Dimensionality and the Dynamics of Human Unstable Equilibrium

Mégrot, Fabrice; Bardy, Benoît G.; Dietrich, Gilles

doi:10.1080/00222890209601950

Cited by 14 publications

(13 citation statements)

References 11 publications

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“…These findings agree with the study of Me´grot et al (2002) on center-of-mass trajectories when standing on an unstable platform with eyes open and closed. In addition, sample entropy values did not differ with and without vision (Fig.…”

Section: Task Conditionssupporting

confidence: 83%

“…The fact that with eyes closed local stability strongly decreased indicates that vision plays a crucial role in modulating postural dynamics (e.g., Nienhuis et al 2001;Me´grot et al 2002;Geurts et al 2005). When the cognitive involvement in postural control was diminished by introducing an arithmetic dual task, the COP trajectories became less regular (sample entropy increased, see Fig.…”

Section: Task Conditionsmentioning

confidence: 99%

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Dynamical structure of center-of-pressure trajectories in patients recovering from stroke

et al. 2006

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In a recent study, De Haart et al. (Arch Phys Med Rehabil 85:886-895, 2004) investigated the recovery of balance in stroke patients using traditional analyses of center-of-pressure (COP) trajectories to assess the effects of health status, rehabilitation, and task conditions like standing with eyes open or closed and standing while performing a cognitive dual task. To unravel the underlying control processes, we reanalyzed these data in terms of stochastic dynamics using more advanced analyses. Dimensionality, local stability, regularity, and scaling behavior of COP trajectories were determined and compared with shuffled and phase-randomized surrogate data. The presence of long-range correlations discarded the possibility that the COP trajectories were purely random. Compared to the healthy controls, the COP trajectories of the stroke patients were characterized by increased dimensionality and instability, but greater regularity in the frontal plane. These findings were taken to imply that the stroke patients actively (i.e., cognitively) coped with the stroke-induced impairment of posture, as reflected in the increased regularity and decreased local stability, by recruiting additional control processes (i.e., more degrees of freedom) and/or by tightening the present control structure while releasing non-essential degrees of freedom from postural control. In the course of rehabilitation, dimensionality stayed fairly constant, whereas local stability increased and regularity decreased. The progressively less regular COP trajectories were interpreted to indicate a reduction of cognitive involvement in postural control as recovery from stroke progressed. Consistent with this interpretation, the dual task condition resulted in less regular COP trajectories of greater dimensionality, reflecting a task-related decrease of active, cognitive contributions to postural control. In comparison with conventional posturography, our results show a clear surplus value of dynamical measures in studying postural control.

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Section: Task Conditionssupporting

confidence: 83%

Section: Task Conditionsmentioning

confidence: 99%

Dynamical structure of center-of-pressure trajectories in patients recovering from stroke

et al. 2006

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“…Generally, those results offer converging evidence for the existence of self-organised phenomena operating at the level of whole body coordination and encourage further examination of the possibility that the interactions between the various components of the postural system might be addressed through the physics of nonequilibrium processes, see Megrot et al (40).…”

Section: Control Of the Postural Stabilitymentioning

confidence: 99%

“…Balance control is chalenging task for biped humans since the large mass (2/3 of body mass) of the head, arms, and trunk (HAT) is located a fair distance (2/3 of body height) over relatively small base of support (BOS). Maintaining an unstable equilibrium requires that multiple joints must be coordinated so that the COM is kept above the base of support (40). Locomotion studies have provided some insight in the undrestanding of the human locomotor control systems (37).…”

Section: Control Of the Postural Stabilitymentioning

confidence: 99%

“…Moreover, the postural stability is fundamental for every locomotion and manipulation (58). Postural stability (PS) and stabilization is becomming harder with increasing age (2,40,61), when interaction of visual, vestibular and somatosensoric systems is impaired and leads to more frequent destabilization, incertainty and falls (2,22,39,40,47,61). The degeneration of the balance control system in the elderly (28,65), and in main pathologies, has forced researchers and clinitians to understand more about how the system works and how to quantify its status at any point in time.…”

Section: Introductionmentioning

confidence: 99%

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Measuring the Balance Control System – Review

Jančová

2008

Acta Med. (Hradec Kralove, Czech Repub.)

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Summary: Past studies of postural control during standing have employed wide range of procedures including the outcome measures use to quantify postural control, the duration of the sample collected, sampling frequency and methods for data processing. Due to these differences there remains little, if any, common grounds for comparisons between studies to establish a concrete understanding of the features and bouns which characterize normal healthy postural control. This article deals with terms such as reliability and repeatability of stabilometric measurements, stabilometric data quantification and analysis. To clear up those terms is suggested, by the author of this paper, very important. The stabilometric measurements remain, nevertheless, different when dealing with aging adults. Though, we notes some alterations of the aging systems, this article is not entirely dedicated to the seniors population. Measurements of COP and technical notes remain the main axis of present paper.

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Lyapunov Exponents

Dingwell¹

2006

Wiley Encyclopedia of Biomedical Engineering

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The analysis of potentially chaotic behavior in biological and biomedical phenomena has attracted increased attention in recent years. Chaotic systems exhibit a trademark “sensitive dependence on initial conditions” (SDIC), meaning that very small perturbations will cause the resulting trajectories to separate exponentially fast. Lyapunov exponents directly measure SDIC by quantifying the exponential rates at which neighboring orbits on an attractor diverge (or converge) as the system evolves in time. Dissipative deterministic systems that exhibit at least one positive Lyapunov exponent are by definition “chaotic.” Hence, Lyapunov exponents are considered one of the most useful diagnostic tools available for analyzing potentially chaotic dynamical systems. However, Lyapunov exponents are also notoriously difficult to estimate reliably from experimental data and one should always be very skeptical about accepting any claims of “chaos” based solely on findings of positive Lyapunov exponents alone. This article reviews the basic definitions of Lyapunov exponents and outlines several of the more widely disseminated algorithms available to estimate them from experimental time series data. Critical issues related to finding spurious exponents, analyzing noisy signals, and validating against surrogate data are reviewed. A brief discussion of how Lyapunov exponents have been applied to study a wide variety of biomedical and biological phenomena is also presented. Finally, some thoughts on appropriate interpretations of such findings and the future directions for using Lyapunov exponents to study potentially chaotic systems are presented.

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Dimensionality and the Dynamics of Human Unstable Equilibrium

Cited by 14 publications

References 11 publications

Dynamical structure of center-of-pressure trajectories in patients recovering from stroke

Dynamical structure of center-of-pressure trajectories in patients recovering from stroke

Measuring the Balance Control System – Review

Lyapunov Exponents

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