Human Lower Limb Motion Recognition Based on Translation Invariance Wavelet Transform and RBF Neural Networks
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.
Translation Invariance wavelet analysis RBF neural network lower limb motion pattern classification
Shiguang Wen Fei Wang Chengdong Wu Hao Wang Yuzhong Zhang
College of Information Science and Engineering,Northeastern University,Shenyang 110004 College of Information Science and Engineering,Northeastern University,Shenyang 110004;State Key Lab State Key Laboratory of Robotics and System (HIT),Harbin 150080
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5055-5058
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）