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Mathematical Problems in Engineering
Volume 2013, Article ID 143435, 12 pages
Research Article

Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2School of Mechanical Engineering, Tsinghua University, Beijing 100084, China

Received 11 August 2013; Accepted 11 September 2013

Academic Editor: Gelan Yang

Copyright © 2013 Zhaoqin Peng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Algorithms based on the ground reflex pressure (GRF) signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA) and kernel principal component analysis (KPCA) for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM), SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.