A human motion estimation method based on GP-UKF

Ziyou Wang, Jun Kinugawa, Hongbo Wang, Kazuhiro Kosuge

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Citations (Scopus)

    Abstract

    A novel human motion estimation method is presented in this paper. The motion of the human is estimated by an Unscented Kalman filter (UKF), in which a nonlinear dynamic model is used to predict trajectory of human. This dynamic model is obtained from sample data by using Gaussian Process (GP) regression. The sample data includes information of body segment posture and trajectory data collected by motion capture system. The GP-UKF can extract the underlying dynamics from the sample data, with which the future non-linear transition can be predicted. The experiment results show that the proposed method has improved accuracy over conventional method.

    Original languageEnglish
    Title of host publication2014 IEEE International Conference on Information and Automation, ICIA 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1228-1232
    Number of pages5
    ISBN (Electronic)9781479941001
    DOIs
    Publication statusPublished - 2014 Oct 21
    Event2014 IEEE International Conference on Information and Automation, ICIA 2014 - Hailar, Hulunbuir, China
    Duration: 2014 Jul 282014 Jul 30

    Publication series

    Name2014 IEEE International Conference on Information and Automation, ICIA 2014

    Other

    Other2014 IEEE International Conference on Information and Automation, ICIA 2014
    Country/TerritoryChina
    CityHailar, Hulunbuir
    Period14/7/2814/7/30

    Keywords

    • GP-UKF
    • Gaussian Process
    • Motion estimation
    • Unscented Kalman filter

    ASJC Scopus subject areas

    • Modelling and Simulation

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