To obtain the aim of the study, the correlation research design was used, where associations between variables can be positive (variables change in the same direction), negative (variables change in opposite directions), or null (no relationship). The sample size was estimated based on Fraenkel and Wallen [40], who recommend a minimum of 30 participants for correlational studies to ensure reliable results. Given the target population and study design, a total of 32 participants was considered sufficient for meaningful statistical analysis.

The study involved 32 youth football players (both male and female) from the Latvian Youth Football League (U-14 and U-15), recruited between September 2023 and December 2023. Participants were required to be between 14 and 15 years old, actively playing in a youth football league, and have at least 5 years of experience in football. They had to be free of pain in the hip, knee, or ankle joints during movement and voluntarily agree to participate in the study. Participants were excluded if they had any lower limb disease, deformity, injury, or surgery within the past 12 months. Those with vestibular disorders that prevented them from performing the “Single Leg Squat” test and its variations were also excluded. Additional exclusion criteria included the presence of metallic implants in areas where sensors were applied and the use of knee or ankle braces or kinesiology tape. Both limbs were evaluated in this study; however, differences between the dominant and nondominant legs were not specifically analyzed. Limb dominance was determined based on the leg preferred for kicking a ball, a commonly used method in sports biomechanics research [41]. All participants completed the “Single Leg Squat” functional test, wearing tight-fitting sportswear that allowed accurate measurements and sports shoes with appropriate insoles.

The study was conducted in compliance with the Declaration of Helsinki and Latvian laws, adhered to the European Union’s General Data Protection Regulation 2016/679. Before participation, the study’s procedures were explained to participants and their guardians, and informed consent was obtained. Ethical approval was granted by the Riga Stradins University Research Ethics Committee (approval number 2-PĒK-4/294/2023). Participation was voluntary, with the right to withdraw at any time, and participant confidentiality was maintained throughout the study. The participants did not receive any compensation for their participation in the study.

Data collection involved three main instruments: the DAid Pressure Sock System (Riga, Latvia), the NOTCH Inertial Sensor System (Notch Interfaces Inc, New York, United States), and the PLUX Wireless Biosignals system (PLUX Wireless Biosignals, Lisbon, Portugal).

This system, equipped with 6 sensors in the sole, measured plantar pressure at up to 200 Hz per channel. The sensors were positioned under the heel, arch, and metatarsal heads to monitor gait and assess foot pressure distribution. Data were transmitted via Bluetooth to a remote device for storage. The system was calibrated using participants’ weight shifts. Activated before testing, the socks were connected via Bluetooth to “Fastreader” software in the LabView environment. The DAid records relative pressure values under each sensor to calculate the COP coordinates along the mediolateral (COPx) and anteroposterior (COPy) axes, as well as the overall COP position (COPw). More details about this system have been described in previous studies [29,30].

The NOTCH inertial measurement unit system, comprising 9-axis inertial sensors, was used to measure lower limb angles during movement. Using 6 sensors placed on the lower body, data were wirelessly transmitted to a mobile device and processed to map a 3D human skeleton. The system recorded at 40 Hz for 10-second intervals during the “Single Leg Squat” test. Sensors were calibrated and placed according to the user manual. The 6 inertial sensors were positioned at specific anatomical landmarks for accurate measurement: one on the sternum, one on the waist, and one on each thigh and calf (placed laterally at the midthigh and midcalf regions). Sensors were color-coded and placed according to the app’s guidelines. The “Lower body + hip” configuration was used, and participants followed specific positioning instructions to ensure accuracy.

The PLUX Wireless Biosignals system with an electromyographic add-on was used to collect muscle electrical activity data. The system includes an electromyographic sensor, triaxial accelerometer, and magnetometer, capturing data at up to 1000 Hz with 16-bit resolution. The device, powered by an internal battery and featuring dual Bluetooth modules, provided real-time muscle activity and movement data for analysis. Four electrodes were placed to measure muscle activity in this study. Electrode placement was identified according to Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM) guidelines [42]. Electrodes were positioned on the vastus lateralis (VL), gluteus maximus (GMx), gluteus medius (GM), and biceps femoris (BF). After placing the electrodes, participants performed a maximum voluntary contraction (MVC) to evaluate muscle activity, achieved by performing maximal contractions against a stable resistance [42]. Muscle activity was recorded throughout the execution of each “Single Leg Squat” repetition, ensuring continuous data collection during movement. The recording duration per trial was 10 seconds, covering the entire squat cycle. Electromyographic signals were normalized [43], using electromyographic data recorded during MVC as the reference. The signals were processed by calculating the root mean square from the rectified signal with a 0.2-second window. The highest root mean square value from all test repetitions was used to normalize the electromyographic signals, assessing muscle activity relative to its maximum neural activation. During the “Single Leg Squat” test and its variations, the PLUX system continuously measured the electrical activity of the VL, GM, GMx, and BF muscles, providing a dynamic assessment of neuromuscular activation patterns.

Participants’ demographic and physical characteristics, such as age, height, weight, shoe size, playing position, and dominant leg, were recorded. Each participant performed the “Single Leg Squat” (SLS) and its 3 variations, completing 3 repetitions of each type (Textbox 1):

During the execution of the “Single Leg Squat” and its variations, participants ensured continuous foot contact with the ground. Valid trials were considered those in which participants maintained balance throughout all 3 repetitions. If an incorrect execution occurred, participants were asked to repeat the attempt. A 3-minute rest period was observed between trials to prevent muscle fatigue.

Throughout the execution of all “Single Leg Squat” tests and their variations, participants wore the DAid smart sock system, the PLUX Wireless Biosignals (muscleBAN kit) for electromyography, and the NOTCH inertial sensor system, as shown in Figure 1.

Each youth league football player provided data to assess biomechanical movement variables during the “Single Leg Squat” functional test and its variations.

Angles from the NOTCH inertial sensor system: The dataset for the “Lower body+ hip” configuration included dynamic knee valgus determining angles: (1) Angles_Thigh: Flexion and extension, abduction and adduction, and internal and external rotation angles of the hip joint. (2) Angles_LowerLeg: Flexion and extension angles of the knee joint.

COP from the DAid smart sock system: This dataset included COP1X, COP1Y, COP1W, COP2X, COP2Y, and COP2W values, which represent the center of pressure on the plantar surface of the foot. Positive COPX values indicate the medial position, negative values indicate the lateral position, positive COPY values indicate the anterior position, and negative values indicate the posterior position. COPW values represent the total center of pressure. Two mathematical methods (COP1 and COP2) were used to calculate the center of pressure.

Electromyography data from the wireless PLUX system: Electrical activity of VL, GM, GMx, and BF and MVC values.

To synchronize datasets from the NOTCH, DAid, and PLUX for biomechanical movement analysis of the lower extremities, data consolidation was performed using the LabView environment. Each participant provided 64 data files, resulting in 2048 files from 32 participants, which were compiled into a unified Database.txt file for correlation analysis. Data analysis was conducted with Microsoft Excel v.16.77.1 and jamovi v.2.3.28.0 (an open-source graphical user interface for the R programming language) using descriptive statistics to describe participant characteristics and results.

Spearman’s correlation analysis, a nonparametric method, was used to determine correlations, considering the Spearman correlation coefficient (ρ). Positive correlations indicate that as one variable increases, so does the other, while negative correlations mean that as one increases, the other decreases. The strength of these relationships is measured by the (ρ up to 0.2 indicates a weak correlation, 0.2 to 0.5 indicates a moderate correlation, and 0.5 to 1 indicates a strong correlation. These correlation strength levels are based on Cohen (1988) [44,45]. Statistical significance was set at P<.05, with analyses conducted using jamovi v.2.3.28.0.

A total of 32 participants, including 16 female and 16 male individuals, all of whom were youth league football players, were involved in this study. Participants were youth football league athletes who met specific inclusion and exclusion criteria (Table 1).

The positions of the youth league football players (study participants) on the field varied—12 players were defenders, 9 were midfielders, and 11 were forwards.

The key findings of this study reveal strong positive and negative correlations between lower limb biomechanics during the “Single Leg Squat” in youth football players, using wireless sensor systems. These results provide important biomechanical insights that may help in identifying movement patterns associated with increased injury risk. Understanding the interaction between foot pressure distribution, joint angles, and muscle activity can guide targeted interventions to improve stability and prevent common football-related injuries, such as ACL injuries and patellofemoral pain syndrome [46,47]. A study by Ahn et al [21] demonstrated that altered foot biomechanics, particularly excessive pronation, may lead to compensatory changes in hip mechanics, such as increased internal hip rotation and hip adduction. This suggests that foot pronation may lead to altered load distribution and neuromuscular adaptations at the hip joint. As a result, foot mechanics play a crucial role in shaping lower limb kinematics and should be carefully considered when designing training programs for young athletes [21,48]. Similarly, Kim et al [10] found that foot pronation is associated with dynamic knee valgus, which involves a combination of hip adduction and internal rotation, contributing to medial knee displacement and increased risk of lower limb injuries. The findings of this study align with those of Kim et al [10], demonstrating that a medial shift in the COP was associated with hip adduction and internal rotation, supporting the link between altered foot mechanics and knee valgus in dynamic movements. Our study further supports these associations, reinforcing the idea that foot pressure analysis can be a valuable screening tool for injury risk assessment. Findings from the DAid smart sock system reinforced the link between medial plantar pressure distribution and hip adduction, which aligns with studies by Sanchis et al [49] and Tran et al [50]. Similarly, hip internal rotation was associated with increased muscle activity in the gluteus maximus and biceps femoris, supporting the role of proximal muscle engagement in dynamic lower limb stability [51]. These findings emphasize the importance of strengthening the hip and hamstring muscles in football players, as improved proximal control may help mitigate excessive knee valgus and reduce injury risk [52]. Further examination of muscle activity patterns reveals the role of neuromuscular control in maintaining lower limb stability. For instance, gluteus maximus and biceps femoris activation correlated with hip internal rotation, which is consistent with previous findings by Wilczynski et al [51] suggesting that proximal muscle control influences knee joint alignment during functional tasks [53]. Increased dynamic knee valgus was associated with higher gluteus maximus activity, reinforcing earlier studies on the importance of hip muscle engagement in knee biomechanics and valgus control [54,55]. This aligns with findings from a systematic review, which highlighted the association between gluteus maximus activation and hip and knee control during single-leg tasks [56], as well as research indicating that higher gluteal activation levels are linked to improved functional performance in tasks requiring lower limb stability and power, such as the 6-meter timed hop test [57]. From a practical perspective, these results indicate that strength and neuromuscular training focusing on the gluteus maximus and hamstrings could be beneficial for reducing valgus-related injuries in youth athletes [58]. While the current study focused on lower limb kinematics and pressure distribution, future research should further explore the dynamic interactions between foot posture, knee mechanics, and proximal muscle function in movement stability. Incorporating real-time feedback using wireless sensor systems during training may enhance movement efficiency and reduce injury risk, an area that warrants further investigation [59,60].

This study highlights key limitations that should be addressed in future research. One important aspect is the distinction between muscle activation and muscle force generation. While surface electromyography provides valuable insights into muscle activity, it does not directly quantify neural drive or muscle force [61]. The relationship between these variables is influenced by multiple factors, including electrode positioning, motor unit recruitment, and tissue conductivity, making it difficult to infer strength solely from electromyographic amplitude. Future studies should further investigate neuromuscular control strategies and their implications for lower limb biomechanics, ensuring that clinical applications account for these complexities and uncertainties [61,62]. In addition, this study focused on youth football players aged 14‐15 years old, a group at higher risk of neuromuscular adaptations and injury [4]. While these findings provide valuable insights into adolescent biomechanics, broader age groups and different sports, such as basketball, should be included in future research to determine the generalizability of the results. Future research could include individuals with lower limb pathologies, such as patellofemoral pain syndrome or postanterior cruciate ligament reconstruction, to provide data on biomechanical parameters and their relationships during functional tasks, aiming to identify risk factors and improve rehabilitation strategies to reduce the risk of progressive lower limb injuries. The research design and methodology effectively captured key biomechanical associations; however, additional experimental conditions—such as fatigue testing or perturbation trials—could provide deeper insights into neuromuscular responses under dynamic conditions.

The use of wireless sensor systems in this study allowed for detailed in-field analysis of lower limb biomechanics, but alternative technologies could further refine measurement accuracy and expand analytical capabilities. For instance, the PLUX Wireless Biosignals (muscleBAN kit) was used to assess muscle activation, but the Ultium Wireless Surface Electromyographic system could offer even greater resolution, with its high sampling rates, low noise levels, and SmartLead options, making it a strong alternative for future research [63]. The ability to capture real-time movement data outside laboratory settings provides a significant advantage for applied sports science [19]. These results demonstrate that wearable sensors can provide meaningful biomechanical insights without the constraints of traditional motion capture systems, making them more feasible for training environments [64]. Similarly, the NOTCH inertial sensor system effectively captured lower limb kinematics, but alternative motion capture systems such as OptiTrack could provide more precise 3D movement analysis [65,66]. However, considering the practical challenges of optical motion capture in sports settings [65,66], the use of wearable inertial sensors presents a valuable alternative for continuous monitoring and individualized movement assessment [67].

Among male participants, hip adduction and hip internal rotation showed a strong correlation, which suggests that males may be more reliant on hip-dominant movement strategies for single-leg stability [56]. This could have implications for training programs, as focusing on hip abductor and external rotator strength may help correct excessive hip adduction patterns that could lead to knee valgus [51,68]. For female participants, the most significant correlation was between knee joint flexion and the overall COP (COP2W). This suggests that female athletes may demonstrate different weight distribution strategies during squatting movements, potentially increasing their vulnerability to ACL injuries [57]. Given that previous research has highlighted greater valgus angles in female athletes, our findings suggest that targeted neuromuscular training, including balance and landing mechanics, may help mitigate these risks [69]. In addition, studies have demonstrated that eccentric hip abductor and lateral rotator strength are significantly associated with lower limb kinematics, particularly in women, reinforcing the importance of proximal control in mitigating excessive knee valgus and femur adduction [69]. Future research should further investigate these gender-based variations in lower limb biomechanics to better understand potential injury risk factors and inform targeted training interventions for male and female athletes [70]. Coaches and physiotherapists could use this information to create individualized training plans to optimize performance while minimizing injury risk.

This study identified significant correlations between lower limb biomechanics during the “Single Leg Squat” functional test in youth football players. Strong associations were observed between foot COP, lower limb movement patterns, and muscle activity, demonstrating the feasibility of using wireless sensor systems for biomechanical assessment in field conditions. The findings confirm that wireless sensor technology, including the DAid smart sock system, provides reliable data for evaluating lower limb biomechanics in football players.