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 antipersistent 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.

The authors present a tutorial description of adaptive fractal analysis (AFA). AFA utilizes an adaptive detrending algorithm to extract globally smooth trend signals from the data and then analyzes the scaling of the residuals to the fit as a function of the time scale at which the fit is computed. The authors present applications to synthetic mathematical signals to verify the accuracy of AFA and demonstrate the basic steps of the analysis. The authors then present results from applying AFA to time series from a cognitive psychology experiment on repeated estimation of durations of time to illustrate some of the complexities of real-world data. AFA shows promise in dealing with many types of signals, but like any fractal analysis method there are special challenges and considerations to take into account, such as determining the presence of linear scaling regions.

These preliminary data indicate that those who do not later sustain an ACL injury exhibit a stronger functional connection between a cortical sensory-motor region and a cerebellar region responsible for balance and coordination. These findings may help to guide development of brain-driven biofeedback training that optimizes and promotes adaptive neuroplasticity to reduce motor coordination errors and injury risk.

Purpose The purpose of this study was to evaluate the utility of postural sway variability as a potential assessment to detect altered postural sway in youth with symptoms related to a concussion. Methods Forty participants (20 who were healthy and 20 who were injured) aged 10 to 16 years were assessed using the Balance Error Scoring System (BESS) and postural sway variability analyses applied to center-of-pressure data captured using a force plate. Results Significant differences were observed between the 2 groups for postural sway variability metrics but not for the BESS. Specifically, path length was shorter and Sample and Renyi Entropies were more regular for the participants who were injured compared with the participants who were healthy (P < .05). Conclusion The results of this study indicate that postural sway variability may be a more valid measure than the BESS to detect postconcussion alterations in postural control in young athletes.