Wearable Sensor Assessment of Neuromuscular Latency: Revealing the Strength-Timing Trade-off in Female Soccer

Wearable Sensor Assessment of Neuromuscular Latency: Revealing the Strength-Timing Trade-off in Female Soccer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Wearable Sensor Assessment of Neuromuscular Latency: Revealing the Strength-Timing Trade-off in Female Soccer Özlem Köklü This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8807183/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background The high incidence of anterior cruciate ligament (ACL) injuries in female soccer players persists despite widespread preventive interventions. Traditional screening relies heavily on isokinetic torque ratios to assess mechanical joint stability; however, this approach often fails to capture the temporal dynamics of sensorimotor control. Wearable wireless electromyography (sEMG) provides a viable modality to assess these neuromuscular latency deficits. Methods Twenty-one female soccer players (age: 17.60 ± 0.87 years) underwent reciprocal concentric isokinetic testing at 60°/s, 180°/s, and 240°/s. The hamstring-quadriceps torque ratio was measured via an isokinetic dynamometer and normalized to body weight. Simultaneously, neuromuscular latency was acquired using a wearable wireless sEMG system (BTS FreeEMG) on the vastus lateralis and semitendinosus. Neuromuscular latency was quantified using a computerized threshold algorithm to determine the agonist-antagonist asynchrony. Results A significant main effect of angular velocity was observed on neuromuscular latency (p < 0.05), which decreased with increasing velocity, reflecting feed-forward adaptation. A positive correlation emerged between the mechanical H/Q torque ratio and neuromuscular latency, most notably at 60°/s (r = 0.792) and 240°/s (r = 0.681). This indicates a paradoxical latency-strength mismatch, in which players with superior mechanical torque ratios exhibit significantly delayed neuromuscular reflexive responses. Conclusions Static mechanical symmetry does not guarantee dynamic temporal efficiency. The identified latency-strength mismatch suggests that standard dynamometry may mask critical sensorimotor deficits. The integration of wearable wireless sEMG technology into injury risk screening is essential to capture these temporal asymmetries and ensure that mechanical capacity is matched by rapid neural drive. Wearable Technology Wireless Electromyography Neuromechanical Coupling ACL Injury Prevention Female Soccer Figures Figure 1 Figure 2 INTRODUCTION The rapid growth of women's soccer has driven increased professionalization and imposed greater physical demands on players. However, a substantial gender disparity persists in injury epidemiology, with female players reporting a significantly higher incidence of severe lower limb injuries, particularly anterior cruciate ligament (ACL) ruptures, compared to male players [ 1 , 2 ]. Despite the widespread implementation of injury prevention programs, the rate of non-contact knee injuries remains high [ 3 ]. This persistence implies that conventional screening methods, which primarily emphasize anatomical characteristics or broad strength assessments, may miss subtle functional deficits that stray from the ideal homeostatic balance, thereby heightening players' risk of injury. Neuromuscular latency, which reflects the central nervous system's (CNS) capacity to coordinate muscle activation, is critical for maintaining dynamic joint stability and preventing loss of functional control [ 4 , 5 ]. Dynamic knee stability hinges on the precise balance of the opposing forces generated by the agonist and antagonist muscle groups. Conventionally, this relationship is quantified utilizing the hamstring-quadriceps (H/Q) peak torque ratio [ 6 ]. In the context of biological function, the knee joint operates as a system that strives for intra-limb equilibrium. An optimal H/Q torque ratio represents muscular strength balance a capacity wherein antagonist forces sufficiently counteract the agonist drive to preserve joint integrity. However, reliance on this static representation of the balance may be misleading. ACL injuries typically occur within 30–50 ms of ground contact, whereas peak muscle tension requires 300–500 ms to develop [ 7 ]. Consequently, the hamstring's capacity for rapid activation before the attainment of peak torque is likely more critical for joint protection than absolute strength alone [ 5 ]. Crucially, stability in biological systems is not merely static but dynamic. We propose that neuromuscular latency, the synchronization between the onset of agonist acceleration and antagonist braking, is paramount. Current screening methods fail because they measure force capacity but ignore force timing. A delayed antagonist response represents a critical neuromuscular deficit. This delay compromises dynamic joint stability, forcing the system into a reactive rather than predictive state, which significantly increases injury risk. The seamless coupling between agonist action and antagonist response signifies the temporal efficiency of the neuromuscular system [ 8 ]. If a strong hamstring exhibits a delayed firing pattern, a state of functional imbalance exists, rendering the joint vulnerable despite a seemingly safe H/Q torque ratio [ 9 ]. While isokinetic dynamometry remains the gold standard for assessing mechanical muscle balance, its utility is limited by its stationary, laboratory-based nature. In contrast, the rapid advancement of wearable technology, particularly wireless electromyography (sEMG) sensors, provides an opportunity to assess neuromuscular function in ecological settings [ 4 , 8 ]. However, the effective deployment of wearables requires the identification of valid physiological metrics that transcend simple force output [ 5 ]. If a dissociation exists between muscular strength balance (measured by dynamometers) and neuromuscular latency (measured by sensors), it will validate the specific need for wearable wireless sEMG as an essential, distinct component of injury risk screening, rather than a redundant tool. Despite the importance of these factors, few studies have integrated strength and latency variables across the high angular velocities characteristic of elite sport. Most research evaluates these parameters in isolation or at low speeds (e.g., 60°/s), which fail to replicate the dynamic demands of competitive match play. It remains unclear whether female players who possess muscular strength balance (high H/Q torque ratio) also exhibit the necessary neural drive (low latency) to maintain stability at high velocities [ 10 – 12 ]. This study introduces the latency-strength mismatch to quantify this deficit. This mismatch may represent a latent risk factor with significant implications for injury prevention strategies in sports science. Therefore, the primary objective of this study was to investigate the relationship between isokinetic H/Q torque ratios (muscular strength balance) and agonist-antagonist neuromuscular latency (neuromuscular control) across varying angular velocities (60°/s, 180°/s, and 240°/s) in female football players. This study hypothesized that maintaining strength balance (high H/Q torque ratios) at high velocities would theoretically require a proportional increase in neuromuscular control efficiency (reduced neuromuscular latency); however, a divergence between these two parameters would indicate a functional deficit. MATERIALS AND METHODS Experimental Approach to the Problem This study employed a cross-sectional laboratory design to assess neuromuscular function under velocity-specific conditions. Participants attended a single testing session, scheduled between 14:00 and 17:00 h to control for circadian variation and minimize fatigue-induced bias. A standardized testing order was implemented: (1) a standardized warm-up [ 14 ], followed by (2) concurrent isokinetic and electromyographic assessments. A reciprocal concentric-concentric protocol was employed to measure the torque ratios at soccer-relevant velocities. Before data collection, participants performed a familiarization protocol consisting of three submaximal repetitions at each velocity. Following familiarization, Isokinetic testing was conducted in an ascending velocity order (60, 180, 240°/s) to optimize performance and limit fatigue. To ensure adequate recovery and maintain high power output, a 15-second brief interval was provided between repetitions within each bout, while a 120-second passive rest period was mandated between sets of different velocities [ 15 ]. These specific velocities were selected to identify the velocity at which the mechanical capacity of the muscle might dissociate from its temporal activation capability [ 10 ]. The quadriceps repetition with the highest peak torque was chosen for data analysis at each velocity. The corresponding H/Q torque ratio and neuromuscular latency values from this specific repetition were extracted for statistical analysis to ensure temporal coupling between muscle strength and neuromuscular control. Participants Twenty-one female soccer players from a competitive youth soccer academy, actively participating in an amateur league, volunteered to participate in this study. All participants had a minimum of three years of competitive experience. Exclusion criteria were established to ensure the integrity of the neuromuscular data: (1) any lower extremity musculoskeletal injury within the preceding six months, (2) history of knee reconstructive surgery, (3) any neurological disorder affecting motor control, or (4) those whose dominant leg is their preferred kicking leg. Although bilateral assessment is common, this study focused exclusively on the dominant limb (defined as the preferred kicking leg) to isolate the intra-limb asymmetry (agonist vs. antagonist) specifically associated with high-velocity ball-striking and landing mechanisms that are frequent in the dominant leg [ 13 ]. The descriptive characteristics of the participants were as follows: age, 17.60 ± 0.87 years; Body Mass, 55.40 ± 4.01 kg; height, 165.30 ± 4.33 cm; and Sports Age, 3.31 ± 1.55 years. Before testing, all participants (and guardians of those under 18 years of age) provided written informed consent. The study was conducted in accordance with the Declaration of Helsinki and was approved by the University Ethics Committee (Protocol No: E-60116787-020-797301). Surface electromyography (sEMG) : Surface electromyography was recorded simultaneously during isokinetic tasks using a wearable wireless sEMG system (FREEEMG 1000, BTS Bioengineering, Milan, Italy). This non-invasive, lightweight system allows for data acquisition without impeding the movement range of motion, simulating the sensor configuration used in field-based wearable applications. Before electrode placement, the skin over the vastus lateralis (VL) and semitendinosus (ST) muscles was shaved, abraded, and cleaned with 70% ethanol to reduce impedance below 5 kΩ. Bipolar Ag/AgCl electrodes (10 mm diameter, 20 mm inter-electrode distance) were positioned on the muscle bellies parallel to the fibers, strictly following the SENIAM guidelines (VL: 2/3 on the line from the anterior spina iliaca superior to the lateral side of the patella; ST: 50% on the line between the ischial tuberosity and the medial epicondyle of the tibia). A reference electrode was placed on the electrically neutral tibial tuberosity. All sensor placements were conducted by the same researcher. The VL and ST were selected as the primary agonist-antagonist pairs for sagittal-plane knee control. All signals were sampled at a frequency of 2000 Hz to ensure high temporal resolution. Isokinetic Dynamometry Knee extensor and flexor strength were assessed using a calibrated Biodex System 4 Pro isokinetic dynamometer (Biodex Medical Systems, Shirley, NY, USA). Participants were seated with the hip flexed at 85° and secured via shoulder harnesses, a lap belt, and a thigh strap to isolate knee motion. The dynamometer axis was aligned with the lateral femoral epicondyle. Gravity correction was performed for each participant by weighing the limb at 30 degrees of knee flexion to account for limb mass. The knee flexion-extension range was standardized to 0°–90° [ 16 ]. Torque data were normalized to body mass (Nm/kg), and the conventional concentric H/Q torque ratio was utilized as the primary index of muscular strength balance [ 6 ]. System Synchronization To eliminate inter-device phase shifts, the isokinetic dynamometer and wearable wireless sEMG system were synchronized via an analog voltage signal from the Biodex System 4 Pro, which was integrated into an auxiliary channel of the BTS EMG unit. This hardware interface enabled the simultaneous acquisition of torque, joint angle, and sEMG data on a common time axis, ensuring precise temporal alignment for neuromuscular latency calculations. Data Processing and Signal Analysis Raw sEMG signals from the VL and ST were processed using a customized computerized threshold algorithm to determine the precise timing of muscle activation. To ensure signal integrity, all raw data were sampled at 2000 Hz and band-pass filtered using a fourth-order Zero-lag Butterworth filter with a frequency range of 20–500 Hz to eliminate motion artifacts and high-frequency noise. Neuromuscular latency was defined as the temporal interval between the initiation of agonist acceleration and the subsequent onset of antagonist muscle activity. To identify the exact activation point, a sliding window root-mean-square (RMS) algorithm with a 20 ms window size was applied. The onset threshold was established at three standard deviations (3 SD) above the mean baseline activity measured during the initial quiet resting phase of the test. To ensure the robustness of the latency-strength mismatch analysis, only signals maintaining activity above this threshold for at least 25 consecutive milliseconds were considered for latency calculation, thereby preventing the inclusion of transient spikes or noise. Neuromuscular latency was calculated as the absolute time interval between the antagonist onset and the agonist onset: Neuromuscular Latency (ms) = |Onset Antagonist - Onset Agonist | A value of 0 ms indicates perfect synchronization, whereas positive values indicate a phase lag (neuromuscular asynchrony). Absolute values were utilized to account for pre-activation strategies where the antagonist might fire prior to the agonist. All data processing and algorithmic computations were performed using MATLAB R2023a, ensuring the high degree of temporal precision required to identify functional deficits within the critical 30–50 ms injury window. Statistical Analysis A prior power analysis was conducted using G*Power (version 3.1.9.7, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). For a repeated-measures Analysis of Variance (ANOVA) (within factors), with an alpha level of 0.05, a power of 0.80, and a large effect size ( \(\:\text{f}=0.40\) ) based on similar isokinetic studies, a minimum sample size of 18 participants was required. Thus, the current sample of 21 participants provided sufficient statistical power. The normality of the data distribution was verified using the Shapiro-Wilk test. Descriptive statistics are presented as mean±standard deviation. Repeated measures ANOVA was used to compare the H/Q torque ratios and neuromuscular latencies across the three angular velocities. Bonferroni post-hoc tests were used to identify specific pairwise differences between the groups. Effect sizes for the ANOVA were reported as partial eta squared ( \(\:{{\eta\:}}_{\text{p}}^{2}\) ). Pearson correlation coefficients ( \(\:r\) ) were calculated to determine the relationship between muscle strength balance and neuromuscular control at each velocity. The magnitude of the relationship was interpreted according to Cohen’s guidelines: ( \(\:\text{r}=0.10\text{\--}0.29\) ), medium ( \(\:\text{r}=0.30\text{\--}0.49\) ), and large ( \(\:\text{r}\ge\:0.50\) ) [ 18 ]. All statistical analyses were performed using SPSS Statistics (Version 26.0, IBM Corp., Armonk, NY, USA). The level of significance was set at \(\:\text{p}<0.05\) . RESULTS Velocity-Dependent Adaptations of Neuromuscular Latency Repeated measures ANOVA revealed a statistically significant main effect of angular velocity on neuromuscular latency. As illustrated in Fig. 1 .B, latency was significantly reduced as velocity increased, decreasing from 35 ± 5ms at 60°/s to 23 ± 3 ms at 240°/s. The longest latency period was observed at the lowest velocity (60°/s), whereas the shortest latency period was observed at the highest velocity (240°/s). Post-hoc pairwise comparisons confirmed that significant differences existed between all velocity conditions ( \(\:p<0.05\) ). The descriptive statistics and confidence intervals are presented in Table 1 . Table 1 Changes in neuromuscular latency across angular velocities (n = 21) Angular Velocity Mean Latency (ms) SD 95% CI [Lower, Upper] Pairwise Differences 60°/s 35 5 [0.33, 0.37] a, b 180°/s 25 3 [0.24, 0.26] a, c 240°/s 23 3 [0.22, 0.24] b, c SD = Standard Deviation; CI = Confidence Interval. ANOVA results : \(\:\text{F}\:\left(\text{2,40}\right)=26.408\) , \(\:p=0.002\) , \(\:{\eta\:}_{p}^{2}=0.57\) . Superscript letters indicate significant pairwise differences ( \(\:p<0.05\) ): a (60° vs. 180°), b (60° vs. 240°), c (180° vs. 240°). ***Table around here*** ***Figure 1 around here*** Velocity-Dependent Adaptations of H/Q Ratio The analysis of muscular strength balance indices demonstrated a significant main effect of angular velocity. As presented in Table 2 and visualized in Fig. 1 .A, H/Q torque ratios increased from 60°/s to 180°/s, then stabilized. The H/Q torque ratio was lowest at 60°/s and significantly increased at 180°/s. Although a slight decrease was observed in the 240s compared to 180°/s, the values remained significantly higher than those observed in the 60s, indicating a velocity-induced shift toward a more balanced agonist-antagonist relationship. Table 2 Changes in muscular strength balance (H/Q Ratio) across angular velocities ( \(\:n=21\) ). Angular Velocity Mean H/Q Torque Ratio SD 95% CI [Lower, Upper] Balance Interpretation Pairwise Differences 60°/s 0.49 0.09 [0.45, 0.53] Quadriceps Dominant b, c 180°/s 0.55 0.08 [0.51, 0.59] Improved Balance a, c 240°/s 0.53 0.08 [0.49, 0.57] Moderate Balance a, b SD = Standard Deviation; CI = Confidence Interval. Superscript letters indicate significant pairwise differences ( \(\:\text{p}<0.05\) ): a (60° vs. 180°), b (60° vs. 240°), c (180° vs. 240°). Balance interpretation is based on the proximity to the normative 0.60 threshold. ***Table 2 around here*** Correlation between H/Q Torque Ratio and Neuromuscular Control Pearson product-moment correlation analysis was conducted to examine the relationship between the H/Q torque ratio and neuromuscular latency. The complete correlation matrix is presented in Table 3 . Specifically, a positive correlation was found at 60°/s ( \(\:r=0.792,p<0.001\) ) and 240°/s ( \(\:r=0.681,p<0.001\) ), while a moderate correlation was observed at 180°/s ( \(\:r=0.589,p=0.005\) ). These results indicate a potential trade-off, with participants exhibiting greater neuromuscular control when they had a greater muscle strength balance (higher H/Q torque ratios). This relationship is illustrated in Fig. 2 . Table 3 Relationship between muscular strength balance (H/Q torque ratio) and neuromuscular control (Neuromuscular latency) ( \(\:n=21\) ). 60°/s Neuromuscular latency r ( p ) 180°/s Neuromuscular latency r ( p ) 240°/s Neuromuscular latency r ( p ) 60°/s H/Q Torque Ratio 0.792 (< 0.001) * 0.615 (0.003) * 0.388 (0.082) 180°/s H/Q Torque Ratio 0.645 (0.002) * 0.589 (0.005) * 0.406 (0.068) 240°/s H/Q Torque Ratio 0.512 (0.018) * 0.577 (0.006) * 0.681 (< 0.001) * Note: * indicates statistical significance ( \(\:\text{p}<0.05\) ). Bold values indicate correlations at matched velocities. P-values are calculated for N=21 ***Table 3 around here*** ***Figure 2 around here*** DISCUSSION The primary objective of this study was to investigate the relationship between muscle strength balance as quantified by the H/Q torque ratio, and neuromuscular control, as defined by agonist-antagonist neuromuscular latency across a range of angular velocities in female soccer players. The key findings indicate an inverse relationship between neuromuscular latency and movement speed, alongside a significant effect of angular velocity on the H/Q torque ratio, with higher H/Q ratios generally correlating with longer neuromuscular latencies. The primary finding of this study reveals a functional trade-off: players exhibiting superior muscular strength balance displayed greater neuromuscular latency. This suggests that static mechanical equilibrium does not necessarily guarantee dynamic temporal synchronization [ 5 ]. Velocity-Dependent Adaptations of Neuromuscular Control Our results demonstrated a clear velocity-dependent adaptation of neuromuscular latency, with latency decreasing significantly from 35 ms at 60°/s to 23 ms at 240°/s. This observed inverse relationship aligns with the existing literature, where increased movement velocity often necessitates a more rapid muscular response to maintain dynamic stability, thereby reducing latency [ 26 ]. Both contribute to neuromuscular latency, which is a critical factor in dynamic joint stability. At high angular velocities (240°/s), the neuromuscular system undergoes a state transition. The reduction in latency may represent a compensatory mechanism to preserve neuromuscular control when the window for afferent feedback closes. The CNS shifts from a reactive (high-latency) to a predictive (low-latency) mode to minimize errors in joint stabilization. This transition likely represents a critical adaptation in the neural strategy, where the system abandons feedback-dependent control in favour of feedforward mechanisms to prevent mechanical failure during rapid, high-intensity movements characteristic of soccer [ 40 ]. Relationship Between Muscular Strength and Neuromuscular Timing A key finding of this study is the positive correlation between the H/Q torque ratio and neuromuscular latency. Specifically, players with higher strength balance tended to exhibit slower muscle activation, particularly at 60°/s. Specifically, participants with more favourable muscle strength ratios exhibited slower muscle reaction times. This inverse correlation (r = 0.81 at 60°/s) suggests strategic divergence in homeostatic regulation. Players with high muscular strength balance (stronger hamstrings) may rely on inherent muscle-tendon stiffness to absorb energy, creating a safety margin that permits a relaxation of timing constraints (slower onset). Conversely, players with lower muscle strength must compensate by enforcing faster activation to prevent joint injuries [ 37 ]. This pattern suggests a compensatory strategy in which mechanical stiffness offsets the need for neural speed. Physiological Mechanisms Underlying the Latency-Strength Mismatch This dissociation highlights the specificity of training adaptations. The high H/Q torque ratios observed in our participants may be a result of hypertrophy-oriented resistance training. While such training effectively increases contractile cross-sectional area and peak torque [ 33 , 51 ], it does not necessarily enhance signal transmission speeds. Furthermore, hypertrophy can alter muscle architecture, specifically by increasing the pennation angle. Although this enhances force production capacity, it may inadvertently prolong the time required to take up muscle slack, the initial compliance of the muscle-tendon unit [ 51 ]. While muscle slack is a likely mechanism, neural inhibition at high velocities may also contribute to the observed neuromuscular latency. This structural adaptation at the fiber level may directly influence the functional outcome, creating the observed latency-strength mismatch. Implications for Neuromuscular Training and Injury Prevention The observed latency-strength mismatch suggests that current training methodologies may benefit from refinement to simultaneously optimize both static torque capabilities and rapid neuromuscular latency, potentially mitigating injury risk more effectively [ 50 ]. Training paradigms should aim to recouple strength and timing. High-velocity ballistic training may be essential to reduce the activation latency in players with high hypertrophy, ensuring that their mechanical potential is accessible within the critical injury window [ 52 ]. Therefore, the stronger players in our cohort may possess high torque capabilities but lack the specific proactive neural adaptations (e.g., increased motor unit discharge rates or doublet firing) that characterize rapid neuromuscular responses. This supports the distinction made by Dideriksen et al. [ 24 ] that maximal strength and rapid force production are separate motor qualities governed by different physiological mechanisms. Consequently, optimizing injury prevention strategies likely necessitates a training paradigm that transcends mere hypertrophy or strength enhancement to include targeted interventions, such as reactive plyometrics and velocity-specific drills, aimed at improving the speed and coordination of neuromuscular co-activation to enhance dynamic joint stability [ 5 , 54 ]. CONCLUSIONS This study identified velocity-dependent changes of neuromuscular latency in female soccer players. The observed positive correlation suggests a latency-strength mismatch, where superior muscular strength does not guarantee rapid reflexive responses. Screening protocols should integrate high-velocity testing and wearable technology to detect these hidden neuromuscular deficits. Practical Implications According to the velocity-dependent latency deficits revealed in this study, practitioners might consider a two-tiered approach to injury prevention: Velocity-Specific Screening Standard clinical assessments rely on low-velocity strength measurements (60°/s). However, our data indicates that deficits in neuromuscular latency are most critical at high velocities (240°/s), where the feed-forward mechanism dominates. Therefore, screening protocols should ideally include high-velocity isokinetic testing. Reactive Agility Training Since neuromuscular latency accounts for a significant portion of injury risk, training must target its reduction. Coaches should require players to decelerate or cut in response to a visual stimulus within a temporal window of < 300 ms, forcing the CNS to streamline signal transmission and improve co-contraction under time pressure [ 41 ]. Integration of Wearable Technology The identification of the latency-strength mismatch highlights the necessity of integrating both mechanical and temporal assessments within injury risk screening and training program design. Wearable technologies, such as advanced sEMG systems and integrated inertial measurement units, offer promising avenues for continuous, ecologically valid monitoring and feedback in these contexts. Limitations and Future Research This study's cross-sectional design limits causal inferences about neuromuscular latency, training adaptations, and ACL injury risk. Fixed-velocity isokinetic tests also fail to capture the dynamics of high-speed eccentric actions vital for injury screening [ 36 ]. Additionally, while this study focused on the dominant (kicking) limb to assess intra-limb agonist-antagonist coupling, it is important to acknowledge that the non-dominant (stance) limb is frequently the site of non-contact ACL injury during planting and cutting maneuvers. Future research should investigate whether the observed latency-strength mismatch is bilateral or if inter-limb asymmetries in neuromuscular timing further exacerbate injury risk. Abbreviations ACL Anterior Cruciate Ligament ANOVA Analysis of Variance CI Confidence Interval CNS Central Nervous System H/Q Hamstring-to-Quadriceps (Ratio) Hz Hertz ms Milliseconds RMS Root Mean Square SD Standard Deviation sEMG Surface Electromyography ST Semitendinosus VL Vastus Lateralis Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE The ethics committee of Pamukkale University approved the study protocol (decision no. 797301, dated 18/12/2025). The study was conducted in accordance with the principles outlined in the Declaration of Helsinki, ensuring respect for the rights and dignity of the participants. Prior to participating in the study, informed written consent was obtained from all participants and, when applicable, their parents. CONSENT FOR PUBLICATION Not applicable. The manuscript does not contain any person’s data in any form (including images, videos, or personal details). COMPETING INTERESTS The authors declare that they have no competing interests. CLINICAL TRIAL NUMBER Not applicable. Author Contribution Ö.K. is the sole author of this research. The author conceptualized the study, performed data collection and analysis, and drafted the manuscript. The final version of the manuscript has been read and approved by the author. Acknowledgments: The author would like to thank all the athletes who volunteered for this study. Special thanks are also due to the laboratory personnel for their technical support during data collection. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. 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Effect of Hamstring-to-quadriceps Ratio on Knee Forces in Females During Landing. Int J Sports Med. 2020;42(3):264–70. https://doi.org/10.1055/a-1128-6995 . Hewett TE, Myer GD, Zazulak BT. Hamstrings to quadriceps peak torque ratios diverge between sexes with increasing isokinetic angular velocity. J Sci Med Sport. 2007;11(5):452–9. https://doi.org/10.1016/j.jsams.2007.04.009 . Hooren BV, Aagaard P, Blazevich AJ. Optimizing Resistance Training for Sprint and Endurance Athletes: Balancing Positive and Negative Adaptations. Sports Med. 2024;54(12):3019–39. https://doi.org/10.1007/s40279-024-02110-4 . Huston LJ, Wojtys EM. Neuromuscular Performance Characteristics in Elite Female Athletes. Am J Sports Med. 1996;24(4):427–36. https://doi.org/10.1177/036354659602400405 . Kacprzak B, Stańczak M, Surmacz J, Hagner-Derengowska M. Biophysics of ACL Injuries. Orthop Rev. 2024;16. https://doi.org/10.52965/001c.126041 . Kline PW, Morgan KD, Johnson DL, Ireland ML, Noehren B. Impaired Quadriceps Rate of Torque Development and Knee Mechanics After Anterior Cruciate Ligament Reconstruction With Patellar Tendon Autograft. Am J Sports Med. 2015;43(10):2553–61. https://doi.org/10.1177/0363546515595834 . Korpinen MM, Trieschock D, Fields JB, Jagim AR, Almonroeder TG, Jones MT. Hamstring-to-Quadriceps Strength Ratios in Women Team Sport Athletes: A Systematic Review. Strength Cond J. 2024;46(1):95–108. https://doi.org/10.1519/SSC.0000000000000867 . Landry SC, McKean KA, Hubley-Kozey CL, Stanish WD, Deluzio KJ. Neuromuscular and Lower Limb Biomechanical Differences Exist between Male and Female Elite Adolescent Soccer Players during an Unanticipated Side-cut Maneuver. Am J Sports Med. 2007;35(11):1888–900. https://doi.org/10.1177/0363546507300823 . Maniar N, Cole MH, Bryant AL, Opar DA. Muscle Force Contributions to Anterior Cruciate Ligament Loading. Sports Med. 2022;52(8):1737–50. https://doi.org/10.1007/s40279-022-01674-3 . McMahon JJ, Suchomel TJ, Lake JP, Comfort P. Understanding the Key Phases of the Countermovement Jump Force-Time Curve. Strength Cond J. 2018;40(4):96–106. https://doi.org/10.1519/ssc.0000000000000375 . Myer GD, Chu DA, Brent JL, Hewett TE. Trunk and hip control neuromuscular training for the prevention of knee joint injury. Clin Sports Med. 2008;27(3):425–48. https://doi.org/10.1016/j.csm.2008.02.006 . Myer MA, Ford JM, Hewett KR. A review of neuromuscular training and biomechanical risk factor screening for ACL injury prevention among female soccer players. Sports Health. 2022;14(3):345–53. https://doi.org/10.1177/19417381221106235 . Pietraszewski P, Maszczyk A, Zając A, Gołaś A. Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review. Appl Sci. 2025;15(9):4959. https://doi.org/10.3390/app15094959 . Ramírez-Campillo R, Gallardo F, Henriquez-Olguín C, Meylan CMP, Martínez C, Álvarez C, et al. Effect of vertical, horizontal, and combined plyometric training on explosive, balance, and endurance performance of young soccer players. J Strength Cond Res. 2015;29(7):1784–95. https://doi.org/10.1519/JSC.0000000000000834 . Ritzmann R, Strütt S, Torreno I, Riesterer J, Centner C, Suárez-Arrones L. Neuromuscular characteristics of agonists and antagonists during maximal eccentric knee flexion in soccer players with a history of hamstring muscle injuries. PLoS ONE. 2022;17(12):e0277949. https://doi.org/10.1371/journal.pone.0277949 . Rodríguez-Rosell D, Pareja-Blanco F, Aagaard P, González-Badillo JJ. Physiological and performance adaptations to an in-season lower-limb resistance training program in elite futsal players. J Strength Cond Res. 2017;31(9):2592603. https://doi.org/10.1519/JSC.0000000000001886 . Ruas CV, Pinto RS, Haff GG, Lima CD, Pinto MD, Brown LE. Alternative Methods of Determining Hamstrings-to-Quadriceps Ratios: a Comprehensive Review. Sports Med Open. 2019;5(1):11. https://doi.org/10.1186/s40798-019-0185-0 . Schache AG, Wrigley TV, Baker R, Pandy MG. Biomechanical response to hamstring muscle strain injury. Gait Posture. 2008;29(2):332–8. https://doi.org/10.1016/j.gaitpost.2008.10.054 . Schmid L, Klotz T, Siebert T, Röhrle O. Characterization of Electromechanical Delay Based on a Biophysical Multi-Scale Skeletal Muscle Model. Front Physiol. 2019;10:1270. https://doi.org/10.3389/fphys.2019.01270 . Steiner M, Baur H, Blasimann A. Sex-specific differences in neuromuscular activation of the knee stabilizing muscles in adults-a systematic review. Arch Physiother. 2023;13(1):1. https://doi.org/10.1186/s40945-022-00158-x . Van Hooren B, Bosch F. Influence of muscle slack on high-intensity sport performance: A review. Strength Conditioning J. 2016;38(3):75–87. https://doi.org/10.1519/SSC.0000000000000251 . Wang S, Jiang X, Chen Z, Xing X, Zhang X, Che T. The effect of complex training and ballistic exercise on the time-course adaptations of lower extremity explosive strength in elite female field hockey players. Front Public Health. 2025;13. https://doi.org/10.3389/fpubh.2025.1676079 . Xu X, Hu G, Williams G, Ma F. Gender comparisons and associations between lower limb muscle activation strategies and resultant knee biomechanics during single leg drop landings. Biomechanics. 2022;2(4):56275. https://doi.org/10.3390/biomechanics2040044 . Zhang Q, Léam A, Fouré A, Wong DP, Hautier C. Relationship Between Explosive Strength Capacity of the Knee Muscles and Deceleration Performance in Female Professional Soccer Players. Front Physiol. 2021;12. https://doi.org/10.3389/fphys.2021.723041 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 10 Feb, 2026 Editor assigned by journal 08 Feb, 2026 Submission checks completed at journal 08 Feb, 2026 First submitted to journal 06 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8807183","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589717079,"identity":"0525343b-aa35-470e-ae0d-680d2cbdf100","order_by":0,"name":"Özlem Köklü","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFAC5gYGhgMMBnwMzAcgAgcIamGEaGFjYEuAaQEJEaWFx4A4Lfz8Bxsf3ThjY8wmkfPtwc82Bjm+GwnsjyvwaJFsONhsnHMjzYxNIne7YW8bg7HkjQTGxjN4tBgcbGyTzvlw2AaoZZsEbxtD4gaQFnwuMzjM2P4boiXnmeTfNoZ6wlqOMbYx59w4DHRYDps00JYEA0JaJHsYm6VzzqQZs/E8MzeWOSdhOPPMw8aZ+LTw8x8++DnnmI1hP3vys4dvymzk+Y4nH/iITwsyYANiCQYGgjGJpmUUjIJRMApGASYAAPukUFJV3NU2AAAAAElFTkSuQmCC","orcid":"","institution":"Pamukkale University","correspondingAuthor":true,"prefix":"","firstName":"Özlem","middleName":"","lastName":"Köklü","suffix":""}],"badges":[],"createdAt":"2026-02-06 12:39:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8807183/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8807183/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102761380,"identity":"c8bb5b0e-0ae0-419a-a758-979073eb864a","added_by":"auto","created_at":"2026-02-16 10:37:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130928,"visible":true,"origin":"","legend":"\u003cp\u003eThe \"Inverse\" Neuromuscular Dynamics\u003cstrong\u003e.\u003c/strong\u003e Divergent modulation of muscular strength balance and neuromuscular timing indices across angular velocities (n=21). \u003cstrong\u003e(A)\u003c/strong\u003e Muscular Strength Balance (H/Q Ratio) significantly increases from low to moderate velocity, stabilizing at high velocity. \u003cstrong\u003e(B)\u003c/strong\u003e Neuromuscular Control (Neuromuscular Latency) significantly decreases in a stepwise manner as velocity increases. Values are presented as Mean ± SD.\u003c/p\u003e\n\u003cp\u003e* indicates a statistically significant difference between the bracketed conditions (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8807183/v1/39de66495fb39a7b08af2228.png"},{"id":102761381,"identity":"8e828f9a-44a3-48b9-958b-d43de34e1bde","added_by":"auto","created_at":"2026-02-16 10:37:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134825,"visible":true,"origin":"","legend":"\u003cp\u003eThe \"Trade-off\" Correlation: Relationship between muscular strength balance (H/Q Torque Ratio) and neuromuscular control (Neuromuscular Latency) at 60°/s (n=21).\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8807183/v1/9763c3622e03358b81977890.png"},{"id":103049175,"identity":"c1134fb4-efd8-4ca7-95a9-d541163776b6","added_by":"auto","created_at":"2026-02-20 07:36:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1236210,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8807183/v1/59f7aa40-d373-46a7-8df2-b05bbac520b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wearable Sensor Assessment of Neuromuscular Latency: Revealing the Strength-Timing Trade-off in Female Soccer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe rapid growth of women's soccer has driven increased professionalization and imposed greater physical demands on players. However, a substantial gender disparity persists in injury epidemiology, with female players reporting a significantly higher incidence of severe lower limb injuries, particularly anterior cruciate ligament (ACL) ruptures, compared to male players [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite the widespread implementation of injury prevention programs, the rate of non-contact knee injuries remains high [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This persistence implies that conventional screening methods, which primarily emphasize anatomical characteristics or broad strength assessments, may miss subtle functional deficits that stray from the ideal homeostatic balance, thereby heightening players' risk of injury. Neuromuscular latency, which reflects the central nervous system's (CNS) capacity to coordinate muscle activation, is critical for maintaining dynamic joint stability and preventing loss of functional control [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDynamic knee stability hinges on the precise balance of the opposing forces generated by the agonist and antagonist muscle groups. Conventionally, this relationship is quantified utilizing the hamstring-quadriceps (H/Q) peak torque ratio [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the context of biological function, the knee joint operates as a system that strives for intra-limb equilibrium. An optimal H/Q torque ratio represents muscular strength balance a capacity wherein antagonist forces sufficiently counteract the agonist drive to preserve joint integrity. However, reliance on this static representation of the balance may be misleading. ACL injuries typically occur within 30\u0026ndash;50 ms of ground contact, whereas peak muscle tension requires 300\u0026ndash;500 ms to develop [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, the hamstring's capacity for rapid activation before the attainment of peak torque is likely more critical for joint protection than absolute strength alone [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCrucially, stability in biological systems is not merely static but dynamic. We propose that neuromuscular latency, the synchronization between the onset of agonist acceleration and antagonist braking, is paramount. Current screening methods fail because they measure force capacity but ignore force timing. A delayed antagonist response represents a critical neuromuscular deficit. This delay compromises dynamic joint stability, forcing the system into a reactive rather than predictive state, which significantly increases injury risk. The seamless coupling between agonist action and antagonist response signifies the temporal efficiency of the neuromuscular system [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. If a strong hamstring exhibits a delayed firing pattern, a state of functional imbalance exists, rendering the joint vulnerable despite a seemingly safe H/Q torque ratio [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While isokinetic dynamometry remains the gold standard for assessing mechanical muscle balance, its utility is limited by its stationary, laboratory-based nature. In contrast, the rapid advancement of wearable technology, particularly wireless electromyography (sEMG) sensors, provides an opportunity to assess neuromuscular function in ecological settings [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the effective deployment of wearables requires the identification of valid physiological metrics that transcend simple force output [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. If a dissociation exists between muscular strength balance (measured by dynamometers) and neuromuscular latency (measured by sensors), it will validate the specific need for wearable wireless sEMG as an essential, distinct component of injury risk screening, rather than a redundant tool.\u003c/p\u003e \u003cp\u003eDespite the importance of these factors, few studies have integrated strength and latency variables across the high angular velocities characteristic of elite sport. Most research evaluates these parameters in isolation or at low speeds (e.g., 60\u0026deg;/s), which fail to replicate the dynamic demands of competitive match play. It remains unclear whether female players who possess muscular strength balance (high H/Q torque ratio) also exhibit the necessary neural drive (low latency) to maintain stability at high velocities [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This study introduces the latency-strength mismatch to quantify this deficit. This mismatch may represent a latent risk factor with significant implications for injury prevention strategies in sports science. Therefore, the primary objective of this study was to investigate the relationship between isokinetic H/Q torque ratios (muscular strength balance) and agonist-antagonist neuromuscular latency (neuromuscular control) across varying angular velocities (60\u0026deg;/s, 180\u0026deg;/s, and 240\u0026deg;/s) in female football players. This study hypothesized that maintaining strength balance (high H/Q torque ratios) at high velocities would theoretically require a proportional increase in neuromuscular control efficiency (reduced neuromuscular latency); however, a divergence between these two parameters would indicate a functional deficit.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Approach to the Problem\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional laboratory design to assess neuromuscular function under velocity-specific conditions. Participants attended a single testing session, scheduled between 14:00 and 17:00 h to control for circadian variation and minimize fatigue-induced bias. A standardized testing order was implemented: (1) a standardized warm-up [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], followed by (2) concurrent isokinetic and electromyographic assessments. A reciprocal concentric-concentric protocol was employed to measure the torque ratios at soccer-relevant velocities.\u003c/p\u003e \u003cp\u003eBefore data collection, participants performed a familiarization protocol consisting of three submaximal repetitions at each velocity. Following familiarization, Isokinetic testing was conducted in an ascending velocity order (60, 180, 240\u0026deg;/s) to optimize performance and limit fatigue. To ensure adequate recovery and maintain high power output, a 15-second brief interval was provided between repetitions within each bout, while a 120-second passive rest period was mandated between sets of different velocities [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These specific velocities were selected to identify the velocity at which the mechanical capacity of the muscle might dissociate from its temporal activation capability [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe quadriceps repetition with the highest peak torque was chosen for data analysis at each velocity. The corresponding H/Q torque ratio and neuromuscular latency values from this specific repetition were extracted for statistical analysis to ensure temporal coupling between muscle strength and neuromuscular control.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eTwenty-one female soccer players from a competitive youth soccer academy, actively participating in an amateur league, volunteered to participate in this study. All participants had a minimum of three years of competitive experience. Exclusion criteria were established to ensure the integrity of the neuromuscular data: (1) any lower extremity musculoskeletal injury within the preceding six months, (2) history of knee reconstructive surgery, (3) any neurological disorder affecting motor control, or (4) those whose dominant leg is their preferred kicking leg. Although bilateral assessment is common, this study focused exclusively on the dominant limb (defined as the preferred kicking leg) to isolate the intra-limb asymmetry (agonist vs. antagonist) specifically associated with high-velocity ball-striking and landing mechanisms that are frequent in the dominant leg [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The descriptive characteristics of the participants were as follows: age, 17.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 years; Body Mass, 55.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01 kg; height, 165.30\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33 cm; and Sports Age, 3.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55 years. Before testing, all participants (and guardians of those under 18 years of age) provided written informed consent. The study was conducted in accordance with the Declaration of Helsinki and was approved by the University Ethics Committee (Protocol No: E-60116787-020-797301).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSurface electromyography (sEMG)\u003c/b\u003e: Surface electromyography was recorded simultaneously during isokinetic tasks using a wearable wireless sEMG system (FREEEMG 1000, BTS Bioengineering, Milan, Italy). This non-invasive, lightweight system allows for data acquisition without impeding the movement range of motion, simulating the sensor configuration used in field-based wearable applications. Before electrode placement, the skin over the vastus lateralis (VL) and semitendinosus (ST) muscles was shaved, abraded, and cleaned with 70% ethanol to reduce impedance below 5 kΩ. Bipolar Ag/AgCl electrodes (10 mm diameter, 20 mm inter-electrode distance) were positioned on the muscle bellies parallel to the fibers, strictly following the SENIAM guidelines (VL: 2/3 on the line from the anterior spina iliaca superior to the lateral side of the patella; ST: 50% on the line between the ischial tuberosity and the medial epicondyle of the tibia). A reference electrode was placed on the electrically neutral tibial tuberosity. All sensor placements were conducted by the same researcher. The VL and ST were selected as the primary agonist-antagonist pairs for sagittal-plane knee control. All signals were sampled at a frequency of 2000 Hz to ensure high temporal resolution.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIsokinetic Dynamometry\u003c/strong\u003e \u003cp\u003eKnee extensor and flexor strength were assessed using a calibrated Biodex System 4 Pro isokinetic dynamometer (Biodex Medical Systems, Shirley, NY, USA). Participants were seated with the hip flexed at 85\u0026deg; and secured via shoulder harnesses, a lap belt, and a thigh strap to isolate knee motion. The dynamometer axis was aligned with the lateral femoral epicondyle. Gravity correction was performed for each participant by weighing the limb at 30 degrees of knee flexion to account for limb mass. The knee flexion-extension range was standardized to 0\u0026deg;\u0026ndash;90\u0026deg; [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Torque data were normalized to body mass (Nm/kg), and the conventional concentric H/Q torque ratio was utilized as the primary index of muscular strength balance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSystem Synchronization\u003c/b\u003e To eliminate inter-device phase shifts, the isokinetic dynamometer and wearable wireless sEMG system were synchronized via an analog voltage signal from the Biodex System 4 Pro, which was integrated into an auxiliary channel of the BTS EMG unit. This hardware interface enabled the simultaneous acquisition of torque, joint angle, and sEMG data on a common time axis, ensuring precise temporal alignment for neuromuscular latency calculations.\u003c/p\u003e\n\u003ch3\u003eData Processing and Signal Analysis\u003c/h3\u003e\n\u003cp\u003eRaw sEMG signals from the VL and ST were processed using a customized computerized threshold algorithm to determine the precise timing of muscle activation. To ensure signal integrity, all raw data were sampled at 2000 Hz and band-pass filtered using a fourth-order Zero-lag Butterworth filter with a frequency range of 20\u0026ndash;500 Hz to eliminate motion artifacts and high-frequency noise.\u003c/p\u003e \u003cp\u003eNeuromuscular latency was defined as the temporal interval between the initiation of agonist acceleration and the subsequent onset of antagonist muscle activity. To identify the exact activation point, a sliding window root-mean-square (RMS) algorithm with a 20 ms window size was applied. The onset threshold was established at three standard deviations (3 SD) above the mean baseline activity measured during the initial quiet resting phase of the test.\u003c/p\u003e \u003cp\u003eTo ensure the robustness of the latency-strength mismatch analysis, only signals maintaining activity above this threshold for at least 25 consecutive milliseconds were considered for latency calculation, thereby preventing the inclusion of transient spikes or noise. Neuromuscular latency was calculated as the absolute time interval between the antagonist onset and the agonist onset:\u003c/p\u003e \u003cp\u003eNeuromuscular Latency (ms) = |Onset\u003csub\u003eAntagonist\u003c/sub\u003e - Onset\u003csub\u003eAgonist\u003c/sub\u003e|\u003c/p\u003e \u003cp\u003eA value of 0 ms indicates perfect synchronization, whereas positive values indicate a phase lag (neuromuscular asynchrony). Absolute values were utilized to account for pre-activation strategies where the antagonist might fire prior to the agonist. All data processing and algorithmic computations were performed using MATLAB R2023a, ensuring the high degree of temporal precision required to identify functional deficits within the critical 30\u0026ndash;50 ms injury window.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eA prior power analysis was conducted using G*Power (version 3.1.9.7, Heinrich-Heine-Universit\u0026auml;t D\u0026uuml;sseldorf, D\u0026uuml;sseldorf, Germany). For a repeated-measures Analysis of Variance (ANOVA) (within factors), with an alpha level of 0.05, a power of 0.80, and a large effect size (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{f}=0.40\\)\u003c/span\u003e\u003c/span\u003e) based on similar isokinetic studies, a minimum sample size of 18 participants was required. Thus, the current sample of 21 participants provided sufficient statistical power. The normality of the data distribution was verified using the Shapiro-Wilk test.\u003c/p\u003e \u003cp\u003eDescriptive statistics are presented as mean\u0026plusmn;standard deviation. Repeated measures ANOVA was used to compare the H/Q torque ratios and neuromuscular latencies across the three angular velocities. Bonferroni post-hoc tests were used to identify specific pairwise differences between the groups. Effect sizes for the ANOVA were reported as partial eta squared (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\eta\\:}}_{\\text{p}}^{2}\\)\u003c/span\u003e\u003c/span\u003e). Pearson correlation coefficients (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e) were calculated to determine the relationship between muscle strength balance and neuromuscular control at each velocity. The magnitude of the relationship was interpreted according to Cohen\u0026rsquo;s guidelines: (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{r}=0.10\\text{\\--}0.29\\)\u003c/span\u003e\u003c/span\u003e), medium (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{r}=0.30\\text{\\--}0.49\\)\u003c/span\u003e\u003c/span\u003e), and large (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{r}\\ge\\:0.50\\)\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. All statistical analyses were performed using SPSS Statistics (Version 26.0, IBM Corp., Armonk, NY, USA). The level of significance was set at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVelocity-Dependent Adaptations of Neuromuscular Latency\u003c/h2\u003e \u003cp\u003eRepeated measures ANOVA revealed a statistically significant main effect of angular velocity on neuromuscular latency. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.B, latency was significantly reduced as velocity increased, decreasing from 35\u0026thinsp;\u0026plusmn;\u0026thinsp;5ms at 60\u0026deg;/s to 23\u0026thinsp;\u0026plusmn;\u0026thinsp;3 ms at 240\u0026deg;/s. The longest latency period was observed at the lowest velocity (60\u0026deg;/s), whereas the shortest latency period was observed at the highest velocity (240\u0026deg;/s). Post-hoc pairwise comparisons confirmed that significant differences existed between all velocity conditions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). The descriptive statistics and confidence intervals are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in neuromuscular latency across angular velocities (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngular Velocity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Latency (ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI [Lower, Upper]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePairwise Differences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e60\u0026deg;/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.33, 0.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea, b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e180\u0026deg;/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.24, 0.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea, c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e240\u0026deg;/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.22, 0.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eb, c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eSD\u0026thinsp;=\u0026thinsp;Standard Deviation; CI\u0026thinsp;=\u0026thinsp;Confidence Interval. ANOVA results\u003c/em\u003e: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{F}\\:\\left(\\text{2,40}\\right)=26.408\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=0.002\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{p}^{2}=0.57\\)\u003c/span\u003e\u003c/span\u003e. \u003cem\u003eSuperscript letters indicate significant pairwise differences (\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e): a (60\u0026deg; vs. 180\u0026deg;), b (60\u0026deg; vs. 240\u0026deg;), c (180\u0026deg; vs. 240\u0026deg;).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e***Table around here***\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e***Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e around here***\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eVelocity-Dependent Adaptations of H/Q Ratio\u003c/h2\u003e \u003cp\u003eThe analysis of muscular strength balance indices demonstrated a significant main effect of angular velocity. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.A, H/Q torque ratios increased from 60\u0026deg;/s to 180\u0026deg;/s, then stabilized. The H/Q torque ratio was lowest at 60\u0026deg;/s and significantly increased at 180\u0026deg;/s. Although a slight decrease was observed in the 240s compared to 180\u0026deg;/s, the values remained significantly higher than those observed in the 60s, indicating a velocity-induced shift toward a more balanced agonist-antagonist relationship.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in muscular strength balance (H/Q Ratio) across angular velocities (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=21\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngular Velocity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean H/Q Torque Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI [Lower, Upper]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBalance Interpretation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePairwise Differences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e60\u0026deg;/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.45, 0.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuadriceps Dominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eb, c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e180\u0026deg;/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.51, 0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImproved Balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea, c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e240\u0026deg;/s\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e[0.49, 0.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate Balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea, b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSD\u0026thinsp;=\u0026thinsp;Standard Deviation; CI\u0026thinsp;=\u0026thinsp;Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSuperscript letters indicate significant pairwise differences (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e): a (60\u0026deg; vs. 180\u0026deg;), b (60\u0026deg; vs. 240\u0026deg;), c (180\u0026deg; vs. 240\u0026deg;). Balance interpretation is based on the proximity to the normative 0.60 threshold.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e***Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e around here***\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation between H/Q Torque Ratio and Neuromuscular Control\u003c/h2\u003e \u003cp\u003ePearson product-moment correlation analysis was conducted to examine the relationship between the H/Q torque ratio and neuromuscular latency. The complete correlation matrix is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Specifically, a positive correlation was found at 60\u0026deg;/s (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r=0.792,p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e) and 240\u0026deg;/s (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r=0.681,p\u0026lt;0.001\\)\u003c/span\u003e\u003c/span\u003e), while a moderate correlation was observed at 180\u0026deg;/s (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r=0.589,p=0.005\\)\u003c/span\u003e\u003c/span\u003e). These results indicate a potential trade-off, with participants exhibiting greater neuromuscular control when they had a greater muscle strength balance (higher H/Q torque ratios). This relationship is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between muscular strength balance (H/Q torque ratio) and neuromuscular control (Neuromuscular latency) (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=21\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026deg;/s Neuromuscular latency \u003cem\u003er\u003c/em\u003e(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180\u0026deg;/s Neuromuscular latency \u003cem\u003er\u003c/em\u003e(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240\u0026deg;/s Neuromuscular latency \u003cem\u003er\u003c/em\u003e(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026deg;/s H/Q Torque Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.792 (\u0026lt;\u0026thinsp;0.001) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615 (0.003) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388 (0.082)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e180\u0026deg;/s H/Q Torque Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.645 (0.002) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.589 (0.005) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.406 (0.068)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e240\u0026deg;/s H/Q Torque Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.512 (0.018) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.577 (0.006) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.681 (\u0026lt;\u0026thinsp;0.001) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: \u003csup\u003e*\u003c/sup\u003e indicates statistical significance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{p}\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). Bold values indicate correlations at matched velocities. P-values are calculated for N=21\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e***Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e around here***\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e***Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e around here***\u003c/h2\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe primary objective of this study was to investigate the relationship between muscle strength balance as quantified by the H/Q torque ratio, and neuromuscular control, as defined by agonist-antagonist neuromuscular latency across a range of angular velocities in female soccer players. The key findings indicate an inverse relationship between neuromuscular latency and movement speed, alongside a significant effect of angular velocity on the H/Q torque ratio, with higher H/Q ratios generally correlating with longer neuromuscular latencies. The primary finding of this study reveals a functional trade-off: players exhibiting superior muscular strength balance displayed greater neuromuscular latency. This suggests that static mechanical equilibrium does not necessarily guarantee dynamic temporal synchronization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVelocity-Dependent Adaptations of Neuromuscular Control\u003c/h2\u003e \u003cp\u003eOur results demonstrated a clear velocity-dependent adaptation of neuromuscular latency, with latency decreasing significantly from 35 ms at 60\u0026deg;/s to 23 ms at 240\u0026deg;/s. This observed inverse relationship aligns with the existing literature, where increased movement velocity often necessitates a more rapid muscular response to maintain dynamic stability, thereby reducing latency [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Both contribute to neuromuscular latency, which is a critical factor in dynamic joint stability. At high angular velocities (240\u0026deg;/s), the neuromuscular system undergoes a state transition. The reduction in latency may represent a compensatory mechanism to preserve neuromuscular control when the window for afferent feedback closes. The CNS shifts from a reactive (high-latency) to a predictive (low-latency) mode to minimize errors in joint stabilization. This transition likely represents a critical adaptation in the neural strategy, where the system abandons feedback-dependent control in favour of feedforward mechanisms to prevent mechanical failure during rapid, high-intensity movements characteristic of soccer [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRelationship Between Muscular Strength and Neuromuscular Timing\u003c/h2\u003e \u003cp\u003eA key finding of this study is the positive correlation between the H/Q torque ratio and neuromuscular latency. Specifically, players with higher strength balance tended to exhibit slower muscle activation, particularly at 60\u0026deg;/s. Specifically, participants with more favourable muscle strength ratios exhibited slower muscle reaction times. This inverse correlation (r\u0026thinsp;=\u0026thinsp;0.81 at 60\u0026deg;/s) suggests strategic divergence in homeostatic regulation. Players with high muscular strength balance (stronger hamstrings) may rely on inherent muscle-tendon stiffness to absorb energy, creating a safety margin that permits a relaxation of timing constraints (slower onset). Conversely, players with lower muscle strength must compensate by enforcing faster activation to prevent joint injuries [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This pattern suggests a compensatory strategy in which mechanical stiffness offsets the need for neural speed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological Mechanisms Underlying the Latency-Strength Mismatch\u003c/h2\u003e \u003cp\u003eThis dissociation highlights the specificity of training adaptations. The high H/Q torque ratios observed in our participants may be a result of hypertrophy-oriented resistance training. While such training effectively increases contractile cross-sectional area and peak torque [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], it does not necessarily enhance signal transmission speeds. Furthermore, hypertrophy can alter muscle architecture, specifically by increasing the pennation angle. Although this enhances force production capacity, it may inadvertently prolong the time required to take up muscle slack, the initial compliance of the muscle-tendon unit [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. While muscle slack is a likely mechanism, neural inhibition at high velocities may also contribute to the observed neuromuscular latency. This structural adaptation at the fiber level may directly influence the functional outcome, creating the observed latency-strength mismatch.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Neuromuscular Training and Injury Prevention\u003c/h2\u003e \u003cp\u003eThe observed latency-strength mismatch suggests that current training methodologies may benefit from refinement to simultaneously optimize both static torque capabilities and rapid neuromuscular latency, potentially mitigating injury risk more effectively [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Training paradigms should aim to recouple strength and timing. High-velocity ballistic training may be essential to reduce the activation latency in players with high hypertrophy, ensuring that their mechanical potential is accessible within the critical injury window [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Therefore, the stronger players in our cohort may possess high torque capabilities but lack the specific proactive neural adaptations (e.g., increased motor unit discharge rates or doublet firing) that characterize rapid neuromuscular responses. This supports the distinction made by Dideriksen et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] that maximal strength and rapid force production are separate motor qualities governed by different physiological mechanisms. Consequently, optimizing injury prevention strategies likely necessitates a training paradigm that transcends mere hypertrophy or strength enhancement to include targeted interventions, such as reactive plyometrics and velocity-specific drills, aimed at improving the speed and coordination of neuromuscular co-activation to enhance dynamic joint stability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study identified velocity-dependent changes of neuromuscular latency in female soccer players. The observed positive correlation suggests a latency-strength mismatch, where superior muscular strength does not guarantee rapid reflexive responses. Screening protocols should integrate high-velocity testing and wearable technology to detect these hidden neuromuscular deficits.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePractical Implications\u003c/h2\u003e \u003cp\u003eAccording to the velocity-dependent latency deficits revealed in this study, practitioners might consider a two-tiered approach to injury prevention:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVelocity-Specific Screening\u003c/strong\u003e \u003cp\u003eStandard clinical assessments rely on low-velocity strength measurements (60\u0026deg;/s). However, our data indicates that deficits in neuromuscular latency are most critical at high velocities (240\u0026deg;/s), where the feed-forward mechanism dominates. Therefore, screening protocols should ideally include high-velocity isokinetic testing.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReactive Agility Training\u003c/strong\u003e \u003cp\u003eSince neuromuscular latency accounts for a significant portion of injury risk, training must target its reduction. Coaches should require players to decelerate or cut in response to a visual stimulus within a temporal window of \u0026lt;\u0026thinsp;300 ms, forcing the CNS to streamline signal transmission and improve co-contraction under time pressure [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIntegration of Wearable Technology\u003c/strong\u003e \u003cp\u003eThe identification of the latency-strength mismatch highlights the necessity of integrating both mechanical and temporal assessments within injury risk screening and training program design. Wearable technologies, such as advanced sEMG systems and integrated inertial measurement units, offer promising avenues for continuous, ecologically valid monitoring and feedback in these contexts.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLimitations and Future Research\u003c/h2\u003e \u003cp\u003eThis study's cross-sectional design limits causal inferences about neuromuscular latency, training adaptations, and ACL injury risk. Fixed-velocity isokinetic tests also fail to capture the dynamics of high-speed eccentric actions vital for injury screening [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, while this study focused on the dominant (kicking) limb to assess intra-limb agonist-antagonist coupling, it is important to acknowledge that the non-dominant (stance) limb is frequently the site of non-contact ACL injury during planting and cutting maneuvers. Future research should investigate whether the observed latency-strength mismatch is bilateral or if inter-limb asymmetries in neuromuscular timing further exacerbate injury risk.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eACL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnterior Cruciate Ligament\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCNS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral Nervous System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eH/Q\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHamstring-to-Quadriceps (Ratio)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHz\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHertz\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ems\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMilliseconds\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRMS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003esEMG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurface Electromyography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eST\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSemitendinosus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVastus Lateralis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/h2\u003e \u003cp\u003eThe ethics committee of Pamukkale University approved the study protocol (decision no. 797301, dated 18/12/2025). The study was conducted in accordance with the principles outlined in the Declaration of Helsinki, ensuring respect for the rights and dignity of the participants. Prior to participating in the study, informed written consent was obtained from all participants and, when applicable, their parents.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e \u003cp\u003eNot applicable. The manuscript does not contain any person\u0026rsquo;s data in any form (including images, videos, or personal details).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCLINICAL TRIAL NUMBER\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026Ouml;.K. is the sole author of this research. The author conceptualized the study, performed data collection and analysis, and drafted the manuscript. The final version of the manuscript has been read and approved by the author.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eThe author would like to thank all the athletes who volunteered for this study. Special thanks are also due to the laboratory personnel for their technical support during data collection. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e \u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMancino F, Kayani B, Gabr A, Fontalis A, Plastow R, Haddad FS. 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Front Physiol. 2021;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fphys.2021.723041\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2021.723041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wearable Technology, Wireless Electromyography, Neuromechanical Coupling; ACL Injury Prevention, Female Soccer","lastPublishedDoi":"10.21203/rs.3.rs-8807183/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8807183/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe high incidence of anterior cruciate ligament (ACL) injuries in female soccer players persists despite widespread preventive interventions. Traditional screening relies heavily on isokinetic torque ratios to assess mechanical joint stability; however, this approach often fails to capture the temporal dynamics of sensorimotor control. Wearable wireless electromyography (sEMG) provides a viable modality to assess these neuromuscular latency deficits.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwenty-one female soccer players (age: 17.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 years) underwent reciprocal concentric isokinetic testing at 60\u0026deg;/s, 180\u0026deg;/s, and 240\u0026deg;/s. The hamstring-quadriceps torque ratio was measured via an isokinetic dynamometer and normalized to body weight. Simultaneously, neuromuscular latency was acquired using a wearable wireless sEMG system (BTS FreeEMG) on the vastus lateralis and semitendinosus. Neuromuscular latency was quantified using a computerized threshold algorithm to determine the agonist-antagonist asynchrony.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA significant main effect of angular velocity was observed on neuromuscular latency (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which decreased with increasing velocity, reflecting feed-forward adaptation. A positive correlation emerged between the mechanical H/Q torque ratio and neuromuscular latency, most notably at 60\u0026deg;/s (r\u0026thinsp;=\u0026thinsp;0.792) and 240\u0026deg;/s (r\u0026thinsp;=\u0026thinsp;0.681). This indicates a paradoxical latency-strength mismatch, in which players with superior mechanical torque ratios exhibit significantly delayed neuromuscular reflexive responses.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eStatic mechanical symmetry does not guarantee dynamic temporal efficiency. The identified latency-strength mismatch suggests that standard dynamometry may mask critical sensorimotor deficits. The integration of wearable wireless sEMG technology into injury risk screening is essential to capture these temporal asymmetries and ensure that mechanical capacity is matched by rapid neural drive.\u003c/p\u003e","manuscriptTitle":"Wearable Sensor Assessment of Neuromuscular Latency: Revealing the Strength-Timing Trade-off in Female Soccer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 10:37:55","doi":"10.21203/rs.3.rs-8807183/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-21T10:21:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T12:33:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T17:03:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63406189797596988903663815732822446468","date":"2026-04-14T14:00:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229275321467980310206457363281101473673","date":"2026-04-13T18:17:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25758570048426501291568739514283617798","date":"2026-04-10T06:20:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-11T01:14:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T00:53:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T00:52:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2026-02-06T11:57:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a0ebc6c-e772-4681-898d-b1a48d50bb2e","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T10:09:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 10:37:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8807183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8807183","identity":"rs-8807183","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}