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Recognition of Gait Patterns Using Support Vector Machines

Recognition of Gait Patterns Using Support Vector Machines

Rezaul Begg, Marimuthu Palaniswami
ISBN13: 9781591408369|ISBN10: 1591408369|EISBN13: 9781591408383
DOI: 10.4018/978-1-59140-836-9.ch008
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MLA

Begg, Rezaul, and Marimuthu Palaniswami. "Recognition of Gait Patterns Using Support Vector Machines." Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, edited by Rezaul Begg and Marimuthu Palaniswami, IGI Global, 2006, pp. 243-262. https://doi.org/10.4018/978-1-59140-836-9.ch008

APA

Begg, R. & Palaniswami, M. (2006). Recognition of Gait Patterns Using Support Vector Machines. In R. Begg & M. Palaniswami (Eds.), Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques (pp. 243-262). IGI Global. https://doi.org/10.4018/978-1-59140-836-9.ch008

Chicago

Begg, Rezaul, and Marimuthu Palaniswami. "Recognition of Gait Patterns Using Support Vector Machines." In Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, edited by Rezaul Begg and Marimuthu Palaniswami, 243-262. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-836-9.ch008

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Abstract

Automated gait pattern recognition capability has many advantages. For example, it can be used for the detection of at-risk or faulty gait, or for monitoring the progress of treatment effects. In this chapter, we first provide an overview of the major automated techniques for detecting gait patterns. This is followed by a description of a gait pattern recognition technique based on a relatively new machine-learning tool, support vector machines (SVM). Finally, we show how SVM technique can be applied to detect changes in the gait characteristics as a result of the ageing process and discuss their suitability as an automated gait classifier.

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