Experimental Brain Research

, Volume 236, Issue 5, pp 1491–1500 | Cite as

Transitions in persistence of postural dynamics depend on the velocity and structure of postural perturbations

  • Troy J. Rand
  • Mukul Mukherjee
Research Article


The sensorimotor system prefers sway velocity information when maintaining upright posture. Sway velocity has a unique characteristic of being persistent on a short time-scale and anti-persistent on a longer time-scale. The time where the transition from persistence to anti-persistence occurs provides information about how sway velocity is controlled. It is, however, not clear what factors affect shifts in this transition point. This research investigated postural responses to support surface movements of different temporal correlations and movement velocities. Participants stood on a force platform that was translated according to three different levels of temporal correlation. White noise had no correlation, pink noise had moderate correlation, and sine wave movements had very strong correlation. Each correlation structure was analyzed at five different average movement velocities (0.5, 1.0, 2.0, 3.0, and 4.0 cm·s−1), as well as one trial of quiet stance. Center of pressure velocity was analyzed using fractal analysis to determine the transition from persistent to anti-persistent behavior, as well as the strength of persistence. As movement velocity increased, the time to transition became longer for the sine wave and shorter for the white and pink noise movements. Likewise, during the persistent time-scale, the sine wave resulted in the strongest correlation, while white and pink noise had weaker correlations. At the highest three movement velocities, the strength of persistence was lower for the white noise compared to pink noise movements. These results demonstrate that the predictability and velocity of support surface oscillations affect the time-scale threshold between persistent and anti-persistent postural responses. Consequently, whether a feedforward or feedback control is utilized for appropriate postural responses may also be determined by the predictability and velocity of environmental stimuli. The study provides new insight into flexibility and adaptability in postural control. This information has implications for the design of rehabilitative protocols in neuromuscular control.


Feedforward Feedback Fractal Temporal correlation Detrended fluctuation analysis Crossover 



This study was supported by the Center of Biomedical Research Excellence Grant (1P20GM109090-01) from NIGMS/NIH and a NASA Nebraska EPSCoR Research mini-grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NASA or the NIH.


  1. Asslander L, Hettich G, Mergner T (2015) Visual contribution to human standing balance during support surface tilts. Hum Mov Sci 41:147–164. CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bak P, Paczuski M (1995) Complexity, contingency, and criticality. Proc Natl Acad Sci USA 92:6689–6696CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bhattacharya J, Edwards J, Mamelak AN, Schuman EM (2005) Long-range temporal correlations in the spontaneous spiking of neurons in the hippocampal-amygdala complex of humans. Neuroscience 131:547–555. CrossRefPubMedGoogle Scholar
  4. Buchanan JJ, Horak FB (1999) Emergence of postural patterns as a function of vision and translation frequency. J Neurophysiol 81:2325–2339CrossRefPubMedGoogle Scholar
  5. Buchanan J, Horak F (2001) Transitions in a postural task: do the recruitment and suppression of degrees of freedom stabilize posture? Exp Brain Res 139:482–494. CrossRefPubMedGoogle Scholar
  6. Collins JJ, De Luca CJ (1993) Open-loop and closed-loop control of posture: a random-walk analysis of center-of-pressure trajectories. Exp Brain Res 95:308–318CrossRefPubMedGoogle Scholar
  7. Corna S, Tarantola J, Nardone A, Giordano A, Schieppati M (1999) Standing on a continuously moving platform: is body inertia counteracted or exploited? Exp Brain Res 124:331–341. CrossRefPubMedGoogle Scholar
  8. Damouras S, Chang MD, Sejdic E, Chau T (2010) An empirical examination of detrended fluctuation analysis for gait data. Gait Posture 31:336–340. CrossRefPubMedGoogle Scholar
  9. Delignieres D, Deschamps T, Legros A, Caillou N (2003) A methodological note on nonlinear time series analysis: Is the open- and closed-loop model of Collins and De Luca (1993) a statistical artifact? J Motor Behav 35:86–96CrossRefGoogle Scholar
  10. Delignieres D, Torre K (2009) Fractal dynamics of human gait: a reassessment of the 1996 data of Hausdorff et al. J Appl Physiol 106:1272–1279. CrossRefPubMedGoogle Scholar
  11. Delignieres D, Torre K, Bernard PL (2011) Transition from persistent to anti-persistent correlations in postural sway indicates velocity-based control. PLoS Comput Biol PubMedPubMedCentralGoogle Scholar
  12. Duarte M, Zatsiorsky VM (2000) On the fractal properties of natural human standing. Neurosci Lett 283:173–176CrossRefPubMedGoogle Scholar
  13. Gahéry Y, Massion J (1981) Co-ordination between posture and movement. Trends Neurosci 4:199–202CrossRefGoogle Scholar
  14. Goldberger AL, West BJ (1987) Fractals in physiology and medicine Yale. J Biol Med 60:421–435Google Scholar
  15. Goldberger AL, Amaral LA, Hausdorff JM, Ivanov P, Peng CK, Stanley HE (2002) Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci USA 99(Suppl 1):2466–2472. CrossRefPubMedPubMedCentralGoogle Scholar
  16. Gorman JC, Amazeen PG, Cooke NJ (2010) Team coordination dynamics. Nonlinear Dynamics Psychol Life Sci 14:265–289. PubMedGoogle Scholar
  17. Hausdorff JM, Purdon PL, Peng CK, Ladin Z, Wei JY, Goldberger AL (1996) Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations. J Appl Physiol (1985) 80:1448–1457CrossRefGoogle Scholar
  18. Jeka J, Kiemel T, Creath R, Horak F, Peterka R (2004) Controlling human upright posture: velocity information is more accurate than position or acceleration. J Neurophysiol 92:2368–2379. CrossRefPubMedGoogle Scholar
  19. Ko JH, Challis JH, Newell KM (2013) Postural coordination patterns as a function of rhythmical dynamics of the surface of support. Exp Brain Res 226:183–191. CrossRefPubMedGoogle Scholar
  20. Liebovitch LS, Yang W (1997) Transition from persistent to antipersistent correlation in biological systems. Phys Rev E 56:4557CrossRefGoogle Scholar
  21. Likens AD, Fine JM, Amazeen EL, Amazeen PG (2015) Experimental control of scaling behavior: what is not fractal? Exp Brain Res. PubMedGoogle Scholar
  22. Macpherson J, Horak F (2013) Posture principles of neural science. McGraw-Hill, New York, p 5Google Scholar
  23. Mandelbrot BB (1982) The fractal geometry of nature. W.H. Freeman and Company, New York CityGoogle Scholar
  24. Nardone A, Grasso M, Tarantola J, Corna S, Schieppati M (2000) Postural coordination in elderly subjects standing on a periodically moving platform. Arch Phys Med Rehabil 81:1217–1223. CrossRefPubMedGoogle Scholar
  25. Peng CK, Havlin S, Stanley HE (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5:82CrossRefPubMedGoogle Scholar
  26. Peterka RJ (2000) Postural control model interpretation of stabilogram diffusion analysis. Biol Cybern 82:335–343. CrossRefPubMedGoogle Scholar
  27. Rand TJ, Myers SA, Kyvelidou A, Mukherjee M (2015) Temporal structure of support surface translations drive the temporal structure of postural control during standing. Ann Biomed Eng 43:2699–2707. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Stergiou N, Decker LM (2011) Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci 30:869–888. CrossRefPubMedPubMedCentralGoogle Scholar
  29. Van Orden GC, Holden JG, Turvey MT (2003) Self-organization of cognitive performance. J Exp Psychol Gen 132:331–350. CrossRefPubMedGoogle Scholar
  30. Van Orden GC, Holden JG, Turvey MT (2005) Human cognition and 1/f scaling. J Exp Psychol Gen 134:117–123. CrossRefPubMedGoogle Scholar
  31. van der Kooij H, de Vlugt E (2007) Postural responses evoked by platform pertubations are dominated by continuous feedback. J Neurophysiol 98:730–743. CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of BiomechanicsUniversity of Nebraska at OmahaOmahaUSA

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