System identification
Nonlinear time series analysis
Nonlinear time series analysis
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The output of the human postural control system is studied by means of linear system theory. It is assumed that the act of maintaining an erect posture can be treated as an autoregressive system with white noise on input. The identification is performed on the basis of the centre of pressure trajectory and it is shown that the most important features of the postural signal are sufficiently well reproduced by a low order linear autoregressive model. It is shown that the parameters of a model depend on the human subject and in some way characterize his state. Poles of the transmitancy function lying close to the unit circle are discussed as parameters describing the response function and human reactions to external perturbations. In additionf, an analysis of the correlation function within the autoregressive model is performed and its scaling exponents are computed and compared to the experimental results.