Combining automated on-line segmentation and incremental clustering for whole body motions

Dana Kulić, Wataru Takano, Yoshihiko Nakamura

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

25 Citations (Scopus)

Abstract

This paper describes a novel approach for incremental learning of human motion pattern primitives through on-line observation of human motion. The observed motion time series data stream is first stochastically segmented into potential motion primitive segments, based on the assumption that data belonging to the same motion primitive will have the same underlying distribution. The motion segments are then abstracted into a stochastic model representation, and automatically clustered and organized. As new motion patterns are observed, they are incrementally grouped together based on their relative distance in the model space. The resulting representation of the knowledge domain is a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The tree leaves, which represent the most specialized learned motion primitives, are then passed back to the segmentation algorithm, so that as the number of known motion primitives increases, the accuracy of the segmentation can also be improved. The combined algorithm is tested on a sequence of continuous human motion data obtained through motion capture, and demonstrates the performance of the proposed approach.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Robotics and Automation, ICRA 2008
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2591-2598
Number of pages8
ISBN (Print)9781424416479
DOIs
Publication statusPublished - 18 Sep 2008
Externally publishedYes
EventIEEE International Conference on Robotics and Automation 2008 - Pasadena, United States of America
Duration: 19 May 200823 May 2008
https://ieeexplore.ieee.org/xpl/conhome/4534525/proceeding (Proceedings)

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

ConferenceIEEE International Conference on Robotics and Automation 2008
Abbreviated titleICRA 2008
CountryUnited States of America
CityPasadena
Period19/05/0823/05/08
Internet address

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