PCA-Based SVM for Automatic Recognition of Gait Patterns

in Journal of Applied Biomechanics
Restricted access

Purchase article

USD  $24.95

Student 1 year subscription

USD  $87.00

1 year subscription

USD  $116.00

Student 2 year subscription

USD  $165.00

2 year subscription

USD  $215.00

In this technical note, we investigate a combination PCA with SVM to classify gait pattern based on kinetic data. The gait data of 30 young and 30 elderly participants were recorded using a strain gauge force platform during normal walking. The gait features were first extracted from the recorded vertical directional foot– ground reaction forces curve using PCA, and then these extracted features were adopted to develop the SVM gait classifier. The test results indicated that the performance of PCA-based SVM was on average 90% to recognize young– elderly gait patterns, resulting in a markedly improved performance over an artificial neural network–based classifier. The classification ability of the SVM with polynomial and radial basis function kernels was superior to that of the SVM with linear kernel. These results suggest that the proposed technique could provide an effective tool for gait classification in future clinical applications.

The authors are with the Key Laboratory of Biomedical Information Engineering, Education Ministry, Xi’an Jiaotong University, Xi’an, China.

All Time Past Year Past 30 Days
Abstract Views 46 46 2
Full Text Views 3 3 1
PDF Downloads 6 6 2