Feature selection and occupancy classification using seismic sensors

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationTrends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Proceedings
Pages605-614
Number of pages10
EditionPART 2
DOIs
StatePublished - Dec 1 2010
Event23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010 - Cordoba, Spain
Duration: Jun 1 2010Jun 4 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6097 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010
CountrySpain
CityCordoba
Period6/1/106/4/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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