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Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait

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Zhang, Jie, Zhang, Kai, Feng, Jianfeng and Small, Michael. (2010) Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait. PL o S Computational Biology, Vol.6 (No.12). ISSN 1553-734X

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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1001033

Abstract

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QP Physiology
Divisions:	Faculty of Science > Centre for Scientific Computing Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Gait in humans, Neuromuscular diseases -- Diagnosis, Pattern perception, Dimension reduction (Statistics)
Journal or Publication Title:	PL o S Computational Biology
Publisher:	Public Library of Science
ISSN:	1553-734X
Date:	16 December 2010
Volume:	Vol.6
Number:	No.12
Identification Number:	10.1371/journal.pcbi.1001033
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Hong Kong Polytechnic University (HKPU), Fu dan da xue (Shanghai, China) [Fudan University]
Grant number:	G-YX0N (Hong Kong)
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URI:	http://wrap.warwick.ac.uk/id/eprint/3967