Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance–based therapy (original) (raw)
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Characterizing the human postural control system using detrended fluctuation analysis
Journal of Computational and Applied Mathematics, 2010
Detrended fluctuation analysis is used to study the behaviour of the time series of the position of the center of pressure, output from the activity of a human postural control system. The results suggest that these trajectories present a crossover in their scaling properties from persistent (for high frequencies, short-range time scale) to anti-persistent (for low frequencies, long-range time scale) behaviours. The values of the scaling exponent found for the persistent parts of the trajectories are very similar for all the cases analysed. The similarity of the results obtained for the measurements done with both eyes open and both eyes closed indicate either that the visual system may be disregarded by the postural control system, while maintaining quiet standing, or that the control mechanisms associated with each type of information (visual, vestibular and somatosensory) cannot be disentangled with this technique.
Identification of dynamic patterns of body sway during quiet standing: Is it a nonlinear process?
… of Bifurcation and …, 2010
During quiet standing, the human body continuously moves about an upright posture in an erratic fashion. Many researchers characterize postural fluctuations as a stochastic process while some others suggest chaotic dynamics for postural sway. In this study, first we examined these assumptions using principles of chaos theory in normal healthy and in patients with deteriorated postural control mechanisms. Next, we compared the ability of a nonlinear dynamics quantifier correlation dimension to that of a linear measure standard deviation to describe variability of healthy and deteriorated postural control mechanisms during quiet standing. Our findings did not provide convincing evidence for existence of low dimensional chaos within normal and abnormal sway dynamics but support the notion that postural fluctuations time series are distinguishable from these generated by a random process. The results indicated that although linear variability measures discriminated well between groups, they did not provide any information about the structure of postural fluctuations. Calculated correlation dimension as a complexity measure which describes spatio temporal organization of time series may be useful in this regard.
A Correlation-Based Framework for Evaluating Postural Control Stochastic Dynamics
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016
The inability to maintain balance during varying postural control conditions can lead to falls, a significant cause of mortality and serious injury among older adults. However, our understanding of the underlying dynamical and stochastic processes in human postural control have not been fully explored. To further our understanding of the underlying dynamical processes, we examine a novel conceptual framework for studying human postural control using the center of pressure (COP) velocity autocorrelation function (COP-VAF) and compare its results to Stabilogram Diffusion Analysis (SDA). Eleven healthy young participants were studied under quiet unipedal or bipedal standing conditions with eyes either opened or closed. COP trajectories were analyzed using both the traditional posturographic measure SDA and the proposed COP-VAF. It is shown that the COP-VAF leads to repeatable, physiologically meaningful measures that distinguish postural control differences in unipedal versus bipedal stance trials with and without vision in healthy individuals. More specifically, both a unipedal stance and lack of visual feedback increased initial values of the COP-VAF, magnitude of the first minimum, and diffusion coefficient, particularly in contrast to bipedal stance trials with open eyes. Use of a stochastic postural control model, based on an Ornstein-Uhlenbeck process that accounts for natural weightshifts, suggests an increase in spring constant and decreased damping coefficient when fitted to experimental data. This work suggests that we can further extend our understanding of the underlying mechanisms behind postural control in quiet stance under varying stance conditions using the COP-VAF and provides a tool for quantifying future neurorehabilitative interventions.
From stochasticism to determinism in evaluation of human postural responses
2017 21st International Conference on Process Control (PC), 2017
The Center of Pressure (COP) signal is a kind of human postural response and it is an established indicator of human ability to maintain balanced posture. Its form of the statokinesigram has complicated profile, which suggests stochastic or chaotic nature of COP movement. Here is presented developed statokinesigram trajectory (DST) as a basis of method for human postural response analysis. Since DST does not show signs of stochastic behavior it is suitable for modeling with help of linear system theory. In this study, volunteer's postural responses were affected by bilateral vibration stimuli of Achilles tendons. This vibration stimulus causes nonlinear response in anterior-posterior direction. DST allows to analyze this phenomenon through mathematical model in form of a transfer function. Its estimated parameters are useful in evaluation of human posture control.
PLoS Computational Biology, 2011
The displacement of the center-of-pressure (COP) during quiet stance has often been accounted for by the control of COP position dynamics. In this paper, we discuss the conclusions drawn from previous analyses of COP dynamics using fractalrelated methods. On the basis of some methodological clarification and the analysis of experimental data using stabilogram diffusion analysis, detrended fluctuation analysis, and an improved version of spectral analysis, we show that COP velocity is typically bounded between upper and lower limits. We argue that the hypothesis of an intermittent velocity-based control of posture is more relevant than position-based control. A simple model for COP velocity dynamics, based on a bounded correlated random walk, reproduces the main statistical signatures evidenced in the experimental series. The implications of these results are discussed.
Correlation dimension estimates of human postural sway
Human postural sway during quiet standing demonstrates a complex structured dynamics, which has been studied by applying numerous methods, such as linear system identification methods, stochastic analysis, and nonlinear system dynamics tools. Although each of the methods applied revealed some particular features of the sway data none of them have succeeded to present a global picture of the quiet stance dynamics, which probably has both sto-chastic and deterministic properties. In this study we have started applying ergodic theory of dynamical systems to explore statistical characteristic of the sway dynamics observed in successive trials of a subject, different subjects in an age group, and finally different age groups constituted by children, adults, and elderly subjects. Five successive 180-s long trials were performed by each of 28 subjects in four age groups at quiet stance with eyes open. Stationary and ergodic signal characteristics of five successive center of pressure time series collected from a subject in ante-ro-posterior direction (CoP x) were examined. 97% of the trials were found to be stationary by applying Run Test while children and elderly groups demonstrated significant nonstationary behavior. On the other hand 13 out of 24 subjects were found to be nonerg-odic. We expected to observe differences in complexity of CoP x dynamics due to aging (Farmer, Ott, & Yorke, 1983). However linear metrics such as standard deviation and Fourier spectra of CoP x signals did not show differences due to the age groups. Correlation dimension (D k) estimates of stationary CoP x signals being an invariant measure of nonlinear system dynamics were computed by using the average displacement method (Eckmann & Ruelle,
Evaluation of nonlinear dynamics in postural steadiness time series
Annals of Biomedical Engineering, 1995
Fractal and correlation dimensions have been computed for time series obtained from tests of balance (postural steadiness). Although these measures appear to be reliable and differentiate subject groups, it has become clear that random (noise) time series may have finite dimensions and appear to demonstrate dynamics characteristic of nonlinear systems. Consequently, it is necessary to apply a test to distinguish a time series with putative nonlinear dynamics from random noise. A simple predictor was utilized to compare center of pressure (COP) time series with surrogate data constructed to have similar time and frequency domain characteristics. It was found that the original time series was more predictable than the surrogate data, suggesting that the COP data is derived from a nonlinear system.