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Research on passive human activity recognition using WiFi ambient signals

  • Affective Computing andAnHui Province Key Laboratory of Advanced Intelligence Machine, Hefei University of Technology, Hefei 230009,China

Cite this: JUSTC, 2015, 45(4): 308-313
https://doi.org/10.3969/j.issn.0253-2778.2015.04.008
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  • Received Date: March 11, 2014
  • Revised Date: October 09, 2014
  • Accepted Date: October 09, 2014
  • Published Date: April 29, 2015
    Abstract
  • Although traditional k-nearest neighbor(K-NN) and Bagging can recognize effectively less human activities using WiFi ambient signal, recognition by either alone of the seven states, namely, empty, walking, sitting, standing, sleeping, falling and running, is not ideal. To improve recognition rates, a new algorithm, fusion algorithm, was designed. It significantly outperforms K-NN and Bagging in terms of recognition ratios in both single-subject and multi-subject experiments.
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