Title :
Realtime gait kinematics classification using LDA and SVM
Author :
Wen, Shiguang ; Wang, Fei ; Wu, Chengdong
Abstract :
Gait analysis is an important part of intelligent prosthesis researching. The automatic segmentation and classification of gait kinematic signal could help intelligent prosthesis to get better control performance. Much feature extraction method was employed by researchers, but it could still be improved. The wavelet based filter is adopted in this paper to segment the gait kinematics data, and LDA/SVM is employed to improve the accuracy of recognition. Result shows better performance than that using traditional method.
Keywords :
feature extraction; filtering theory; gait analysis; kinematics; medical signal processing; prosthetics; signal classification; support vector machines; wavelet transforms; LDA; SVM; control performance; feature extraction method; gait kinematic signal classification; gait kinematic signal segmentation; gait kinematics data segmentation; intelligent prosthesis researching; wavelet based filter; Acceleration; Accuracy; Classification algorithms; Feature extraction; Kinematics; Principal component analysis; Support vector machines; Gait kinematics LDA SVM Feature extraction;
Conference_Titel :
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location :
Mianyang
Print_ISBN :
978-1-4244-8737-0
DOI :
10.1109/CCDC.2011.5968250
