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