DocumentCode
2512273
Title
Realtime gait kinematics classification using LDA and SVM
Author
Wen, Shiguang ; Wang, Fei ; Wu, Chengdong
fYear
2011
fDate
23-25 May 2011
Firstpage
592
Lastpage
595
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location
Mianyang
Print_ISBN
978-1-4244-8737-0
Type
conf
DOI
10.1109/CCDC.2011.5968250
Filename
5968250
Link To Document