Manchester Metropolitan University
Complexity and dynamics of switched human balance control systems with noise
Human neuromotor control system during quiet or perturbed standing, or object tracking, can be modelled by means of switched systems with noise, and time delay in the switching function. We link the dynamics of such systems with complexity measures such as Sample Entropy and Detrended Fluctuation Analyses. In particular, we seek to establish what is the effect of the presence of discontinuities on the complexity measures which we consider. We then link the results with complexity measures found in experimental data of human sway motion during quiet standing. Finally, we analyse model-generated and experimental time series data sets in view of determining a pattern of control strategy triggered by the presence of intermittent control (switchings) by means of an algorithm that relies on the combination of wavelet analysis and normalised Hilbert transform. We obtain a time-frequency representation of a signal generated by our model system. We are then able to detect manifestations of discontinuities in the signal as spiking behaviour. We show that similar spikes can be detected by analysing experimental posturographic data sets.
This is joint work with Dr. Salam Nema and Prof. Ian Loram.