Comparative study of representations for segmentation of whole body human motion data

Authors:
Dana Kulic;Yoshihiko Nakamura
Affiliations:
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada and Department of Mechano-Informatics, University of Tokyo;Department of Mechano-Informatics, University of Tokyo, Tokyo, Japan
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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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.