NDLI: A novel approach for discrimination of human gait using kernel learning algorithm

Content Provider	IEEE Xplore Digital Library
Author	Jianning Wu
Copyright Year	2010
Description	Author affiliation: School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, China (Jianning Wu)
Abstract	The discrimination of human gait change has important role in clinical and rehabilitation contexts. In order to avoid limitations of the quantitative assessment of the change of human gait such as the loss of the useful gait feature information and the lower sensitivity, kernel-based linear discriminant analysis (LDA) for recognizing change of human gait was investigated. The gait data of two groups of participants including young and old peoples respectively were acquired during normal walking, and were analyzed using the proposed method. Experimental results indicated that the proposed technique could obtain more useful gait feature information related to human gait than LDA, and identify gait patterns with 87.5% accuracy, demonstrating that the proposed technique could be used as an effective tool for the discrimination of human gait in the future clinical context.
Starting Page	3253
Ending Page	3256
File Size	237341
Page Count	4
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5582578
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy kernel-based LDA Humans gait clasification Polynomials Eigenvalues and eigenfunctions Pattern recognition Classification algorithms Kernel gait analysis
Content Type	Text
Resource Type	Article

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