Kernel-Based Method for Automated Walking Patterns Recognition Using Kinematics Data

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, suggesting that K-SVM can be applied as an efficient gait classifier for young and elderly gait patterns.

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.