A human motion estimation method based on GP-UKF

Ziyou Wang, Jun Kinugawa, Hongbo Wang, Kosuge Kazahiro

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
CountryChina
CityHailar, Hulunbuir
Period14/7/2814/7/30

Keywords

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

ASJC Scopus subject areas

  • Modelling and Simulation

Fingerprint Dive into the research topics of 'A human motion estimation method based on GP-UKF'. Together they form a unique fingerprint.

Cite this