Neuromuscular Control in Postural Stability: Insights into Myoelectric Activity Involved in Postural Sway During Bipedal Balance Tasks

Abstract

Examining the dynamic interplay of muscle contributions to postural stability enhances our understanding of the neuromuscular mechanisms underlying balance control. This study examined the similarity in shape (using cross-correlation analysis) between seven individual lower limb electromyographic (EMG) signals and center-of-pressure (COP) displacements (i.e., EMG–COP correlation) in 20 young adults (25.2 ± 4.0 years) performing bipedal balance tasks on both stable and multi-axially unstable surfaces, testing the effects of four factors—leg dominance, surface stability, sway direction, and foot position—on individual EMG–COP correlations. The results revealed significant effects of leg dominance (p = 0.004), surface stability (p ≤ 0.001), and sway direction (p ≤ 0.001) on specific muscles. Notably, balancing on the non-dominant leg resulted in a stronger correlation between tibialis anterior activity and postural sway compared to the dominant leg. On a stable surface, postural sway showed stronger correlations with the rectus femoris, semitendinosus, biceps femoris, gastrocnemius medialis, and soleus muscles than on an unstable surface. Additionally, anteroposterior postural sway exhibited a greater correlation with semitendinosus and tibialis anterior activity compared to mediolateral sway. These findings underscore the importance of specific muscles in maintaining bipedal balance, with implications for improving balance performance across various populations.

1. Introduction

The intricate coordination of muscle actions, directed by the central nervous system, allows the human body to maintain balance and stability [1]. Neuromuscular control, which integrates sensory and motor functions, is essential for sustaining balance, posture, and smooth, efficient movements across a wide range of activities [2,3]. This control relies on inputs from sensory systems—vestibular, visual, and somatosensory—that enable precise posture regulation [2,3]. Posturography, a widely used technique for assessing balance by tracking center-of-pressure (COP) movements, plays a key role in understanding sensory–motor integration [4]. By analyzing COP patterns during swaying motions, posturography is essential for diagnosing balance disorders, assessing injury risks, monitoring physical performance, and guiding training or rehabilitation programs [4,5,6,7].

While analyzing two-dimensional COP movements provides valuable insights, it may not fully reveal the neuromuscular control mechanisms driving postural sway [8]. This limitation arises from the indirect nature of posturographic data, which primarily reflect ground reaction forces and moment-of-force outcomes [4]. To address this gap, direct analyses of movements and muscle activations offer a deeper understanding of postural control mechanisms beyond COP-based measures [8]. Surface electromyography (EMG), which records electrical muscle activity during tasks, provides a more accurate assessment of muscle contributions to postural control [9,10]. By capturing subtle muscle activity during quiet stance and analyzing its temporal relationship with COP displacements through cross-correlation analysis, the EMG-COP coordination approach offers unique insights into muscle activation patterns associated with postural adjustments [11,12,13,14,15]. This method highlights the complex temporal and coordination dynamics of muscular contributions to stability, advancing an understanding of the neuromuscular control mechanisms underpinning postural stability during balance tasks [16,17].

In this regard, the current study aimed to enhance our understanding of neuromuscular control during bipedal stance—a fundamental part of daily activity—focusing on testing the effects of leg dominance, surface stability, sway direction, and foot position through detailed examination of individual EMG–COP correlations. First, bipedal motor tasks require the coordinated engagement of both hemispheres to maintain an upright posture through synchronized limb movements [18]. Leg dominance influences the symmetry of neuromuscular control essential for postural stability, seen as different movement control strategies of movement components [19,20], and serves as one of the risk factors for sports-related lower limb injuries [21,22,23,24]. Analyzing the symmetrical contributions of leg muscles during bipedal tasks is therefore crucial for understanding neuromuscular control. Second, unstable surfaces like foam pads and wobble boards challenge postural stability by reducing sensory input and impairing corrective ankle torque [12,25,26,27]. The role of muscle activity during anticipatory postural control is critical, as lower limb and trunk adjustments counteract disturbances [11]. Understanding surface stability’s effects can offer valuable insights into neuromuscular control, with implications for clinical balance training. Third, upright postural stability varies between movement planes (sagittal and frontal), with specific lower limb muscles playing distinct roles along these axes [28,29,30]. For instance, a study on unipedal stability examined muscle contributions across these planes, uncovering their essential roles in maintaining balance [28]. Understanding these roles is critical for developing targeted rehabilitation strategies, enhancing athletic performance, and reducing injury risks by strengthening and conditioning the muscles that support stability in both planes. Finally, the base of support (BOS) plays a significant role in postural control during upright standing by influencing the body’s ability to maintain stability [31,32,33]. Variations in BOS, such as narrowing or widening the stance, alter the biomechanical and neuromuscular demands necessary to sustain balance [32]. A narrower BOS, such as the feet-together stance, poses greater challenges to postural stability [32], shifting the focus of control toward mediolateral stability rather than anteroposterior stability [32]. This adjustment requires a more intricate control strategy [15,32,34]. Compared to a feet-apart stance, the feet-together position increases the activation of lower leg muscles, including the tibialis anterior, peroneus longus, and gastrocnemius medialis [32]. Understanding the effects of foot position is essential for developing interventions to prevent falls [35], enhance athletic performance [36], and improve postural control in individuals with balance impairments [35].

In summary, this study aimed to investigate neuromuscular control during bipedal stance by analyzing the relationship between lower limb EMG signals and COP displacements using cross-correlation analysis in healthy young adults performing balance tasks on stable and multiaxially unstable surfaces. This study explores how leg dominance, surface stability, sway direction, and foot position influence muscle activity and postural adjustments. It was hypothesized that these factors would influence specific muscle activity patterns [28]. The findings provide valuable insights into how variations in movement planes and base of support affect muscle activation, with practical applications in balance assessment, injury prevention, fall risk evaluation, rehabilitation, and performance enhancement.

2. Materials and Methods

2.1. Participants

This study involved 20 physically active young adults (10 females and 10 males) with no neurological or musculoskeletal issues and no recent balance-specific training within the last six months. The average age was 25.2 ± 4.0 years, with a body mass index (BMI) of 22.4 ± 2.1 kg/m². Participants reported an average of 9.1 ± 5.3 h of physical activity per week, with no significant gender differences in activity levels. None of the participants reported any history of injury prior to participation. All participants indicated that running was their primary physical activity. Each participant identified their right leg as dominant, determined by their preferred leg for kicking a ball, and their dominant leg matched their dominant hand (based on their writing hand). The study protocol was approved by the Board for Ethical Questions in Science at the University of Innsbruck, Austria (Approval Code No. 14/2016), and this study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Participant characteristics are provided in

Table 1. Characteristics of participants (mean ± SD); * p < 0.001.

	Total (n = 20)	Male (n = 10)	Female (n = 10)
Age (yrs.)	25.2 ± 4.0	25.4 ± 2.2	25.0 ± 5.3
Weight (kg)	68.7 ± 11.0	75.3 ± 11.4	62.1 ± 5.2 *
Height (cm)	174.7 ± 8.9	181.3 ± 7.7	168.2 ± 4.0 *
Body mass index (kg/m2)	22.4 ± 2.1	22.8 ± 1.8	22.0 ± 2.4
Physical activity participation (hours/week)	9.1 ± 5.3	9.2 ± 6.0	9.1 ± 4.9

.

2.2. Equipment

Electromyographic (EMG) activity was recorded from seven muscles in each leg using a Noraxon TeleMyo™ 2400T G2 Direct Transmission System (Noraxon Inc., Scottsdale, AZ, USA) at a sampling rate of 1500 Hz. The measured muscles included the rectus femoris (RF), semitendinosus (ST), biceps femoris (BF), tibialis anterior (TA), peroneus longus (PL), gastrocnemius medialis (GM), and soleus (SO). Following SENIAM standards [37], disposable, pre-gelled bipolar 22 mm AgCl surface adhesive electrodes (Ambu Neuroline 720 01-K/12; Ambu, Ballerup, Denmark) were applied with 20 mm center-to-center spacing. Electrodes were placed over the muscle bellies, as per SENIAM guidelines [37]. To minimize movement artifacts, the EMG wires were secured with tape, and the reference electrode was positioned on the tibial tuberosity of the right leg. The electrodes and skin were prepared to achieve a low-impedance resistance (<6 kΩ) through cleaning, scrubbing, and shaving.

Center-of-pressure (COP) displacement was recorded using an OPTIMA™ AMTI force plate (AMTI, Watertown, NY, USA) with a sampling rate of 1250 Hz. To induce further posture adjustments, a multiaxially unstable balancing board (MFT Challenge Disc, Trend Sport Trading GmbH, Vienna, Austria) was used. This board features four rubber cylinders spaced eight centimeters apart on the upper 44 cm diameter round plate and base circle plate. To standardize the positioning of the balance board (Figure 1) [38,39], the center of the balancing board was placed over the center of a reticle crossline marked on the floor. The anteroposterior and mediolateral diameters, marked with tape, were aligned to these crossed lines. This method ensured that the same fulcrum position was set for all trials and participants. The force plate was controlled using Nexus 2.2.3 software, integrated with the EMG system (Vicon Motion Systems Ltd., Oxford, UK).

2.3. Experimental Procedure

Without any guidance or feedback, each participant started with a 15-s familiarization period on the balancing board. For each type of support surface, participants performed two 80-s bipedal barefoot stances in random order with two different foot positions (feet apart or feet together). The order of the stable and unstable support surfaces was also randomized.

Participants were instructed to place their hands on their hips and position a marked point (the base of the second metatarsal bone) on each foot over a horizontal line taped on the floor for stable-surface trials or over the horizontal diameter of the balance board for unstable-surface trials. For feet-apart trials, they were to align the medial borders of each foot close together, and for feet-together trials, they were instructed to align the medial borders of the distal ends of the first metatarsal bones with a specific inter-foot distance (15 percent of bi-acromial diameter [32]).

During testing, participants were directed to gaze straight ahead at a 10 cm diameter red circle on a white background, positioned at eye level on a wall about five meters away. They were to remain motionless during stable conditions and keep the balancing board level during unstable conditions. After each trial, participants could take a one- to three-minute break but were not allowed to stand on the unstable platform during this time.

2.4. Data Analysis

2.4.1. EMG and COP Data Pre-Processing

All data processing was conducted using MATLAB™ version 2020a (MathWorks Inc., Natick, MA, USA). Individual EMG signals were filtered with a 2nd-order band-pass Butterworth filter with cutoff frequencies of 20–500 Hz to remove movement artifacts and high-frequency noise before rectification [40].

Anteroposterior (ap) and mediolateral (ml) center-of-pressure (COP) displacements were calculated using ground reaction forces (Fx and Fy) and moments of force (Mx and My), while accounting for the height ($h$) of the base of support above the force plate, to generate the COP signals [4].

$$\mathrm{C}\mathrm{O}\mathrm{P}\mathrm{a}\mathrm{p}=(-h\times Fx-My)/Fz$$

(1)

$$\mathrm{C}\mathrm{O}\mathrm{P}\mathrm{m}\mathrm{l}=(-h\times Fy+Mx)/Fz$$

(2)

Subsequently, a 3rd-order zero-phase 10 Hz low-pass Butterworth filter was applied to the EMG and COP signals, and the signals were down-sampled to 250 Hz [28]. Finally, 15,000 time points of EMG and COP data from the middle of the 60-s interval were chosen for further analysis [28].

2.4.2. Determining the EMG–COP Coordination

Ten 6-s subsets of EMG and COP data, each comprising 1500 time points, were extracted from the central 60 s of the recording [28,41]. The 6-s cross-correlation analysis window was chosen to focus on short-term temporal relationships between EMG signals and COP displacements, minimizing the impact of long-term lags that are less relevant for examining EMG-COP coordination [28]. Although the full 60-s data were used, dividing them into smaller subsets helped refine the results and reduce noise [28]. The strength of the correlation between individual EMG signals and COP displacements within each subset was assessed using normalized cross-correlation analysis, which measures the relationship between two signals [17]. This method improved the reliability of calculating the cross-correlation coefficient (r) at a specific time delay (τ) [28]. Correlation values of r (ǀrǀ) with absolute values ranging from 0.1 to 0.3 were classified as minor, 0.3 to 0.5 as moderate, and 0.5 to 1.0 as large [28]. For statistical comparison, the average ǀrǀ of each participant’s subsets was computed, and Fisher’s r-to-z transformation was applied to standardize the data [28].

2.5. Statistical Analysis

All statistical analyses were performed using SPSS software version 26.0 (IBM SPSS Statistics, SPSS Inc., Chicago, IL, USA), with the significance level set at α = 0.05. The normality of the variables was assessed using Shapiro–Wilk tests. A repeated-measures ANOVA was employed to evaluate the effects of four factors (leg dominance, foot position, surface stability, and sway direction). To control the family-wise error rate across 7 comparisons for each factor, the Holm–Bonferroni correction [42] was applied, resulting in an adjusted alpha level. p-values below 0.05 were considered significant, while those that did not meet the Bonferroni–Holm criterion [42] were regarded as indicative of a statistical trend.

3. Results

3.1. Overview of EMG–COP Correlation

Under stable conditions, the EMG–COP correlation coefficients exhibited a range from small to large ($\left|r\right|$ = 0.1 to 0.8) for both standing feet-apart and standing feet-together positions. Notably, median time delay ($\tau m$) alignments were only seen in the sagittal plane for the PL, GM, and SO muscles while standing feet apart. In contrast, the feet-together stance showed median time delay ($\tau m$) alignments in both COP directions, although this was limited to the GM and SO muscles.

For unstable conditions, the EMG–COP correlation coefficients for each participant ranged from small to large (|r| = 0.1 to 0.8) across both foot positions. During feet-apart standing, median time delay ($\tau m$) alignments were found in specific muscles for both COP directions: COPap alignments were seen in the RF, TA, GM, and SO muscles, while COPml alignments were observed in the GM and SO muscles. When standing with feet together, median time delay ($\tau m$) alignments were noted in both COPap and COPml directions across various muscles. Specifically, COPap alignments at the median time delays ($\tau m$) were evident in the RF, TA, GM, and SO muscles, while COPml alignments were found in the GM and SO muscles.

The overviews of individual cross-correlations between seven lower limb EMG signals and COP displacements in the anteroposterior (COPap) and mediolateral (COPml) directions during bipedal standing on stable and unstable surfaces are presented in the feet-apart and feet-together positions, as shown in Figure 2 and Figure 3, respectively.

3.2. Individual Pairs of EMG–COP Correlations

Table 2. The z-transformed EMG–COP correlation coefficients (mean ± Std. Error) were analyzed across four factors: (A) leg dominance, (B) surface stability, (C) sway direction, and (D) foot position. Note: the symbol # denotes p-values less than 0.05, and the symbol * indicates p-values meeting the Bonferroni–Holm criterion.

A: Leg Dominance
Muscle	ND	DO	p-Value	$\mathit{\eta }\mathit{p}$2	Power
RF	0.20 ± 0.01	0.20 ± 0.02	0.669	0.009	0.070
ST	0.23 ± 0.02	0.23 ± 0.02	0.933	0.000	0.051
BF	0.25 ± 0.02	0.23 ± 0.02	0.367	0.039	0.142
TA	0.30 ± 0.02	0.23 ± 0.01	0.004 *	0.325	0.859
PL	0.28 ± 0.02	0.30 ± 0.02	0.433	0.030	0.119
GM	0.46 ± 0.02	0.43 ± 0.02	0.190	0.080	0.253
SO	0.41 ± 0.02	0.42 ± 0.02	0.824	0.002	0.055
B: Surface Stability
Muscle	SS	US	p-Value	$\mathit{\eta }\mathit{p}$2	Power
RF	0.23 ± 0.02	0.16 ± 0.01	0.001 *	0.439	0.971
ST	0.29 ± 0.03	0.17 ± 0.01	<0.001 *	0.485	0.988
BF	0.30 ± 0.02	0.18 ± 0.01	<0.001 *	0.492	0.990
TA	0.25 ± 0.02	0.28 ± 0.01	0.238	0.066	0.213
PL	0.32 ± 0.02	0.25 ± 0.02	0.012 #	0.263	0.743
GM	0.54 ± 0.03	0.34 ± 0.02	<0.001 *	0.596	1
SO	0.50 ± 0.03	0.33 ± 0.02	<0.001 *	0.569	0.999
C: Sway Direction
Muscle	AP	ML	p-Value	$\mathit{\eta }\mathit{p}$2	Power
RF	0.22 ± 0.02	0.18 ± 0.01	0.081	0.138	0.418
ST	0.28 ± 0.03	0.18 ± 0.01	0.001 *	0.402	0.948
BF	0.26 ± 0.02	0.22 ± 0.01	0.085	0.135	0.407
TA	0.32 ± 0.02	0.22 ± 0.01	<0.001 *	0.519	0.995
PL	0.30 ± 0.02	0.28 ± 0.02	0.356	0.041	0.147
GM	0.47 ± 0.02	0.42 ± 0.02	0.060	0.158	0.475
SO	0.45 ± 0.02	0.39 ± 0.02	0.027 #	0.211	0.618
D: Foot Position
Muscle	FA	FT	p-Value	$\mathit{\eta }\mathit{p}$2	Power
RF	0.20 ± 0.02	0.19 ± 0.01	0.608	0.013	0.079
ST	0.25 ± 0.03	0.21 ± 0.02	0.138	0.102	0.313
BF	0.24 ± 0.02	0.23 ± 0.02	0.600	0.013	0.080
TA	0.25 ± 0.01	0.29 ± 0.01	0.056	0.163	0.488
PL	0.26 ± 0.01	0.31 ± 0.02	0.057	0.162	0.484
GM	0.43 ± 0.02	0.46 ± 0.02	0.317	0.048	0.165
SO	0.39 ± 0.02	0.45 ± 0.02	0.021 #	0.229	0.663

presents the mean values of the absolute z-transformed EMG–COP correlation coefficients (|r|) assessed across four influencing factors: leg dominance, surface stability, sway direction, and foot position. The average |r| values range from 0.2 to 0.5, reflecting a spectrum of low-to-high EMG–COP correlations. These variations depend on specific muscles and factors. Notably, the highest |r| value is observed in the GM muscle under stable conditions, attributed to the impact of surface stability. The main findings indicate that specific factors—leg dominance, surface stability, and sway direction—affect different muscles under particular conditions. For leg dominance, a higher correlation coefficient is observed for the TA muscle (p = 0.004) during the non-dominant leg (ND) stance compared to the dominant leg (DO) stance. Concerning surface stability, stronger correlation coefficients are noted for the RF (p = 0.001), ST (p < 0.001), BF (p < 0.001), GM (p < 0.001), and SO (p < 0.001) muscles when balancing on a stable surface (SS) versus an unstable surface (US). Regarding sway direction, higher correlation coefficients are seen for the ST (p = 0.001) and TA (p < 0.001) muscles in the anteroposterior (AP) direction compared to the mediolateral (ML) direction.

Interaction effects also reveal significant relationships between specific factor pairs and targeted muscles. Notably, the interaction between surface stability and foot position significantly affects the RF (p = 0.001), ST (p = 0.001), BF (p < 0.001), TA (p < 0.001), PL (p < 0.001), GM (p < 0.001), and SO (p < 0.001) muscles. The interaction between leg dominance and foot position is significant for the PL (p = 0.002) and GM (p = 0.002) muscles. Additionally, the interaction between leg dominance and sway direction is significant for the GM (p = 0.003) muscle. Lastly, the interaction of sway direction with itself is significant for the PL muscle (p < 0.001).

4. Discussion

The current study investigated the temporal similarities in shape between electromyography (EMG) signals from seven lower limb muscles and center-of-pressure (COP) signals, known as the EMG–COP correlation, in physically active young adults performing bipedal balancing tasks on both stable and multiaxially unstable surfaces. This study focused on leg dominance, surface stability, sway direction, and foot position. The findings revealed significant effects of leg dominance, surface stability, and sway direction on the EMG–COP correlations for specific muscles.

Based on the current empirical findings, three points can be discussed. First, this study reveals a significant asymmetry in muscle activity related to leg dominance, particularly in the tibialis anterior (TA) muscle. A higher correlation coefficient for the TA is observed during non-dominant leg stance compared to dominant leg stance. This suggests that even during bipedal balance tasks, the non-dominant leg plays a more prominent role in postural control [43], compensating for the dominant leg’s reduced involvement, aligning with the idea that the non-dominant leg plays a more prominent role in maintaining balance during challenging tasks [44]. This asymmetry is especially important in bimanual motor tasks, such as standing, which require coordinated muscle activity between both legs to maintain an upright posture [41]. Relying on one leg for stability in these tasks could increase the risk of injury [19,20], as muscle fatigue and overuse may result from unequal load distribution. Therefore, balancing muscle engagement across both legs in bimanual tasks should be a key focus in training and injury prevention strategies [45]. This finding underscores the need to give greater attention to leg dominance when assessing balance and developing injury prevention protocols [24,45]. Second, this study emphasizes the crucial roles of thigh muscles (RF, ST, BF) and plantarflexors (GM, SO) in maintaining bipedal balance on stable surfaces, as these muscles counteract postural sway by stabilizing the knee and ankle joints [28]. Unlike the tibialis anterior (TA) and peroneus longus (PL), which are key for postural control during unipedal balance [28], the thigh and plantarflexor muscles are more essential for bipedal stability. Their role diminishes on unstable surfaces, supporting prior assumptions that unstable surfaces challenge postural stability [12,25,26,27], disrupting upright stances by reducing sensory input and compromising ankle torque, thereby requiring a shift in muscle recruitment and postural strategies to maintain balance [32]. The decreased influence of these muscles on unstable surfaces may indicate a shift in reliance on other muscles or strategies to maintain balance. Third, this study highlights distinct roles for specific muscles in controlling bipedal postural stability across the sagittal and frontal planes. The semitendinosus (ST) and tibialis anterior (TA) muscles show stronger correlations with anteroposterior (AP) postural sway than mediolateral (ML) sway, reflecting their key roles in forward–backward stability similarly to unipedal stance [28]. Interestingly, while ST and TA correlate with ML sway during unipedal balancing [28], this pattern is not observed in bipedal standing, as evidenced in this study. This suggests that muscle recruitment differs between unipedal and bipedal tasks, emphasizing the need for targeted training based on specific activity demands [28,46].

In terms of practical applications, the current results highlight the critical role of specific muscles in maintaining balance [28] and the importance of recognizing asymmetry in lower limb muscle activation [47]. This is particularly relevant for developing injury prevention and rehabilitation interventions, as sports-related lower limb injuries often differ in prevalence between the dominant and non-dominant legs [21,22,23,24]. While injury mechanisms related to laterality vary across sports [48], addressing differences in interlimb neuromuscular control could help prevent and rehabilitate such injuries. Furthermore, when designing exercises for postural control, it is essential to consider the role of postural muscles, such as the lower limb muscles, along with factors like surface stability and sway direction [49]. The observed asymmetries in tibialis anterior activity on the non-dominant leg and the differing contributions of thigh and plantarflexor muscles to postural stability emphasize the need to address these imbalances during training and rehabilitation. Sports and physical activities often demand rapid and precise adjustments to postural sway, particularly on unstable surfaces [49]. Therefore, incorporating exercises that strengthen muscles like the semitendinosus, rectus femoris, and soleus while mimicking sport-specific movements could improve balance performance and reduce injury risk. Although the direct effect of foot position was not observed in the main effects, its interaction with surface stability had a significant impact on all measured muscles, supporting the idea that adjusting foot position (e.g., reducing the supporting area) could improve neuromuscular control [32,50]. For example, a feet-together position may enhance the effectiveness of balancing exercises on stable, rigid surfaces.

Limitations and Future Research

One limitation of this study is its focus on the strength of EMG–COP correlations without exploring other types of correlations, such as positive or negative cross-correlations. These variations could provide deeper insights into specific muscle contributions to postural control [28,46]. Future research could examine the relationship between lower limb muscle activity and postural stability in individuals with and without a history of injuries (e.g., ankle or knee injuries from sports), which could inform sports-related injury prevention and rehabilitation [49]. Expanding the participant range to include different age groups and activity levels would enhance the generalizability of the findings.

Another limitation is the reliance on cross-correlation analysis, which, while useful for examining temporal relationships [16,17], may not capture the non-linear dynamics of neuromuscular control [51]. This suggests the need for alternative methods to gain a more comprehensive understanding of postural control.

Additionally, the current study focused exclusively on young, healthy participants, which may not fully represent neuromuscular control mechanisms in individuals with traumatic injuries. Future studies could involve individuals with musculoskeletal injuries to explore how trauma affects postural stability and muscle coordination, using healthy participants as controls. Research could also assess how myoelectric activity influences postural stability across different populations, aiding in rehabilitation and injury prevention. Finally, future work could explore the effects of varying surfaces, footwear, and environmental factors, as well as long-term studies to understand how neuromuscular control evolves with age or rehabilitation.

5. Conclusions

This study found significant variations in the cross-correlation between lower limb electromyography (EMG) and center of pressure (COP) during bipedal balance tasks in healthy, active young adults. Leg dominance, surface stability, and sway direction influenced these correlations. The tibialis anterior showed a stronger correlation with postural sway on the non-dominant leg. On stable surfaces, muscles such as the rectus femoris, semitendinosus, biceps femoris, gastrocnemius medialis, and soleus had stronger correlations with postural sway, which decreased on unstable surfaces. Additionally, anteroposterior sway was more strongly correlated with semitendinosus and tibialis anterior activity compared to mediolateral sway. These findings underscore the key role of specific muscles in maintaining bipedal postural control, with implications for balance enhancement across various populations.