Wavelet-frequency analysis for the detection of discontinuities in switched system models of human balance

•We provide a methodology for detecting discontinuities in time series data.•We combine the advantages of Wavelet decomposition technique and Normalized Hilbert Transform.•As a test-bed, we use a switched model of human balance control during quiet standing.•We observe peaks in the time-frequency representation of the simulated and experimental data.•Our results support the theory of intermittent control of human quiet standing.

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.