Sensing motion using spectral and spatial analysis of WLAN RSSI

  • Authors:
  • Kavitha Muthukrishnan;Maria Lijding;Nirvana Meratnia;Paul Havinga

  • Affiliations:
  • University of Twente, Faculty of Computer Science, Computer Architecture Design and Test for Embedded Systems group, AE, Enschede, The Netherlands;University of Twente, Faculty of Computer Science, Computer Architecture Design and Test for Embedded Systems group, AE, Enschede, The Netherlands;University of Twente, Faculty of Computer Science, Computer Architecture Design and Test for Embedded Systems group, AE, Enschede, The Netherlands;University of Twente, Faculty of Computer Science, Computer Architecture Design and Test for Embedded Systems group, AE, Enschede, The Netherlands

  • Venue:
  • EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
  • Year:
  • 2007

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Abstract

In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis confirms our claim that 'signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move'. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of WLAN signal's RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support.