Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5829
Title: Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait
Authors: Zhang, J
Zhang, K
Feng, J
Small, M
Keywords: Time-series analysis
Fractal dynamics
Granger causality
Complex networks
Stride-interval
Nystrom method
Human walking
Systems
Disease
FMRI
Issue Date: 16-Dec-2010
Publisher: Public Library of Science (PLoS)
Source: PLoS computational biology, 16 Dec., 2010, v. 6, no. 12, e1001033, p. 1-11 How to cite?
Journal: PLoS computational biology 
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.
URI: http://hdl.handle.net/10397/5829
ISSN: 1513-7368 (online)
DOI: 10.1371/journal.pcbi.1001033
Rights: This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
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