Resident Identification using Kinect Depth Image Data and Fuzzy Clustering Techniques

Tanvi Banerjee, James M. Keller, Marjorie Skubic

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

Abstract

As a part of our passive fall risk assessment research in home environments, we present a method to identify older residents using features extracted from their gait information from a single depth camera. Depth images have been collected continuously for about eight months from several apartments at a senior housing facility. Shape descriptors such as bounding box information and image moments were extracted from silhouettes of the depth images. The features were then clustered using Possibilistic C Means for resident identification. This technology will allow researchers and health professionals to gather more information on the individual residents by filtering out data belonging to non-residents. Gait related information belonging exclusively to the older residents can then be gathered. The data can potentially help detect changes in gait patterns which can be used to analyze fall risk for elderly residents by passively observing them in their home environments.

Original languageAmerican English
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
PublisherIEEE
Pages5102-5105
Number of pages4
ISBN (Electronic)978-1-4577-1787-1
ISBN (Print)978-1-4244-4119-8
DOIs
StatePublished - Nov 10 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Conference

Conference34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus Subject Areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Keywords

  • Clustering Algorithms
  • Green Products
  • Monitoring
  • Phase Change Materials

Disciplines

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

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