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

Dana Kulić, Yoshihiko Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4300-4305
Number of pages6
ISBN (Print)9781424438044
DOIs
Publication statusPublished - 11 Dec 2009
Externally publishedYes
Event2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 - St. Louis, MO, United States of America
Duration: 11 Oct 200915 Oct 2009

Publication series

Name2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009

Conference

Conference2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
CountryUnited States of America
CitySt. Louis, MO
Period11/10/0915/10/09

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