El procesado del desplazamiento del centro de presiones para el estudio de la relación complejidad/rendimiento observada en el control postural en bipedestación

Caballero, Carla; Barbado, David; Moreno, Francisco J. Magraner

doi:10.1016/s1888-7546(13)70043-1

Cited by 7 publications

(8 citation statements)

References 45 publications

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“…This high variance may reduce the time duration needed to achieve a stationary time series. In the stable condition, different locations of the center of gravity (COG) in the surface of support allow a person to maintain stability (Caballero et al, 2013); different stability locations can help achieve good performance. However, more difficult conditions limit the region of stability (Lee & Granata, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…However, more difficult conditions limit the region of stability (Lee & Granata, 2010). Thus, measures of the dispersion of the data relative to a midpoint, such as SD or BVE, are used as an indicator of postural control, but they may be affected by the nonstationarity of this data (Caballero et al, 2013). Therefore, scattering variables appear to be unreliable indexes of balance performance in stable conditions.…”

Section: Discussionmentioning

confidence: 99%

“…When analyzing the COP dynamic using nonlinear measures, signal oversampling could lead to artificial collinearities, that would affect the dynamics of the COP and mask the real values (Rhea et al, 2011). Therefore, using sampling frequencies close to the COP dynamic is recommended (Caballero, Barbado, & Moreno, 2013).…”

Section: Experimental Procedures and Data Collectionmentioning

confidence: 99%

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What COP and Kinematic Parameters Better Characterize Postural Control in Standing Balance Tasks?

Caballero¹,

Barbado²,

Moreno³

2015

Journal of Motor Behavior

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The authors' aim was to determine which variables allow for the characterization of motor balance behavior. Traditional measures and nonlinear measures of center of pressure (COP; n = 30) and kinematics (n = 10) were tested in their absolute and relative consistency in a 30-s standing balance task protocol under stable and unstable conditions. Regarding COP variables, mean velocity (mVel), permutation entropy (PE) and detrended fluctuation analysis (DFA) exhibited high consistency between trials and ranked individuals more accurately compare with other metrics. In the kinematic signal mVel, PE and DFA had good intrasession reliability values in unstable conditions. Overall, the intrasession reliability values were better in the unstable condition than in the stable condition and the measures calculated using derived data had better intrasession reliability values. In conclusion, mVel, PE, and DFA allow for the good characterization of motor balance behavior in a simplified protocol where velocity time series are analyzed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Experimental Procedures and Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

What COP and Kinematic Parameters Better Characterize Postural Control in Standing Balance Tasks?

Caballero¹,

Barbado²,

Moreno³

2015

Journal of Motor Behavior

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“…An application under Labview 2009 (Mathworks, Natick MA, USA), developed in our laboratory, was used to perform the data analysis. COP time series were previously down sampled from 1000 Hz to 20 Hz due to: 1) there being little of physiological significance above 10 Hz in the COP signal (Borg and Laxåback 2010), and suggestions to use sampling frequencies close to COP dynamics (Caballero et al 2013); 2) signal oversampling possibly leading to artificial co-linearities, affecting the variability data (Rhea et al 2011). The first and last 5 s of each trial were discarded to avoid non-stationarity related to trial initiation (van Dieën et al 2010).…”

Section: Data Analysis and Reductionmentioning

confidence: 99%

Variations in task constraints shape emergent performance outcomes and complexity levels in balancing

et al. 2016

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This study investigated the extent to which specific interacting constraints of performance might increase or decrease the emergent complexity in a movement system, and whether this could affect the relationship between observed movement variability and the central nervous system's capacity to adapt to perturbations during balancing. Fifty-two healthy volunteers performed eight trials where different performance constraints were manipulated: task difficulty (three levels) and visual biofeedback conditions (with and without the center of pressure (COP) displacement and a target displayed). Balance performance was assessed using COP-based measures: mean velocity magnitude (MVM) and bivariate variable error (BVE). To assess the complexity of COP, fuzzy entropy (FE) and detrended fluctuation analysis (DFA) were computed. ANOVAs showed that MVM and BVE increased when task difficulty increased. During biofeedback conditions, individuals showed higher MVM but lower BVE at the easiest level of task difficulty. Overall, higher FE and lower DFA values were observed when biofeedback was available. On the other hand, FE reduced and DFA increased as difficulty level increased, in the presence of biofeedback. However, when biofeedback was not available, the opposite trend in FE and DFA values was observed. Regardless of changes to task constraints and the variable investigated, balance performance was positively related to complexity in every condition. Data revealed how specificity of task constraints can result in an increase or decrease in complexity emerging in a neurobiological system during balance performance.

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“…Finally, it should be noted that NLTs are sensitive to the stationarity of the time series ( Peng et al, 2009 ; Caballero et al, 2013 ), which may have influenced the results reported by the studies reviewed. Two studies ( Chatain et al, 2020 ; Chatain et al, 2021 ) applied Empirical Mode Decomposition (EMD) to reduce the non-stationarity of the signal.…”

Section: Discussionmentioning

confidence: 99%

The use of non-linear tools to analyze the variability of force production as an index of fatigue: A systematic review

et al. 2022

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Background: Fatigue is a process that results in a decreased ability to produce force, and which could eventually affect performance and increase the risk of injury. Force variability analysis has been proposed to describe the level of fatigue with the purpose of detecting the development of fatigue. Variability is credited to play a functional and adaptive role through which the components of a system self-organize to solve a motor problem. Non-linear tools have been applied to analyze the variability of physiological signals, revealing that the structure of motor fluctuations provides relevant information about the functional role of variability. It has been suggested that the presence of lower complexity in the variability structure could reveal a less functional and adaptative state (e.g., ageing or illness). In the last years, an increased number of studies have applied these techniques to force variability analysis in relation to fatigue.Objective: To provide an overview of the current knowledge on the use of non-linear tools on force variability as a fatigue index.Methods: Following PRISMA guidelines, a systematic search of SPORTDiscus, Scopus, Web of Science and PubMed was carried out. Studies included were: a) original studies that analyzed the effect of fatigue on humans during an action focused on force production; b) published studies with their title and abstract in English; c) studies that applied non-linear tools on a signal directly related to force production.Results: Twenty-five studies were included in this review. The relationship between fatigue and the complexity of force variability, the type of action and relative intensity, the nature of the signal and the non-linear tools used, and the methods of data acquisition and processing were identified.Conclusion: The articles reviewed suggest that fatigue leads to a decrease in complexity mostly in isometric contractions, but this is not as clear in dynamic contractions. This fatigue-induced loss of complexity seems to be a result of changes in the nervous system at the central level, albeit triggered by peripheral mechanisms. It should be noted that non-linear tools are affected by the relative intensity of contraction, non-stationarity, and the acquisition and treatment of the signal.

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El procesado del desplazamiento del centro de presiones para el estudio de la relación complejidad/rendimiento observada en el control postural en bipedestación

Cited by 7 publications

References 45 publications

What COP and Kinematic Parameters Better Characterize Postural Control in Standing Balance Tasks?

What COP and Kinematic Parameters Better Characterize Postural Control in Standing Balance Tasks?

Variations in task constraints shape emergent performance outcomes and complexity levels in balancing

The use of non-linear tools to analyze the variability of force production as an index of fatigue: A systematic review

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