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Resident identification using kinect depth image data and fuzzy clustering techniques.
| Content Provider | CiteSeerX |
|---|---|
| Author | Banerjee, Tanvi Keller, James M. Skubic, Marjorie |
| Abstract | 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. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Fuzzy Clustering Technique Depth Image Home Environment Image Moment Possibilistic Mean Several Apartment Health Professional Elderly Resident Shape Descriptor Single Depth Camera Gait Related Information Resident Identification Gait Information Senior Housing Facility Individual Resident Gait Pattern Box Information Passive Fall Risk Assessment Research Fall Risk |
| Content Type | Text |
