Model-driven statistical analysis of human gait motion

Author

Yoo, Jang-Hee ; Nixon, Mark S. ; Harris, Chris J.

Author_Institution

Dept. of Electron. & Comput. Sci., Southampton Univ., UK

Volume

1

fYear

2002

fDate

2002

Abstract

We describe a new method for analyzing and extracting human gait motion by combining statistical methods with image processing. The periodic motion of human gait is modeled by trigonometric-polynomial interpolant functions. The gait description is derived by topological analysis guided by medical studies that selects areas from which joint angles are derived by regression analysis. Then, the interpolant functions are fitted to the gait data and whilst showing fidelity to earlier medical studies, also show recognition capability. As such, a new combination of medical knowledge, image processing and regression analysis can be used to label human motion in image sequences.

Keywords

computer vision; feature extraction; gait analysis; image motion analysis; image sequences; interpolation; polynomial approximation; statistical analysis; computer vision; gait data; human gait motion analysis; human gait motion extraction; human motion; image processing; image sequences; interpolant functions; joint angles; medical studies; model-driven statistical analysis; periodic motion; regression analysis; statistical methods; topological analysis; trigonometric-polynomial interpolant functions; Biomedical imaging; Computer vision; Humans; Image analysis; Image motion analysis; Image processing; Image sequences; Kinematics; Motion analysis; Statistical analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing. 2002. Proceedings. 2002 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-7622-6

Type

conf

DOI

10.1109/ICIP.2002.1038015

Filename

1038015