DocumentCode :
3634317
Title :
Comparative study of representations for segmentation of whole body human motion data
Author :
Dana Kulić;Yoshihiko Nakamura
Author_Institution :
Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Ontario, N2L 3G1, Canada
fYear :
2009
Firstpage :
4300
Lastpage :
4305
Abstract :
In previous work, the authors have been developing a stochastic model based approach for on-line segmentation of whole body human motion patterns during human motion observation and learning, using a simplified kinematic model of the human body. In this paper, we extend the proposed approach to larger, more realistic kinematic models, which can better represent a larger variety of human motions. These larger models may include spherical in addition to revolute joints. We examine the effects on segmentation performance due to motion representation choice, and compare the segmentation efficacy when Cartesian or joint angle data is used. The approach is tested on whole body human motion data modeled with a 42DoF kinematic model. The results indicate that Cartesian data seems to correspond most closely to the human evaluation of segment points. The experiments also demonstrate the efficacy of the segmentation approach for large kinematic models and a variety of human motions.
Keywords :
"Humans","Biological system modeling","Kinematics","Joints","Humanoid robots","Hidden Markov models","Animation","Torso","Intelligent robots","Stochastic systems"
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on
ISSN :
2153-0858
Print_ISBN :
978-1-4244-3803-7
Electronic_ISBN :
2153-0866
Type :
conf
DOI :
10.1109/IROS.2009.5353982
Filename :
5353982
Link To Document :
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