Limits...
Transition from persistent to anti-persistent correlations in postural sway indicates velocity-based control.

Delignières D, Torre K, Bernard PL - PLoS Comput. Biol. (2011)

Bottom Line: In this paper, we discuss the conclusions drawn from previous analyses of COP dynamics using fractal-related methods.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.

View Article: PubMed Central - PubMed

Affiliation: EA 2991 Movement To Health, Montpellier-1 University, Euromov, Montpellier, France. didier.delignieres@univ-montp1.fr

ABSTRACT
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 fractal-related 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.

Show MeSH

Related in: MedlinePlus

Mean graphical results for simulated series.Average log-log diffusion plots and power spectra obtained from DFA and PSD with simulated position and velocity series. These graphs are based on point-by-point averaging of the results obtained from 26 randomly selected simulated series. The dashed line in the upper plots (DFA) represents the slope of 0.5, corresponding to the boundary between persistent and anti-persistent correlation.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3044760&req=5

pcbi-1001089-g006: Mean graphical results for simulated series.Average log-log diffusion plots and power spectra obtained from DFA and PSD with simulated position and velocity series. These graphs are based on point-by-point averaging of the results obtained from 26 randomly selected simulated series. The dashed line in the upper plots (DFA) represents the slope of 0.5, corresponding to the boundary between persistent and anti-persistent correlation.

Mentions: Figure 5 shows an example of the simulated series of position and velocity produced by the model. When the analyses previously used for the experimental series were applied to these simulated series, the model was able to account for the main statistical signatures observed experimentally: DFA and spectral analysis revealed a cross-over for velocity series, but not for position series (see Figure 6).


Transition from persistent to anti-persistent correlations in postural sway indicates velocity-based control.

Delignières D, Torre K, Bernard PL - PLoS Comput. Biol. (2011)

Mean graphical results for simulated series.Average log-log diffusion plots and power spectra obtained from DFA and PSD with simulated position and velocity series. These graphs are based on point-by-point averaging of the results obtained from 26 randomly selected simulated series. The dashed line in the upper plots (DFA) represents the slope of 0.5, corresponding to the boundary between persistent and anti-persistent correlation.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3044760&req=5

pcbi-1001089-g006: Mean graphical results for simulated series.Average log-log diffusion plots and power spectra obtained from DFA and PSD with simulated position and velocity series. These graphs are based on point-by-point averaging of the results obtained from 26 randomly selected simulated series. The dashed line in the upper plots (DFA) represents the slope of 0.5, corresponding to the boundary between persistent and anti-persistent correlation.
Mentions: Figure 5 shows an example of the simulated series of position and velocity produced by the model. When the analyses previously used for the experimental series were applied to these simulated series, the model was able to account for the main statistical signatures observed experimentally: DFA and spectral analysis revealed a cross-over for velocity series, but not for position series (see Figure 6).

Bottom Line: In this paper, we discuss the conclusions drawn from previous analyses of COP dynamics using fractal-related methods.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.

View Article: PubMed Central - PubMed

Affiliation: EA 2991 Movement To Health, Montpellier-1 University, Euromov, Montpellier, France. didier.delignieres@univ-montp1.fr

ABSTRACT
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 fractal-related 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.

Show MeSH
Related in: MedlinePlus