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Evaluation of the temporal structure of postural sway fluctuations based on a comprehensive set of analysis tools

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  • Kirchner, M.
  • Schubert, P.
  • Schmidtbleicher, D.
  • Haas, C.T.

Abstract

The analysis of postural control has a long history. Traditionally, the amount of body sway is solely used as an index of postural stability. Although this leads to some extent to an effective evaluation of balance performance, the control mechanisms involved have not yet been fully understood. The concept of nonlinear dynamics suggests that variability in the motor output is not randomness but structure, providing the stimulus to reveal the functionality of postural sway. The present work evaluates sway dynamics by means of COP excursions in a quiet standing task versus a dual-task condition in three different test times (30, 60, 300 s). Besides the application of traditional methods–which estimate the overall size of sway–the temporal pattern of body sway was quantified via wavelet transform, multiscale entropy and fractal analysis. We found higher sensitivity of the structural parameters to modulations of postural control strategies and partly an improved evaluation of sway dynamics in longer recordings. It could be shown that postural control modifications take place on different timescales corresponding to the interplay of the sensory systems. A continued application of nonlinear analysis can help to better understand postural control mechanisms.

Suggested Citation

  • Kirchner, M. & Schubert, P. & Schmidtbleicher, D. & Haas, C.T., 2012. "Evaluation of the temporal structure of postural sway fluctuations based on a comprehensive set of analysis tools," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4692-4703.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:20:p:4692-4703
    DOI: 10.1016/j.physa.2012.05.034
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    References listed on IDEAS

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    1. Didier Delignières & Kjerstin Torre & Pierre-Louis Bernard, 2011. "Transition from Persistent to Anti-Persistent Correlations in Postural Sway Indicates Velocity-Based Control," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-10, February.
    2. Govindan, R.B. & Wilson, J.D. & Eswaran, H. & Lowery, C.L. & Preißl, H., 2007. "Revisiting sample entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 158-164.
    3. Cannon, Michael J. & Percival, Donald B. & Caccia, David C. & Raymond, Gary M. & Bassingthwaighte, James B., 1997. "Evaluating scaled windowed variance methods for estimating the Hurst coefficient of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 241(3), pages 606-626.
    4. Costa, M. & Peng, C.-K. & L. Goldberger, Ary & Hausdorff, Jeffrey M., 2003. "Multiscale entropy analysis of human gait dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 330(1), pages 53-60.
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    Cited by:

    1. Xue Pan & Lei Hou & Mutua Stephen & Huijie Yang & Chenping Zhu, 2014. "Evaluation of Scaling Invariance Embedded in Short Time Series," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-27, December.
    2. Picoli, Sergio & Bombo, Giorgio & Santos, Edenize S.D. & Deprá, Pedro P. & Mendes, Renio S., 2022. "Characterizing postural sway signals by the analysis of zero-crossing patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    3. Marietta Kirchner & Patric Schubert & Magnus Liebherr & Christian T Haas, 2014. "Detrended Fluctuation Analysis and Adaptive Fractal Analysis of Stride Time Data in Parkinson's Disease: Stitching Together Short Gait Trials," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-6, January.

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