AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks

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@InProceedings{Qi:2013:RTAS,
  author =       "Xin Qi and Matthew Keally and Gang Zhou and 
                 Yantao Li and Zhen Ren",
  title =        "{AdaSense}: Adapting sampling rates for activity
                 recognition in Body Sensor Networks",
  booktitle =    "19th IEEE Real-Time and Embedded Technology and
                 Applications Symposium (RTAS 2013)",
  year =         "2013",
  month =        apr,
  pages =        "163--172",
  keywords =     "genetic algorithms, genetic programming, Activity
                 Recognition, Sampling Rate Reduction, Body Sensor
                 Network",
  DOI =          "doi:10.1109/RTAS.2013.6531089",
  ISSN =         "1080-1812",
  abstract =     "In a Body Sensor Network (BSN) activity recognition
                 system, sensor sampling and communication quickly
                 deplete battery reserves. While reducing sampling and
                 communication saves energy, this energy savings usually
                 comes at the cost of reduced recognition accuracy. To
                 address this challenge, we propose AdaSense, a
                 framework that reduces the BSN sensors sampling rate
                 while meeting a user-specified accuracy requirement.
                 AdaSense uses a classifier set to do either
                 multi-activity classification that requires a high
                 sampling rate or single activity event detection that
                 demands a very low sampling rate. AdaSense aims to use
                 lower power single activity event detection most of the
                 time. It only resorts to higher power multi-activity
                 classification to find out the new activity when it is
                 confident that the activity changes. Furthermore,
                 AdaSense is able to determine the optimal sampling
                 rates using a novel Genetic Programming algorithm.
                 Through this Genetic Programming approach, AdaSense
                 reduces sampling rates for both lower power single
                 activity event detection and higher power
                 multi-activity classification. With an existing BSN
                 dataset and a smart phone dataset we collect from eight
                 subjects, we demonstrate that AdaSense effectively
                 reduces BSN sensors sampling rate and outperforms a
                 state-of-the-art solution in terms of energy savings.",
  notes =        "Also known as \cite{6531089}",
}

Genetic Programming entries for Xin Qi Matthew Keally Gang Zhou Yantao Li Zhen Ren

Citations