TY - JOUR
AU1 - Banerjee, T.
AU2 - Keller, J. M.
AU3 - Skubic, M.
AU4 - 
AB - 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August -1 September, 2012 Resident Identification Using Kinect Depth Image Data and Fuzzy Clustering Techniques Tanvi Banerjee, Student Member, IEEE, James M. Keller, Fellow, IEEE, and Marjorie Skubic, Member, IEEE of a human and unwrapping it by evenly sampling the Abstract-As a part of our passive fall risk assessment research in home environments, we present a method to contour. The distance between each contour point and its identify older residents using features extracted from their gait center of gravity was computed, termed, "distance information from a single depth camera. Depth images have profiling." The unwrapped contour was then processed by been collected continuously for about eight months from principal component analysis, and compared against the several apartments at a senior housing facility. Shape profiles of the class exemplars using the nearest neighbor descriptors such as bounding box information and image approach, testing with a standardized dataset [12]. Other moments were extracted from silhouettes of the depth images. work using template matching techniques included that of The features were then clustered using Possibilistic C Means Collins et al. [13] who first extracted key frames from a for 
TI - Resident identification using kinect depth image data and fuzzy clustering techniques
JF - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
DO - 10.1109/embc.2012.6347141
DA - 2012-08-01
UR - https://www.deepdyve.com/lp/unpaywall/resident-identification-using-kinect-depth-image-data-and-fuzzy-BNnqcAR7a0
DP - DeepDyve
ER -