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.
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Wu, J., Wang, J., Liu, L. (2006). Kernel-Based Method for Automated Walking Patterns Recognition Using Kinematics Data. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_69
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DOI: https://doi.org/10.1007/11881223_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
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