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Nonlinear time series analysis of normal and pathological human walking

Chaos 10, 848 (2000); doi:10.1063/1.1324008

Issue Date: December 2000

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Jonathan B. Dingwell
Sensory Motor Performance Program, Rehabilitation Institute of Chicago, 345 E. Superior Street, Chicago, Illinois 60611

Joseph P. Cusumano
Department of Engineering Science and Mechanics, Penn State University, 131 Hammond Building, University Park, Pennsylvania 16802
Characterizing locomotor dynamics is essential for understanding the neuromuscular control of locomotion. In particular, quantifying dynamic stability during walking is important for assessing people who have a greater risk of falling. However, traditional biomechanical methods of defining stability have not quantified the resistance of the neuromuscular system to perturbations, suggesting that more precise definitions are required. For the present study, average maximum finite-time Lyapunov exponents were estimated to quantify the local dynamic stability of human walking kinematics. Local scaling exponents, defined as the local slopes of the correlation sum curves, were also calculated to quantify the local scaling structure of each embedded time series. Comparisons were made between overground and motorized treadmill walking in young healthy subjects and between diabetic neuropathic (NP) patients and healthy controls (CO) during overground walking. A modification of the method of surrogate data was developed to examine the stochastic nature of the fluctuations overlying the nominally periodic patterns in these data sets. Results demonstrated that having subjects walk on a motorized treadmill artificially stabilized their natural locomotor kinematics by small but statistically significant amounts. Furthermore, a paradox previously present in the biomechanical literature that resulted from mistakenly equating variability with dynamic stability was resolved. By slowing their self-selected walking speeds, NP patients adopted more locally stable gait patterns, even though they simultaneously exhibited greater kinematic variability than CO subjects. Additionally, the loss of peripheral sensation in NP patients was associated with statistically significant differences in the local scaling structure of their walking kinematics at those length scales where it was anticipated that sensory feedback would play the greatest role. Lastly, stride-to-stride fluctuations in the walking patterns of all three subject groups were clearly distinguishable from linearly autocorrelated Gaussian noise. As a collateral benefit of the methodological approach taken in this study, some of the first steps at characterizing the underlying structure of human locomotor dynamics have been taken. Implications for understanding the neuromuscular control of locomotion are discussed. ©2000 American Institute of Physics.
History: Received 2 December 1999; accepted 19 September 2000
Permalink: http://link.aip.org/link/?CHAOEH/10/848/1
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KEYWORDS and PACS

Keywords
PACS
  • 87.19.St
    Biological and medical physics Properties of higher organisms Movement and locomotion
  • 05.45.Tp
    Statistical physics, thermodynamics, and nonlinear dynamical systems Nonlinear dynamics and nonlinear dynamical systems Time series analysis
  • 05.40.Ca
    Statistical physics, thermodynamics, and nonlinear dynamical systems Fluctuation phenomena, random processes, noise, and Brownian motion Noise
  • YEAR: 2000

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PUBLICATION DATA

ISSN:
1054-1500 (print)   1089-7682 (online)
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