Resident identification using kinect depth image data and fuzzy clustering techniques.
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.
Electrical and Computer Engineering Department at the University of Missouri, Columbia, MO 65211, USA. tsbycd@mizzou.edu
Tanvi
T
Banerjee
James M
JM
Keller
Marjorie
M
Skubic
D000058:Accidental Falls / Q000517:prevention & control*; D056044:Actigraphy / Q000379:methods*; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D017143:Fuzzy Logic; D005684:Gait / Q000502:physiology*; D015577:Geriatric Assessment / Q000379:methods; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods; D010355:Patient Identification Systems / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D018570:Risk Assessment / Q000379:methods; D018910:Video Games