Sit-to-Stand Detection using Fuzzy Clustering Techniques

Tanvi Banerjee, James M. Keller, Marjorie Skubic, Carmen Abbott

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

Abstract

The ability to rise from a chair is an important parameter to assess the balance deficits of a person. In particular, this can be an indication of risk for falling in elderly persons. Our goal is automated assessment of fall risk using video data. Towards this goal, we present a simple yet effective method of detecting transition, i.e. sit-to-stand and stand-to-sit, from image frames using fuzzy clustering methods on image moments. The technique described in this paper is shown to be robust even in the presence of noise and has been tested on several data sequences using different subjects yielding promising results.

Original languageAmerican English
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010
PublisherIEEE
ISBN (Electronic)978-1-4244-6921-5, 978-1-4244-6920-8
ISBN (Print)978-1-4244-6919-2
DOIs
StatePublished - Sep 23 2010
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - Barcelona, Spain
Duration: Jul 18 2010Jul 23 2010

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
Country/TerritorySpain
CityBarcelona
Period7/18/107/23/10

ASJC Scopus Subject Areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

Keywords

  • Government
  • Fuzzy Rough Set

Disciplines

  • Bioinformatics
  • Communication Technology and New Media
  • Databases and Information Systems
  • OS and Networks
  • Science and Technology Studies

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