Correlation dimension estimates of human postural sway

Gürses, Senih; Çelik, Hüseyin

doi:10.1016/j.humov.2012.07.007

Cited by 14 publications

(18 citation statements)

References 36 publications

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“…Several studies have introduced nonlinear time series analysis methods to study the stochastic and dynamic characteristics of human postural control [14]–[23]. Stochastic activity of the postural control system has been shown to be sensitive to altered visual conditions, aging, or neurological disorders [24]–[31]. Methods such as the Stabilogram Diffusion Analysis (SDA) [15], [24], [32], [33] assume that the COP during quiet stance can be modeled as a system of coupled, correlated random walks with short-term and long-term scaling exponents that do not change over time.…”

Section: Introductionmentioning

confidence: 99%

A Correlation-Based Framework for Evaluating Postural Control Stochastic Dynamics

Hernandez

Snider²,

Stevenson

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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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 weight-shifts, 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.

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Section: Introductionmentioning

confidence: 99%

A Correlation-Based Framework for Evaluating Postural Control Stochastic Dynamics

Hernandez

Snider²,

Stevenson

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Taking the aforementioned dilemma into account, prior to any nonlinear feature extraction, the behavior of COP dataset was investigated in terms of determinism or stochasticity by examining their correlation dimension (Dc) versus embedding dimension (m) curve [15]. More specifically, the dynamic of a nonlinear system is usually represented in a m-dimensional phase space, that is, a Cartesian coordinate system with m axes.…”

Section: Investigation Of the Cop Data Behaviormentioning

confidence: 99%

“…Moreover, the correlation dimension estimates the "density" (or dimensionality) of the system's attractor [5] and, consequently, the Dc versus m curve of a given system shows the changes in the density of its attractor embedded at different m-dimensional phase spaces. Importantly, for deterministic systems, Dc typically attains a saturation level with the m increment whereas it increases up to infinite for stochastic processes [15]. Since the COP signal is a mixture of random and deterministic components [30], both mentioned situations are possible [15]; it will depend on which component is "stronger".…”

Section: Investigation Of the Cop Data Behaviormentioning

confidence: 99%

“…Moreover, the "sway profile" may contain useful information regarding the subject's underlying postural control mechanisms [6], such as the motor strategies adopted to maintain the balance [12]. Thus, studies in the field of balance assessment have investigated several properties of the postural control mechanisms under different health conditions (e.g., aging, neurological disorders) as well as sensorial manipulations (e.g., deprivation of sight, changes in surface hardness) based on changes in the postural sway profiles (e.g., [6,[13][14][15][16]). For this purpose, the body sway is usually quantified by the center-ofpressure (COP) fluctuations (e.g., [5,7]), a posturographic variable which represents a collective outcome from both postural control system and force of gravity [1,2] (see [17] for more detailed information).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the structural parameters or nonlinear posturographic parameters may reveal body sway patterns along the time, that is, they are associated with the temporal ordering of COP trajectories (see [19,20] for further details). These metrics have been used in literature to describe several properties of the postural control system, such as the number of (active) dynamical degrees of freedom (e.g., [4,6,15]) and its sensitivity to local perturbations (internal and/or external) (e.g., [4,6,16]). …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the Relevance of Linear and Nonlinear Posturographic Features in the Recognition of Healthy Subjects and Stroke Patients

Marrega

Nievola

2014

2014 IEEE International Conference on Data Mining Workshop

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Since balance control is a basic prerequisite for most of our daily activities, this task has crucial importance in the functional independence of humans. During the balance control, the human body sways constantly, even in the quiet upright stance. This body sway is usually captured in the form of time series of center-of-pressure (COP) displacements with the help of a measurement device known as force platform. In this paper, rather than using the traditional statistical analysis widely found in balance assessment studies, machine learning techniques were employed to recognize stroke patients and healthy matched subjects based on posturographic features extracted from their COP data. In this context, our main purpose was to investigate the relevance of 16 linear and 9 nonlinear posturographic features commonly examined in the balance assessment field. Thus, the average joint performance among six popular classification methods was evaluated under a 65 instances-size dataset in three situations: using only linear features, only nonlinear features and, finally, linear and nonlinear posturographic features combined. The former situation yielded significantly (P < 0.01) better results. This finding suggest that, following an approach based on classification methods to distinguish healthy from stroke physiological systems, the overall amount of sway indexed by the linear features is more relevant than the temporal patterns of sway described by the nonlinear features.

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Postural coordination and control to the precision demands of light finger touch

2019

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Correlation dimension estimates of human postural sway

Cited by 14 publications

References 36 publications

A Correlation-Based Framework for Evaluating Postural Control Stochastic Dynamics

A Correlation-Based Framework for Evaluating Postural Control Stochastic Dynamics

Evaluation of the Relevance of Linear and Nonlinear Posturographic Features in the Recognition of Healthy Subjects and Stroke Patients

Postural coordination and control to the precision demands of light finger touch

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