Effect of whole-body vibration on center-of-mass movement during standing in children and young adults

Liang, Huaqing; Beerse, Matthew; Ke, Xiang; Wu, Jianhua

doi:10.1016/j.gaitpost.2017.03.005

Cited by 15 publications

(9 citation statements)

References 29 publications

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“…The significantly negative main effect of the Metronome track on fractality across directions may indicate disturbances to head sway, with less persistent movement patterns induced by the monotonous nature of this 45 second stimulus. Previous research has shown the negative effect of physical and sensory disturbances on the fractal properties of CoM and CoP (Stambolieva, 2011;Liang et al, 2017). Results from the analysis of the effects of music on movement are consistent with this speculation, with the Metronome track significantly increasing QoM in the ML, SI, and 3D direction.…”

Section: Effect Of Music On Head Sway Fractalitysupporting

confidence: 79%

“…In this context, studies on the sway of the human body during standing have revealed the fractal properties of center of mass (CoM) and CoP trajectories, observing a decrease in the fractal properties of posture control in the presence of disturbances (Liang et al, 2017), and with age (Duarte and Sternad, 2008). However, the majority of research on the fractality of human posture control during standing have focused on "standing as still as possible" without auditory perturbations.…”

Section: Fractalitymentioning

confidence: 99%

“…Research on the complexity and fractality of human balance control during standstill has consistently shown similar long-range correlations in the sway of the center of pressure (CoP) and center of mass (CoM). In addition, such fractal properties of postural control have been observed to decrease with age, disease, and perturbations (Thurner et al, 2000;Duarte and Sternad, 2008;Blázquez et al, 2009;Liang et al, 2017). Although such studies are fundamen- tally different to the present study due to their use of conventional measures of posture and balance from individuals in a non-competitive setting, the effects of physical and sensory perturbances on the fractality of human behavioral outputs have been observed in a wider range of settings and conditions (Kello et al, 2007;Alves et al, 2017).…”

Section: Fractal Propertiesmentioning

confidence: 99%

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Analysis of the Movement-Inducing Effects of Music through the Fractality of Head Sway during Standstill

González-Sánchez

Żelechowska

Jensenius

2019

Journal of Motor Behavior

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The links between music and human movement have been shown to provide insight into crucial aspects of human's perception, cognition, and sensorimotor systems. In this study, we examined the influence of music on movement during standstill by analyzing head sway fractality, with the aim of further characterizing the correspondences between movement, music, and cognition. Eighty seven participants were asked to stand as still as possible for 500 seconds while being presented with alternating silence and musical stimuli. The auditory stimuli were all rhythmic in nature, ranging from a metronome track to complex electronic dance music. The head position of each participant was captured with an optical motion capture system. Long-range correlations of head movement were estimated by detrended fluctuation analysis (DFA). The results support findings from previous work on the movement-inducing effect of music, showing significantly greater head sway and lower head sway fractality during the music stimuli. In addition, the patterns across stimuli suggest that there is a two-way adaptation process, with the music stimuli influencing head sway while at the same time fractality modulated movement responses. The results indicate that fluctuations in head movement in both conditions exhibit long-range correlations, suggesting that the effects of music on head movement depended not only on the value of the most recent measured intervals, but also on the values of those intervals at distant times.

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Section: Effect Of Music On Head Sway Fractalitysupporting

confidence: 79%

Section: Fractalitymentioning

confidence: 99%

Section: Fractal Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of the Movement-Inducing Effects of Music through the Fractality of Head Sway during Standstill

González-Sánchez

Żelechowska

Jensenius

2019

Journal of Motor Behavior

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“…Overall, the scaling exponent α was larger than 1.5 for all directions and all groups, indicating a Brownian noise (Stergiou, 2016). These results were slightly higher but similar to those of Liang et al (2017) , which considered the CoM instead of center of pressure for DFA (Lin et al, 2008;Blázquez et al, 2010;Munafo et al, 2016) as in the present study. On the other hand, even though DFA has become a predominant method for fractal analysis, it may not provide useful results for short time series (Stergiou, 2016).…”

Section: Quantification Of Com Swaysupporting

confidence: 85%

Influence of Controlled Stomatognathic Motor Activity on Sway, Control and Stability of the Center of Mass During Dynamic Steady-State Balance—An Uncontrolled Manifold Analysis

Fadillioglu

Kanus

Möhler

et al. 2022

Front. Hum. Neurosci.

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Multiple sensory signals from visual, somatosensory and vestibular systems are used for human postural control. To maintain postural stability, the central nervous system keeps the center of mass (CoM) within the base of support. The influence of the stomatognathic motor system on postural control has been established under static conditions, but it has not yet been investigated during dynamic steady-state balance. The purpose of the study was to investigate the effects of controlled stomatognathic motor activity on the control and stability of the CoM during dynamic steady-state balance. A total of 48 physically active and healthy adults were assigned to three groups with different stomatognathic motor conditions: jaw clenching, tongue pressing and habitual stomatognathic behavior. Dynamic steady-state balance was assessed using an oscillating platform and the kinematic data were collected with a 3D motion capturing system. The path length (PL) of the 3D CoM trajectory was used for quantifying CoM sway. Temporal dynamics of the CoM movement was assessed with a detrended fluctuation analysis (DFA). An uncontrolled manifold (UCM) analysis was applied to assess the stability and control of the CoM with a subject-specific anthropometric 3D model. The statistical analysis revealed that the groups did not differ significantly in PL, DFA scaling exponents or UCM parameters. The results indicated that deliberate jaw clenching or tongue pressing did not seem to affect the sway, control or stability of the CoM on an oscillating platform significantly. Because of the task-specificity of balance, further research investigating the effects of stomatognathic motor activities on dynamic steady-state balance with different movement tasks are needed. Additionally, further analysis by use of muscle synergies or co-contractions may reveal effects on the level of muscles, which were not visible on the level of kinematics. This study can contribute to the understanding of postural control mechanisms, particularly in relation to stomatognathic motor activities and under dynamic conditions.

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“…Some biomedical data have non-linear properties and complex behavior, e.g., heart rate variability ( Lake and Moorman, 2011 ; Liu et al, 2011 ), electroencephalogram (EEG; Mesin and Costa, 2014 ; Li et al, 2016 ), electromyogram (EMG; Mesin et al, 2016 ), pupillogram ( Mesin et al, 2013 ; Onorati et al, 2016 ), center of mass during quiet standing ( Liang et al, 2017 ). The estimation of their level of regularity can be useful in many applications ( Seely and Christou, 2000 ; Gao et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Complexity of Sampled Biomedical Continuous Time Signals Using Approximate Entropy

Mesin

2018

Front. Physiol.

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Non-linear analysis found many applications in biomedicine. Approximate entropy (ApEn) is a popular index of complexity often applied to biomedical data, as it provides quite stable indications when processing short and noisy epochs. However, ApEn strongly depends on parameters, which were chosen in the literature in wide ranges. This paper points out that ApEn depends on sampling rate of continuous time signals, embedding dimension, tolerance (under which a match is identified), epoch duration and low frequency trends. Moreover, contradicting results can be obtained changing parameters. This was found both in simulations and in experimental EEG. These limitations of ApEn suggest the introduction of an alternative index, here called modified ApEn, which is based on the following principles: oversampling is compensated, self-recurrences are ignored, a fixed percentage of recurrences is selected and low frequency trends are removed. The modified index allows to get more stable measurements of the complexity of the tested simulated data and EEG. The final conclusions are that, in order to get a reliable estimation of complexity using ApEn, parameters should be chosen within specific ranges, data must be sampled close to the Nyquist limit and low frequency trends should be removed. Following these indications, different studies could be more easily compared, interpreted and replicated. Moreover, the modified ApEn can be an interesting alternative, which extends the range of parameters for which stable indications can be achieved.

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Effect of whole-body vibration on center-of-mass movement during standing in children and young adults

Cited by 15 publications

References 29 publications

Analysis of the Movement-Inducing Effects of Music through the Fractality of Head Sway during Standstill

Analysis of the Movement-Inducing Effects of Music through the Fractality of Head Sway during Standstill

Influence of Controlled Stomatognathic Motor Activity on Sway, Control and Stability of the Center of Mass During Dynamic Steady-State Balance—An Uncontrolled Manifold Analysis

Estimation of Complexity of Sampled Biomedical Continuous Time Signals Using Approximate Entropy

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