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
.