A statistical mechanical analysis of postural sway using non-Gaussian FARIMA stochastic models

In this paper, postural sway is modeled using a fractional autoregressive i ntegrated moving average (FARIMA) family of models: the center-of-pressure (COP) motion is viewed in terms of a self-similar, anti-persistent random-w alk process, obtained by fractionally summating non-Gaussian random variabl es, whose correlation structure for small time lags is shaped by a linear t ime-invariant low-pass filter. The model parameters are: the strength of the stochastic driving, e.g., the root mean square (rms) value of the time-differenced COP motion; the DC ga in, damping ratio and natural frequency of the filter; the Hurst exponent, which measures the random-walk anti-persistence magnitude. In the proposed modeling procedure, a graphical estimator for determining t he Hurst exponent is cascaded to a method for matching autoregressive (AR) models to fractionally differenced COP motion via higher order cumulants, T he effect of the presence or absence of vision on the model parameter value s is discussed with regard to data from experiments on healthy young adults .