Recognizing knee pathologies by using gait dynamics via kernel principal component analysis and deterministic learning theory-Reference-Cited by-同舟云学术

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Recognizing knee pathologies by using gait dynamics via kernel principal component analysis and deterministic learning theory

Published:2018-06-05 Issue: Volume: Page:
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Deng Muqing^ORCID,Feng Xiaoreng,Zeng Wei,Cao Jiuwen,Zhang Yu,Zheng Tongjia

Funder

National R & D Program for Major Research Instruments

National Natural Science Foundation of China

Key research grant for national fitness from General Administration of Sports of China

Guangdong Natural Science Foundation

111 Project

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s12652-018-0890-4.pdf

Reference28 articles.

1. Armand S, Watelain E, Mercier M, Lensel G, Lepoutre F (2007) Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees. Gait Posture 25:475–484

2. Baker R (2006) Gait analysis methods in rehabilitation. J Neuroeng Rehabil 3(1):4

3. Barton G, Lisboa P, Lees A, Attfield S (2007) Gait quality assessment using selforganising artificial neural networks. Gait Posture 25:374–379

4. Barton G, Hawken M, Scott M, Schwartz M (2012) Movement deviation profile: a measure of distance from normality using a self-organizing neural network. Hum Mov Sci 31:284–294

5. Begg R, Palaniswami M, Owen B (2005) Support vector machines for automated gait classification. IEEE Trans Biomed Eng 1:52–65

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