Abstract

Info

Title:: KPCA-based continuous motion estimation of knee joint in multiple motion modes

Author(s):: ZHANG Jianhua1; WANG Hao1; LI Kexiang1; 2; WANG Chang1; 2; 1. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China 2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China

Keywords:: Keywords: surface electromyography signal kernel principal component analysis time domain feature continuous motion estimation

Abstract:: Abstract: A kernel principal component analysis (KPCA) based method is proposed for the effective estimation of multi-mode continuous motion of knee joint. The time domain features of multi-dimensional surface electromyography signal are fused to obtain the comprehensive motion information in different motion modes. Then, KPCA method is used to reduce the dimensionality of EMG features and obtain the most relevant principal component vectors, and the effective estimation of multi-mode continuous motion of knee joint is realized through the combination with back propagation neural network. Experimental verification is carried out on 4 motion modes of 5 subjects. The results show that the proposed method can not only effectively estimate the multi-mode continuous motion angles of knee joint, but also significantly improve the estimation accuracy as compared with PCA algorithm.