Citation

Kowalczyk P, Nema S, Glendinning P, Loram I, Brown M. Chaos Solitons Fractals 2014; 24(2): e022101.

Affiliation

School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom.

Copyright

(Copyright © 2014, Elsevier Publishing)

DOI

10.1063/1.4871880

PMID

24985413

Abstract

Linear Time Invariant (LTI) processes can be modelled by means of Auto-Regressive Moving Average (ARMA) model systems. In this paper, we examine whether an ARMA model can be fitted to a process characterised by switched nonlinearities. In particular, we conduct the following test: we generate data from known LTI and nonlinear (threshold/dead-zone) models of human balance and analyse the output using ARMA. We show that both these known systems can be fitted, according to standard criteria, with low order ARMA models. To check if there are some obvious effects of the dead-zone, we compare the power spectra of both systems with the power spectra of their ARMA models. We then examine spectral properties of three posturographic data sets and their ARMA models and compare them with the power spectra of our model systems. Finally, we examine the dynamics of our model systems in the absence of noise to determine what is the effect of the switching threshold (dead-zone) on the asymptotic dynamics.

Language: en