Long-Range Correlations in Stride Intervals May Emerge from Non-Chaotic Walking Dynamics
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Ahn-2013-Long-range correlati.pdf
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Author(s)
Ahn, Jooeun
Hogan, Neville
Date Issued
September 2013
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Ahn, Jooeun, and Neville Hogan. “Long-Range Correlations in Stride Intervals May Emerge from Non-Chaotic Walking Dynamics.” Edited by Ramesh Balasubramaniam. PLoS ONE 8, no. 9 (September 23, 2013): e73239.
Version
Final published version
Abstract
Stride intervals of normal human walking exhibit long-range temporal correlations. Similar to the fractal-like behaviors observed in brain and heart activity, long-range correlations in walking have commonly been interpreted to result from chaotic dynamics and be a signature of health. Several mathematical models have reproduced this behavior by assuming a dominant role of neural central pattern generators (CPGs) and/or nonlinear biomechanics to evoke chaos. In this study, we show that a simple walking model without a CPG or biomechanics capable of chaos can reproduce long-range correlations. Stride intervals of the model revealed long-range correlations observed in human walking when the model had moderate orbital stability, which enabled the current stride to affect a future stride even after many steps. This provides a clear counterexample to the common hypothesis that a CPG and/or chaotic dynamics is required to explain the long-range correlations in healthy human walking. Instead, our results suggest that the long-range correlation may result from a combination of noise that is ubiquitous in biological systems and orbital stability that is essential in general rhythmic movements.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0073239
