Citation

Nema S, Kowalczyk P, Loram I. Hum. Mov. Sci. 2016; 51: 27-40.

Affiliation

School of Healthcare Science, Manchester Metropolitan University, Manchester, UK.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.humov.2016.08.002

PMID

27838506

Abstract

This paper is concerned with detecting the presence of switching behavior in experimentally obtained posturographic data sets by means of a novel algorithm that is based on a combination of wavelet analysis and Hilbert transform. As a test-bed for the algorithm, we first use a switched model of human balance control during quiet standing with known switching behavior in four distinct configurations. We obtain a time-frequency representation of a signal generated by our model system. We are then able to detect manifestations of discontinuities (switchings) in the signal as spiking behavior. The frequency of switchings, measured by means of our algorithm and detected in our models systems, agrees with the frequency of spiking behavior found in the experimentally obtained posturographic data.

Copyright © 2016. Published by Elsevier B.V.

Language: en