Feature selection and occupancy classification using seismic sensors

  • Authors:

  • Arun Subramanian;Kishan G. Mehrotra;Chilukuri K. Mohan;Pramod K. Varshney;Thyagaraju Damarla

  • Affiliations:

  • Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY;Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY;Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY;Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY;Army Research Laboratory, Adelphi, MD

  • Venue:
  • IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we consider the problem of indoor surveillance and propose a feature selection scheme for occupancy classification in an indoor environment. The classifier aims to determine whether there is exactly one occupant or more than one occupant. Data are obtained from six seismic sensors (geophones) that are deployed in a typical building hallway. Four proposed features exploit amplitude and temporal characteristics of the seismic time series. A neural network classifier achieves performance ranging between 77% to 95% on the test data, depending on the type of construction of the location in the building being monitored.