Document Type
Conference Proceeding
Publication Date
2012
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.
Repository Citation
Banerjee, T.,
Keller, J. M.,
& Skubic, M.
(2012). Resident Identification using Kinect Depth Image Data and Fuzzy Clustering Techniques. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 5102-5105.
https://corescholar.libraries.wright.edu/knoesis/1112
DOI
10.1109/EMBC.2012.6347141
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Presented at the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, August 28-September 1, 2012.