title: Wavelet-frequency analysis for the detection of discontinuities in switched system models of human balance.
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.
authors.affiliation: School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, UK. Electronic address: s.nema@mmu.ac.uk.
authors.forename: Salam
authors.initials: S
authors.lastname: Nema
authors.affiliation: School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, UK.
authors.forename: Piotr
authors.initials: P
authors.lastname: Kowalczyk
authors.affiliation: School of Healthcare Science, Manchester Metropolitan University, Manchester, UK.
authors.forename: Ian
authors.initials: I
authors.lastname: Loram
mesh_terms: D000465:Algorithms; D006801:Humans; D008962:Models, Theoretical; D004856:Postural Balance; D011187:Posture / Q000502:physiology; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis
keywords: Discontinuities; Instantaneous frequency; Normalized Hilbert transform; Switched systems; Wavelets