A human motion estimation method based on GP-UKF

Author

Ziyou Wang ; Kinugawa, Jun ; Hongbo Wang ; Kazahiro, Kosuge

Author_Institution

Dept. of Bioeng. & Robot., Tohoku Univ., Sendai, Japan

fYear

2014

fDate

28-30 July 2014

Firstpage

1228

Lastpage

1232

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.

Keywords

Gaussian processes; Kalman filters; motion estimation; regression analysis; GP regression; GP-UKF; Gaussian process; body segment posture; human motion estimation method; motion capture system; nonlinear dynamic model; nonlinear transition; trajectory data; unscented Kalman filter; Gaussian processes; Kalman filters; Motion estimation; Prediction algorithms; Predictive models; Robots; Trajectory; GP-UKF; Gaussian Process; Motion estimation; Unscented Kalman filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation (ICIA), 2014 IEEE International Conference on

Conference_Location

Hailar

Type

conf

DOI

10.1109/ICInfA.2014.6932836

Filename

6932836