Circular components in center of pressure signals

Static posturography provides an objective assessment of postural control by characterizing the body sway during upright standing. The Center-of-Pressure (CoP) signal is recorded by a force platform and it is analyzed by means of many different models and techniques. Most of the parameters calculated according to these different approaches are affected by relevant intra- and inter-subject variability and/or do not have a clear physiological interpretation. Traditional approaches decompose the CoP signal into antero-posterior and medio-lateral time series, corresponding to ankle plantar/dorsiflexion and hip adduction/abduction, respectively. In this study we hypothesized that CoP signals show inherent rotational characteristics. To verify our hypothesis we applied the rotary spectra analysis to the 2-dimensional CoP signal to decompose it into clockwise and counter-clockwise rotational components. We demonstrated the presence of rotational components in the CoP signal of healthy subjects, providing a reference data set of the spectral characteristics of these component

Postural sway in volleyball players

The aim of this work was to analyze the postural sway of volleyball players in bipedal quiet stance. The center of pressure (CoP) was measured in 46 athletes and 42 non-athlete controls. Each subject was tested in 10 different conditions, 5 with their eyes open and 5 with their eyes closed. Volleyball players showed greater CoP ellipses, suggesting a different model of sensory integration in their postural stability. A multivariate approach to data analysis demonstrated that the postural sway of the two groups was different when the subjects kept their eyes open, but it was not with visual deprivation. This could partially be explained by the better ‘dynamic' visual acuity of athletes, since possible (‘static') refractive errors were corrected for both groups. Furthermore, we expected that national players, engaged in more intensive training programs, were more different from controls than regional ones, and that defensive players, whose role requires the quickest reaction times, were more different from controls than hitters. Our results confirmed these hypothesis. The protocol presented might be useful to assess the efficacy of intensive sport training programs and/or to select elite players with an aptitude for a specific playing positio

Use of Nanoparticles as Nanoelectrodes in Contact-Less Cell Membrane Permeabilization by Time-Varying Magnetic Field: A Computational Study

This paper describes a computational approach for the assessment of electric field enhancement by using highly conductive gold nanoparticles (Au NPs) in time-varying electromagnetic fields cell membrane permeabilization, estimating the influence of the presence of Au NPs on transmembrane potential and on the pore opening dynamics. To account for variability and uncertainty about geometries and relative placement and aggregations of the Au NPs, three different NP configurations were considered: spherical Au NPs equally spaced around the cell; cubic Au NPs, for accounting for the possible edge effect, equally spaced around the cell; and spherical Au NPs grouped in clusters. The results show that the combined use of Au NPs and a time-varying magnetic field can significantly improve the permeabilization of cell membranes. The variability of NPs’ geometries and configurations in proximity of the cell membrane showed to have a strong influence on the pore opening mechanism. The study offers a better comprehension of the mechanisms, still not completely understood, underlying cell membrane permeabilization by time-varying magnetic fields

From the classical analysis of the center of pressure signals to a novel approach: study of rotational components.

The balance control is known to be a complex motor skill, which involves the integration of many types of sensory information. The postural control is achieved by feedback mechanisms based on the body-sway motion detected primarily by visual, vestibular, and proprioceptive sensory systems. Static posturography provides an objective assessment of postural control by characterizing the body sway during upright standing. The Center-of-Pressure (CoP) signal is recorded by a force platform and analyzed according to different techniques. Traditional approaches decompose the CoP signal into antero-posterior (AP) and medio-lateral (ML) time series and extrapolate geometrical, statistical and spectral parameters. In this dissertation the problem of analyzing CoP signals was faced with two different strategies. Firstly, I applied geometrical parameters to describe the CoP signals and a multivariate statistical approach to analyze differences in quiet standing among groups of subjects, in two different studies. A new acquisition protocol was proposed, which adds to frontal open- and closed-eye conditions, conditions in which quiet standing of the subject is evaluated after a fast or a slow head rotation, on the left or on the right side, both with eyes open and closed. The aim of the first work was to analyze the postural control of volleyball players and the impact that vision has on it. The main hypothesis of this study was that, since volleyball players use the visual system differently from untrained subjects, the role of vision on postural control should also be different. Volleyball players showed greater CoP ellipses with respect to controls. A multivariate approach to data analysis demonstrated that the two groups were different when the subjects kept their eyes open, but they were not with visual deprivation. The influence of the athlete’s expertise and team role on balance performances was also analyzed. Differences in the upright stance of national and regional athletes were found, as well as differences between defensive players and hitters. The second work evaluated differences in postural performances in controls and patients with residual neuro-ophthalmic deficits after a traumatic brain injury. It was possible to evidence significant balance abnormalities in TBI patients with respect to controls. Moreover, by means of a multivariate analysis, I was able to discriminate different levels of residual neuro-ophthalmic impairment. The second approach was based on the hypothesis that the CoP signal contains rotational components. To verify this hypothesis and to extract rotational components from the CoP signal I applied the rotary spectra analysis, a well known technique developed in the meteorological and oceanographic field. Rotary spectra analysis involves the representation of a two-dimensional signal in the complex plane as a superimposition of ellipses, which can be analyzed in terms of their shape and orientation. Each ellipse is the sum of a counterclockwise (CCW) and a clockwise (CW) rotating phasors, called rotary components. This approach allows to consider both the AP and ML time-series not only as mono-variate signals, but also as components of a complex signal, taking into account both the amplitude of the whole signal and its phase. The rotary spectra approach permits to separate rotational isofrequential components from non-rotational ones, providing a different approach in understanding of the physiological mechanisms underlying postural control. The rotary spectra analysis was applied to the CoP signal of a population of healthy subjects. The presence of rotational components in the signal was demonstrated, and useful parameters about the rotational characteristics of the body sway were extracted. An interesting result highlighted by this new approach was that the mode value of the rotary spectra fell in the range 0.14-0.17 rps. The peak that was observed in this frequency range probably has a physiologically explainable meaning that was never documented before. I hypothesized that rotary spectra peaks obtained in the study of postural control during upright standing are strictly correlated to the bursts of muscle sympathetic nerve activity. This work passed the first stage of review to be published in “Motor Control”. Since the CoP signal is not strictly stationary, considering the classical rotary spectra analysis it is possible only to obtain frequency marginals of the signal during the 60s test. To analyze the CoP trajectories as non-stationary random signals, the classical rotary spectra theory was extended, to deal with non-stationary signals. In particular, I applied both bilinear Cohen's class Choi-Williams time-frequency distribution and wavelet method to obtain time-frequency analysis of rotating components. This allowed evaluating rotational characteristics of the CoP signal in the time-frequency plane. As interpretation and classification of a large number of time-frequency distributions could be difficult, time expensive, and highly depending on the researcher, I developed an automatic analysis method. It consists of three main steps: (i) using image processing tools, the main frequency components were identified on the time-frequency plane; (ii) a set of features was extracted; (iii) a clustering algorithm was applied. Applying this method to the distributions calculated by the time-frequency rotary analysis of the CoP signals of a population of healthy subjects, I obtained a fast and repeatable description of each distribution in terms of frequency components. Moreover, a classification in five groups of the distribution was obtained. In conclusion, both followed strategies gave satisfactory results. The proposed protocol and the evaluation of geometrical parameters allowed highlighting differences between groups of pathological and healthy subjects. I proposed a new approach in analyzing CoP signal in terms of rotational components, which gave interesting results on a group of healthy subjects. The technique was extended to time-frequency domain and an automatic analysis method of time-frequency distribution was developed. The rotary spectra analysis, both in stationary and non-stationary cases, is complementary to other approaches described in literature to study the CoP signal, and could contribute in reaching better understanding of the physiological mechanisms underlying postural control

From the classical analysis of the center of pressure signals to a novel approach: study of rotational components

The balance control is known to be a complex motor skill, which involves the integration of many types of sensory information. The postural control is achieved by feedback mechanisms based on the body-sway motion detected primarily by visual, vestibular, and proprioceptive sensory systems. Static posturography provides an objective assessment of postural control by characterizing the body sway during upright standing. The Center-of-Pressure (CoP) signal is recorded by a force platform and analyzed according to different techniques. Traditional approaches decompose the CoP signal into antero-posterior (AP) and medio-lateral (ML) time series and extrapolate geometrical, statistical and spectral parameters. In this dissertation the problem of analyzing CoP signals was faced with two different strategies. Firstly, I applied geometrical parameters to describe the CoP signals and a multivariate statistical approach to analyze differences in quiet standing among groups of subjects, in two different studies. A new acquisition protocol was proposed, which adds to frontal open- and closed-eye conditions, conditions in which quiet standing of the subject is evaluated after a fast or a slow head rotation, on the left or on the right side, both with eyes open and closed. The aim of the first work was to analyze the postural control of volleyball players and the impact that vision has on it. The main hypothesis of this study was that, since volleyball players use the visual system differently from untrained subjects, the role of vision on postural control should also be different. Volleyball players showed greater CoP ellipses with respect to controls. A multivariate approach to data analysis demonstrated that the two groups were different when the subjects kept their eyes open, but they were not with visual deprivation. The influence of the athlete's expertise and team role on balance performances was also analyzed. Differences in the upright stance of national and regional athletes were found, as well as differences between defensive players and hitters. The second work evaluated differences in postural performances in controls and patients with residual neuro-ophthalmic deficits after a traumatic brain injury. It was possible to evidence significant balance abnormalities in TBI patients with respect to controls. Moreover, by means of a multivariate analysis, I was able to discriminate different levels of residual neuro-ophthalmic impairment. The second approach was based on the hypothesis that the CoP signal contains rotational components. To verify this hypothesis and to extract rotational components from the CoP signal I applied the rotary spectra analysis, a well known technique developed in the meteorological and oceanographic field. Rotary spectra analysis involves the representation of a two-dimensional signal in the complex plane as a superimposition of ellipses, which can be analyzed in terms of their shape and orientation. Each ellipse is the sum of a counterclockwise (CCW) and a clockwise (CW) rotating phasors, called rotary components. This approach allows to consider both the AP and ML time-series not only as mono-variate signals, but also as components of a complex signal, taking into account both the amplitude of the whole signal and its phase. The rotary spectra approach permits to separate rotational isofrequential components from non-rotational ones, providing a different approach in understanding of the physiological mechanisms underlying postural control. The rotary spectra analysis was applied to the CoP signal of a population of healthy subjects. The presence of rotational components in the signal was demonstrated, and useful parameters about the rotational characteristics of the body sway were extracted. An interesting result highlighted by this new approach was that the mode value of the rotary spectra fell in the range 0.14-0.17 rps. The peak that was observed in this frequency range probably has a physiologically explainable meaning that was never documented before. I hypothesized that rotary spectra peaks obtained in the study of postural control during upright standing are strictly correlated to the bursts of muscle sympathetic nerve activity. This work passed the first stage of review to be published in "Motor Control". Since the CoP signal is not strictly stationary, considering the classical rotary spectra analysis it is possible only to obtain frequency marginals of the signal during the 60s test. To analyze the CoP trajectories as non-stationary random signals, the classical rotary spectra theory was extended, to deal with non-stationary signals. In particular, I applied both bilinear Cohen's class Choi-Williams time-frequency distribution and wavelet method to obtain time-frequency analysis of rotating components. This allowed evaluating rotational characteristics of the CoP signal in the time-frequency plane. As interpretation and classification of a large number of time-frequency distributions could be difficult, time expensive, and highly depending on the researcher, I developed an automatic analysis method. It consists of three main steps: (i) using image processing tools, the main frequency components were identified on the time-frequency plane; (ii) a set of features was extracted; (iii) a clustering algorithm was applied. Applying this method to the distributions calculated by the time-frequency rotary analysis of the CoP signals of a population of healthy subjects, I obtained a fast and repeatable description of each distribution in terms of frequency components. Moreover, a classification in five groups of the distribution was obtained. In conclusion, both followed strategies gave satisfactory results. The proposed protocol and the evaluation of geometrical parameters allowed highlighting differences between groups of pathological and healthy subjects. I proposed a new approach in analyzing CoP signal in terms of rotational components, which gave interesting results on a group of healthy subjects. The technique was extended to time-frequency domain and an automatic analysis method of time-frequency distribution was developed. The rotary spectra analysis, both in stationary and non-stationary cases, is complementary to other approaches described in literature to study the CoP signal, and could contribute in reaching better understanding of the physiological mechanisms underlying postural contro

Rotational components in centre of pressure signals

Static posturography provides an objective assessment of postural control performances by characterizing the body sway during quiet upright standing. The Center-of-Pressure (CoP) signal is recorded by a force platform and then analyzed according to different techniques. Traditional approaches extrapolate geometrical, statistical and spectral parameters from the sway path. In this study we hypothesized that CoP signal shows inherent rotational characteristics. To extract these components we applied the rotary spectra technique and we demonstrated the presence of clockwise and counter-clockwise rotational components in the CoP signal of healthy subject

Automatic Classification of Time-Frequency Plots applied to the Center-of-Pressure rotational components

Time-frequency plots are widely applied to the non-stationary analysis of signals. These plots may be difficult to interpret, particularly when large data sets have to be considered. The aim of this work is to propose an automatic procedure of feature selection and clustering to be applied to time-frequency plots. We focus on the application of this procedure to plots obtained from a non-stationary analysis of the center-of-pressure signals acquired in upright bipedal stance. From a data set of 168 time-frequency plots we obtained 5 different clusters, each characterized by a few distinctive features. We were able to interpret the results of the clustering relating them to the physiological mechanisms underlying postural sway

Stochastic Dosimetry Based on Low Rank Tensor Approximations for the Assessment of Children Exposure to WLAN Source

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