Human Lower Limb Motion Recognition Based on Translation Invariance Wavelet Transform and RBF Neural Networks

被引：0

作者：

Wen, Shiguang ^{[1
]}

Wang, Fei ^{[1
,2
]}

Wu, Chengdong ^{[2
]}

Wang, Hao ^{[1
]}

Zhang, Yuzhong ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, Peoples R China

[2] HIT, State Key Lab Robot & Syst, Harbin 150080, Peoples R China

来源：

CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS | 2009年

关键词：

Translation Invariance; wavelet analysis; RBF neural network; lower limb; motion pattern classification;

D O I：

10.1109/CCDC.2009.5194949

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The effective de-noising of gait kinematic signals is the prerequisite and guarantee for correct recognition and diagnose. Traditional Fourier Transform and Wavelet Analysis can introduce the additional disturbance during de-noising process named Pseudo-Gibbs phenomenon. In this paper, translation invariance wavelet de-noising method is proposed to process the kinematics information acquired from inertial sensors mounted on the lower limb of human. This way, Pseudo-Gibbs phenomenon was inhibited effectively and high precision classification of human lower limb motion pattern was achieved by combining the propose de-noising method with radial-based function (RBF) neural network. Experimental results demonstrated the effectiveness and correctness of the proposed system.

引用

页码：5055 / +

页数：2