Support Vector Machine Classification in a Device-Free Passive Localisation (DfPL) Scenario

  • Gabriel Deak
  • Kevin Curran
  • Joan Condell
  • Daniel Deak
  • Piotr Kiedrowski
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 184)


The holy grail of tracking people indoors is being able to locate them when they are not carrying any wireless tracking devices. The aim is to be able to track people just through their physical body interfering with a standard wireless network that would be in most peoples home. The human body contains about 70% water which attenuates the wireless signal reacting as an absorber. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person’s location. This paper is focused on taking the principle of Device-free Passive Localisation (DfPL) and applying it to be able to actually distinguish if there is more than one person in the environment. In order to solve this problem, we tested a Support Vector Machine (SVM) classifier with kernel functions such as Linear, Quadratic, Polynomial, Gaussian Radial Basis Function (RBF) and Multilayer Perceptron (MLP) in order to detect movement based on changes in the wireless signal strength.


Suport Vector Machine Wireless Sensor Network Kernel Function Receive Signal Strength Indicator Gaussian Radial Basis Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gabriel Deak
    • 1
  • Kevin Curran
    • 1
  • Joan Condell
    • 1
  • Daniel Deak
    • 2
  • Piotr Kiedrowski
    • 3
  1. 1.Intelligent System Research CentreDerryUK
  2. 2.S.C. Centrul de Calcul Info98 S.A.PetrosaniRomania
  3. 3.Institute of TelecommunicationUniversity of Technology and Life ScienceBydgoszczPoland

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