Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population.
The Microsoft Kinect camera is becoming increasingly popular in many areas aside from entertainment, including human activity monitoring and rehabilitation. Many people, however, fail to consider the reliability and accuracy of the Kinect human pose estimation when they depend on it as a measuring system. In this paper we compare the Kinect pose estimation (skeletonization) with more established techniques for pose estimation from motion capture data, examining the accuracy of joint localization and robustness of pose estimation with respect to the orientation and occlusions. We have evaluated six physical exercises aimed at coaching of elderly population. Experimental results present pose estimation accuracy rates and corresponding error bounds for the Kinect system.
Department of Electrical Engineering and Computer Sciences, University of Californiaat Berkeley, Berkeley, CA 94720, USA. xobdrzal@eecs.berkeley.edu
Stepán
S
Obdrzálek
Gregorij
G
Kurillo
Ferda
F
Ofli
Ruzena
R
Bajcsy
Edmund
E
Seto
Holly
H
Jimison
Michael
M
Pavel
D000368:Aged; D000369:Aged, 80 and over; D015444:Exercise; D005260:Female; D006801:Humans; D008297:Male; D011187:Posture; D014741:Video Recording