https://orcid.org/0000-0002-9863-1999
Abstract
Postural stability is a crucial aspect of human balance and equilibrium. One way to quantify postural stability is by analyzing variations in the center of pressure under the feet. However, there are several factors involved in quantifying postural stability, and interpreting the information they provide can be challenging, especially in the context of pathological motion. The aim of this work is to reduce the confounding effects of these factors to the extent that measurable differences in results are primarily due to a pathological condition rather than secondary factors such as procedural variations. Materials and methods: The initial research conditions were established based on theoretical aspects of human standing stability. Thirteen normal subjects (5 female and 8 male) with a mean age of 21 ± 4.97 years and a mean BMI of 25.27 ± 4.53 kg/m2 were recruited for the study. Four investigations were conducted, including eyes closed-closed base, eyes open-closed base, eyes closed-open base, and eyes open-open base conditions. Data collection was performed using the AMTI AccuGait force plate and Balance Clinic software. Spectral characteristics, composite multiscale entropy, and conventional stability parameters were computed for both the anterior-posterior (AP) and medial-lateral (ML) directions. Results: Composite multiscale entropy shows no significant difference (p > 0.05) in both AP and medial-lateral (ML) directions. Whereas the frequency of oscillations in ML is more sensitive than other parameters with relatively large effect size (>0.9). Conclusion: The resulting set of variables provides a broad range of options for selecting specific variables related to a particular study topic. The benefit of this method is that it standardizes the analysis of the center of pressure and enables the comparability of variables gathered during various research measurements. For general standing stability measurements, composite multiscale sampling entropy is the most relevant factor that can be used to discriminate the stability state in both the anterior-posterior and medial-lateral directions.
1. Introduction
In the past few decades, scientists have focused on investigating the mechanical stability of humans during normal daily activities to prevent or reduce the incidence of falls and related injuries. Maintaining stability has become increasingly crucial due to the degradation of the balance control system in the elderly and various pathological conditions. Additionally, it is important to explore the effects of orthotic and prosthetic devices on human stability for design improvement. This has forced clinicians and researchers to better understand how the human balance system works and how to quantify its condition.
Balance is generally defined as the dynamics of body posture against perturbations (Johansson & Magnusson, Citation1991). Currently, there are limited clinical tests available for quantifying postural balance, such as standing on one leg for a certain amount of time or performing observable tasks (Yiou et al., Citation2017). Equipment used for assessing balance includes force plates, the Balance Master, and the Equitest. Unfortunately, these instruments are only available in biomechanical laboratories due to their high cost (ranging from $6500 up to $100,000) and the difficulties associated with interpreting the data (Chaudhry et al., Citation2011).
The bipedal locomotion nature of humans, involving various foot contact states (no foot contact during running, one foot in contact during walking, and two-foot contact during standing), poses a major challenge to the balance control system of the human body. In recent decades, a considerable number of studies have investigated the complexities of center of pressure (CoP) trajectories (also known as stabilogram) during quiet standing. Since the displacements of the CoP during quiet standing display highly irregular and non-stationary oscillations, the examination of CoP dynamics can provide information on the postural control exerted (Rigoldi et al., Citation2013).
The evaluation of CoP can be useful for the detection of postural stability for various pathological conditions. For instance, balance changes in Parkinson’s disease (Kamieniarz et al., Citation2021), Stroke (Jagroop et al., Citation2023), scoliosis (Horng et al., Citation2021; Sim et al., Citation2018), Multiple sclerosis patients (Van Emmerik et al., Citation2010), and rehabilitation procedures (Ruhe et al., Citation2011). It is considered that the simple procedure used to measure resting balance on a stable, hard surface already offers useful information for assessing the likelihood of fall risk (Bauer et al., Citation2016). Therefore, static posturography on a force plate could be a useful tool for determining the risk of falling, especially for the elderly who are known to have psychomotor disorders and greatly restrict the possibility of undergoing functional tests that could compromise their already precarious balance. Therefore, forceplate is currently used for quantifying balance (Quijoux et al., Citation2021).
There are a wide range of variables found in the literature used for quantifying CoP (Palmieri et al., Citation2002; Quijoux et al., Citation2021). The assessment of standing stability commonly relies on parameters such as the maximum CoP displacement in anterior-posterior (AP) and medial-lateral (ML) directions, mean velocity, and 95% confidence ellipse area (Collins & De Luca, Citation1993; Pan et al., Citation2016).
Entropy, which measures the rate of information production and reflects irregularity, has been proposed as a means to quantify complexity or chaos in postural control (Cimolin et al., Citation2011; De Wu et al., Citation2013; Hansen et al., Citation2017). The objective of this study is to determine the most relevant parameters for balance assessment and establish their ranges for normal subjects.
2. Physiological aspects of upright posture
The process of standing is a complex reflex that involves maintaining body equilibrium through impulse signals generated by various receptors in the body, such as muscle and tendon proprioceptors, the vestibular and visual apparatus, and the skin exteroceptors. The signals from these receptors are transmitted to the CNS, which activates reflex contractions in the muscles to restore equilibrium when body balance is disturbed (Terekhov, Citation1976). As a result, the body undergoes continuous oscillations that maintain the dynamic balance of the posture. During quiet standing, humans experience unconscious micromotions in the anteroposterior (A/P) and mediolateral (M/L) directions, which compensate for any loss of balance and ensure posture is maintained through the complex reflex mechanism of the CNS. The amplitude and frequency of these body oscillations vary between individuals and depend on pathological conditions (Horak et al., Citation1992; Kim et al., Citation2008; Rocchi et al., Citation2002).
3. Biomechanical aspects of upright posture
The objective of postural equilibrium is to maintain the center of mass (CoM) of the body within the base of support (BoS) (King & Zatsiorsky, Citation1997). From a biomechanical standpoint, postural stability is achieved when the CoM aligns with the center of pressure (CoP) and falls within the BoS (Husein & Chung, Citation2019; Okubo et al., Citation1979; Shumway-Cook & Woollacott, Citation2000).
The CoP is the average location of the pressure exerted by the feet in contact with the ground, and it represents the point at which the vertical ground reaction force vector is applied, independent of CoM. In the case of both feet being in contact with a single force plate, the net CoP is located somewhere between the two feet, depending on the relative weight distribution between them (Okubo et al., Citation1979).
The contemporary approach to postural control regards balance as the outcome of integrated inputs and considers the body as a mechanical system that interacts with the nervous system in an ever-changing environment (Jeka et al., Citation2008; Winter et al., Citation2003).
Numerous studies have shown that balance can be influenced by constraints or deficits in the underlying systems (Horak et al., Citation2009). The Systems Framework for Postural Control recognizes that there are several critical components involved in maintaining postural control, such as cognitive processing, movement strategies, orientation in space, biomechanical system constraints, dynamic control, and sensory strategies (Horak, Citation2006; Sibley et al., Citation2015).
4. Materials and methods
4.1. Participants
The experiment was conducted with 13 (8 males and 5 females) healthy participants as verified by self-report, with mean age 21 ± 4.97 years, and body mass index of BMI = 25.27 ± 4.53 kg/m2.
The inclusion criteria were sane adults between 20 and 30 years old. The exclusion criteria were neurological or vision disorders and BMI greater than 30 kg/m2. The study was approved by Biomedical Engineering research committee/Al-Nahrain University.
4.2. Instrumentation and procedure
The methodology used for converting mechanical oscillations of the subject’s physiologic gravicentre into electrical signals with the associated conditioning stages is called Stabilometry. AMTI AccuGait force plate (Figure ) was used for this purpose with a sampling rate of 100 Hz. The experimental procedure is conducted at the biomechanics laboratory/Al-Nahrain University.
Figure 1. AMTI AccuGait balance system.
The test procedure is simple and comfortable; after recording the weight, and the height, the participant is asked to stand still and barefooted on the force plate in their natural position (Figure ), arms along the body, looking ahead at a marker (sticker) for concentration. This marker is placed about 250 cm from the subject, and other visual interference such as lab equipment was removed from the line of sight to eliminate any possible distractions. In order to examine the effect of sensory feedback and size of BOS on human balance, the following protocol is performed: (1) Eyes Open − Open Base (OO), which is the normal standing comfortable position (i.e., self-referred); (2) Eyes Closed − Open Base (CO); (3) Eyes Open − Closed Base (OC), in which both feet are close together without medial malleolus contact and (4) Eyes Closed − Closed Base (CC). Each protocol is recorded within 30 seconds. The data (CoP in the mediolateral and anteroposterior directions) were collected at a rate of 100 Hz and filtered with zero-phase second-order Butterworth filter with 5 Hz cut-off frequency. The experiment was performed in the same environmental conditions for all subjects at 10 to 12 am to avoid the metabolic and physiologic changes.
Figure 2. Participant posture during the experiment (case: open base).
4.3. Data analysis
The classical stability parameters were computed to quantify the difference among the stability protocols. The following variables were calculated from postural sway: the CoP displacements and the related velocities in the AP and ML directions (cm); the average velocity (Vavg), which represents the ratio of the path length to the trial duration (cm/s); the path length (cm); the surface area of 95% confidence ellipse, that is, the smallest surface area occupied by the ellipse (cm2). The formulas used for calculating the aforementioned variables can be found in (Quijoux et al., Citation2021).
The frequency of oscillations in ML (FML) and AP (FAP) was calculated using Morlet wavelet transform with a sampling period of 0.005. Due to the large variability of CoP displacements data, composite multiscale entropy (CMSE) was computed for regularity measurement for a time scale of 0.2 times the standard deviation and embedded dimension of 2. The theory behind CMSE is well described in (De Wu et al., Citation2013).
The normality of data was checked using Shapiro-Wilk test and resulted in 43% of the calculated parameters are non-normally distributed. Therefore, Kruskal-Wallis test was used to evaluate the statistical difference (p < 0.05), and the data are reported as median and inter-quartile range (IQR). Finally, Dunn-Bonferroni post hoc test was used as a post hoc multi-comparison analysis. The OO protocol is considered the control group. MATLAB R2019b (GitHub - dnafinder/dunn: Dunn procedure for multiple non parametric comparisons, Citation2021) was used for postural variables computation and statistical analysis. The effect size was calculated to find the effect size for factors that had a statistically significant difference, as large (>0.8), moderate (0.5 − 0.8), small (0.25 − 0.5), and insignificant (<0.25).
4.4. Results
Kruskal-Wallis test indicated significant differences (p < 0.05) in six parameters: CoPML, VML, VAvg, path length, area, and FML (Table ). Dunn-Bonferroni post-hoc test indicated a statistically significant difference between the OO and OC conditions for CoPML and FML(Q < 2.3877). In the meanwhile, significant differences were revealed between OO and CC conditions for the path length, VML, VAvg, area, and FML. However, there is no difference between OO and CO protocols.
Table 1. The non-parametric statistical tests (median ± IQR) on differences in classical and nonlinear complexity parameters between the four experimental circumstances (OO, CO, OC, CC)
5. Discussion
The CoP trajectory represents a highly complex nonlinear signal. The signal complexity is resulted from the nonlinear interaction between various control nodes across various time scales. The inability to distinguish the stabilogram disturbance in the context of instability (whether related to vestibular, visual, proprioceptive, motor, etc.), and the complexity of quantification and interpretation were the urge for the study. A common protocol for balance test is by taking into account the vision and BoS. However, in the applications of rehabilitation, orthosis and prosthetic design, there is a need for certain measures that are independent on vision and BoS (which is also, practically, difficult to standardize). In this study, nonlinear complexity parameters (represented by wavelet frequency analysis and composite multiscale sampling entropy) were compared to classical parameters to see any differences in CoP displacements throughout four different standing protocols.
The results show that the influence of the visual feedback and the BOS in the body sway control is evident. Path length, VML, VAvg, and FML were able to differentiate between OO and CC protocols. Whereas the influence of the biomechanical factors in terms of the base of support can be explained using CoPML, and FML. further, the largest variability and effect size was revealed in analyzing the frequency spectrum in ML direction (FML).
However, for quantification of human balance and discovering the influence of a pathological or an orthosis on standing balance, we must use a parameter that cancels out the variations caused by the vision and the size of BOS, keeping in mind that the boundaries of BOS should be within the cone of stability (Erdeniz et al., Citation2019; Pluchino et al., Citation2011). Therefore, the aforementioned variables are considered not reliable in studying postural stability. The main reason is the computational methods are for stationary signal, whereas the CoP is non stationary. Entropy seems to be the most suitable parameter as it captures the inner dynamics of the non-stationary oscillations. Our results is agreed with F. J. Moreno, et al (Kamieniarz et al., Citation2021), as they suggested the decomposing of CoP trajectory into different frequency bands will be more reliable in analyzing the CoP complexity. Further, vision suppression results in minor changes in CoP characteristics (Van Emmerik et al., Citation2010). The limitations of this study include the absence of comparisons based on gender, age, or weight, and the individuals’ natural foot type (neutral, pronated, or supinated).
6. Conclusion
It is crucial to establish a consensus on factors that can affect postural stability testing data and to quantify postural stability accurately. The findings of this study suggest that certain nonlinear entropy measures may be more effective in discriminating postural displacement than traditional measures. Specifically, the results indicate that parameters such as CoPML, Path Length, VML, VAvg, Area, and FML may not be reliable indicators of quiet standing stability. Therefore, when analyzing postural stability for diagnosis, orthosis and prosthesis application, composite sample entropy should be applied for quantifying stability. Future research should focus on examining diverse populations with various clinical conditions to determine if the strategies used in this study can be applied to confirm the reliability and validity of these findings.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data available on request from the authors.
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