title: Adaptive fractal analysis reveals limits to fractal scaling in center of pressure trajectories.
abstract: Fractal time series analysis methods are commonly used for analyzing center of pressure (COP) signals with the goal of revealing the underlying neuromuscular processes for upright stance control. The use of fractal methods is often coupled with the assumption that the COP is an instance of fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). Our purpose was to evaluate the applicability of the fGn-fBm framework to the COP in light of several characteristics of COP signals revealed by a new method, adaptive fractal analysis (AFA). AFA quantifies how the variance of the residuals to fits of a globally smooth trend signal scales with the time scale at which the fits are performed. Application of AFA to COP signals revealed that there are potentially three fractal scaling regions in the COP as opposed to one as expected from a pure fGn or fBm process. The scaling region at the fastest scale was anti-persistent and spanned ~30-90 ms, the intermediate was persistent and spanned ~200 ms-1.9 s, and the slowest was anti-persistent and spanned ~5-40 s. The intermediate fractal scaling region was the most clearly defined, but it only contributed around 11% of the total spectral energy of the COP signal, indicating that other features of the COP signal contribute more importantly to the overall dynamics. Also, more than half of the Hurst exponents estimated for the intermediate region were greater than the theoretically expected range [0,1] for fGn-fBm processes. These results suggest the fGn-fBm framework is not appropriate for modeling COP signals. ON-OFF intermittency might provide a better modeling framework for the COP, and multiscale approaches may be more appropriate for analyzing COP data.
authors.affiliation: Center for Cognition, Action, & Perception, Department of Psychology, University of Cincinnati, Cincinnati, OH 45221-0376, USA.
authors.forename: Nikita
authors.initials: N
authors.lastname: Kuznetsov
authors.forename: Scott
authors.initials: S
authors.lastname: Bonnette
authors.forename: Jianbo
authors.initials: J
authors.lastname: Gao
authors.forename: Michael A
authors.initials: MA
authors.lastname: Riley
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D000293:Adolescent; D000328:Adult; D003198:Computer Simulation; D005260:Female; D005528:Foot / Q000502:physiology*; D017709:Fractals; D006801:Humans; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D009068:Movement / Q000502:physiology*; D004856:Postural Balance / Q000502:physiology*; D011187:Posture / Q000502:physiology*; D011312:Pressure; D011597:Psychomotor Performance / Q000502:physiology*; D055815:Young Adult