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

  • Jianning Wu
  • Jue Wang
  • Li Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


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.


Support Vector Machine Joint Angle Gait Pattern Generalization Performance Kernel Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jianning Wu
    • 1
  • Jue Wang
    • 1
  • Li Liu
    • 1
  1. 1.Key Laboratory of Biomedical Information Engineering of Education MinistryXi’an Jiaotong UniversityXi’anChina

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