Body sway and global equilibrium condition of the elderly in quiet standing posture by using competitive neural networks
Graphical abstract
Introduction
The body steadiness concerns the equilibrium condition of individuals and is one of the major factors for achieving human movements. Accountable for maintaining an upright posture, the body control system enables accomplishing a posture with low balance, attaining the limits of stability and steadiness [1], [2], [3]. Such a system empowers the body to achieve the movements when performing daily activities or to react to external disturbances.
Evaluating if the body control system achieves steadiness can, for instance, be accomplished by employing the center of gravity (COG) of individuals. Located in a position where the weight of a body is distributed equally in all directions, the COG is the main measure for pointing out the overall body position, balance, stability, and steadiness.
Body's aging, in turn, affects the physical conditions and the COG position of individuals leading the elderly to a balance deficit, thus, with a tendency to fall. Changes in energy metabolism, increase of body fat, decrease of muscle fibers and muscle mass, reduction in stature and bone density, as well as vertebral curvatures, all of them interfere at the stability and steadiness conditions. Important in all age groups, determining the actual equilibrium condition of the elderly is of special interest.
The evaluation of the degree of stability and steadiness depends on diverse factors and can employ distinct measures of body stance. The center of gravity and its vertical projection over the support base assume, in general, a leading role in the body sway analysis, especially when in quiet standing posture (Fig. 1). The support base concerns the upper boundary, given by the line of the fingers; the side boundaries, by the sideline of the feet; and the lower bound, by the line of heels (Fig. 1(a)). Keeping the perpendicular projection of the COG over their support base is a suitable approach used to evaluate if the body control system works properly, thus achieving stability and steadiness (Fig. 1(b) and (c)). Contrary behavior, when individuals present difficulty in keeping the COG projection within the support base, concerns unstable equilibrium condition, leading to a fall (Fig. 1(d)) [4], [5].
Nevertheless, determining the COG or its derived vertical projection is not a simple task [6]. An alternative to measure the body position and displacement is the center of pressure (COP). Concerning the body control system, it is a neuromuscular response to the displacement and position of the COG to keep it inside the support base. The center of gravity and the center of pressure can be assigned closely related, COG ≈ COP, when dealing with steadiness conditions – i.e., when the body mass acceleration is null. Due to that, the center of pressure also comes to be an important variable to quantify quiet standing body sway [5].
The most commonly used technique for measuring the COP position and displacement is the stabilometry (Fig. 2). Body measurements are performed by using sensors that capture, through plantar receptors, ground reaction intensity (Fig. 2(c)) on a force measuring platform (also stabilometer) (Fig. 2(b)) [7]. Such a platform captures successive measurements over a time interval, COP(t), resulting in a time series data. The resulting body sway area formed by the set of COP positions can, thus, be related to the balance and equilibrium of an individual. Such time series enable the analysis of both the stability and steadiness conditions and the body control system, mainly when computing the centroid of the COP data set. This point, at which the total area is assumed to be concentrated, corresponds to the global center of pressure measure, herein assigned as G-COP to differentiate from the single COP measure. The G-COP concerns, thus, the vertical line downward from the physical center of gravity when dealing with quiet standing posture, G-COP ≈ COG. In this sense, the G-COP is, in fact, the measure employed to steadiness analysis. When the G-COP is within the base of support, the body will be stable, counterclockwise, when it is no longer above the base of support, there is unstable body sway, presenting risk of fall (Fig. 1).
Several approaches came about to deal with stabilometric data, in general, and the center of pressure, in particular. Postural steadiness can be computed based on the magnitude and distribution concerning time-domain, frequency-domain, or hybrid analysis [2]. The COP displacement over time includes computing distance, area, speed, or length of the path traveled. One-dimensional displacement over time along the horizontal (vertical) motion, given by stabilogram, or two-dimensional displacement over time along the horizontal-vertical motion, given by statokinesigrams, can be employed to postural steadiness, stability, and body control analysis [5], [8]. When interested in determining the G-COP, the most common approach employed to achieve body sway analysis is the classical, statistical approach. The mean value of the set of COP measures along with the analysis of the area is employed to assign the risk of fall and postural control. The question that comes up is if there would be a feasible alternative to assess the G-COP and, thus, the postural steadiness, body sway, and equilibrium conditions distinct of the conventional statistical approach.
This study aims at assessing the equilibrium condition of the elderly by employing a competitive, self-organizing neural network, inspired by biological neural model of human beings. The Kohonen-based competitive neural network [9] is employed to identify the position and displacement [10], [11] of a global center of pressure obtained from a COP(t) time series data. Such an approach is suitable to recognize patterns (clustering) to group similarities of input data with the advantage of not requiring previous training (learning). The COP body sway area is generated when using a force measuring platform, concerning a statokinesigram whose variables are the media-lateral (ML) and antero-posterior (AP) directions. Due to these characteristics, the proposed competitive neural network-based statokinesigram approach addresses computing the position and displacement of quiet standing center of pressure of body sway in the elderly.
Section snippets
Elderly body displacement data
Experimental data are collected in a random population of older adult patients submitted to body sway and equilibrium analysis by using the stabilometry. The proposed approach addresses patients aging from 60 to 80 years old, regardless the gender. In order to avoid influence in the results, patients are not included when presenting the prescription of analgesics, antibiotics, or anti-inflammatory. They are also excluded when presenting severe organ dysfunction; have a diagnosis of autoimmune
Experimental body steadiness analysis of the elderly by using competitive neural network
Experimental body COP data set representing distinct spontaneous displacement of two older adults are shown by the AP(t) × ML(t) statokinesigrams in Fig. 7 and Fig. 8. The COP data set is collected over time in quiet standing posture comprising a set of 300 COP measures, for each of three ground reaction intensity pressure center samples, Si, ∀ i = 1, 2, 3. The importance in obtaining these three COP time series is to avoid biased body sway and equilibrium analysis. Each data set, COP, is
Conclusions
The statokinesigram analysis based on the competitive neural network proposed in this study comes to be an alternative to evaluate body control of the elderly. Such an approach computes the position and the displacement of the global center of pressure by computing the barycenter (centroid) of the set of COP measures, instead of using the mean value based statistical approach, thus, contributing to the postural steadiness and equilibrium condition analysis. Addressing the center of pressure
Acknowledgements
The authors Glaubia E.F. Bentes and Dr. Renato Zângaro would like to thank the National Council of Scientific and Technological Development of Brazil – CNPq – under Grant 458.789/2013-5 for the partial funding of this research.
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