Kernel-Based method for automated walking patterns recognition using kinematics data

Authors:
Jianning Wu;Jue Wang;Li Liu
Affiliations:
Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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Abstract

A novel scheme is proposed for training Support Vector Machines (SVMs) in automatic recognition of young-old gait types with a higher accuracy. Kernel-based Principal Component Analysis (KPCA) is employed to initiate the training set, which efficiently extracts more nonlinear features from highly correlated time-dependent gait variables and improves the generalization performance of SVM. With the proposed method (abbreviated K-SVM), the gait patterns of 24 young and 24 elderly normal participants were analyzed. Cross-validation test results show that the generalization performance of K-SVM was on average 89.6% to identify young and elderly gait patterns, compared with that of PCA-based SVM 83.3%, SVM 81.3% and a neural network 75.0%. These results suggest that K-SVM can be applied as an efficient gait classifier for young and elderly gait patterns.