Long-Range Correlations in Stride Intervals May Emerge from Non-Chaotic Walking Dynamics
Author(s)Ahn, Jooeun; Hogan, Neville
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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.
DepartmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology. Department of Mechanical Engineering
Public Library of Science
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.
Final published version