Biomechanics of the Lower Extremity in Youth Football League:

FIFA 11+ One Leg Squat Analysis

Anna Davidovica

, Sergejs Davidovics

, Guna Semjonova

, Alexei Katashev

Alexander Oks

, Linda Lancere

, Signe Tomsone

and Maksims Zolovs

Department of Rehabilitation, Riga Stradins University, 16 Dzirciema Street, LV-1007 Riga, Latvia

Institute of Mechanical and Biomedical Engineering, Riga Technical University, LV-1048 Riga, Latvia

Institute of Architecture and Design, Riga Technical University, LV-1048 Riga, Latvia

Department of Sociotechnical Systems Modelling, Vidzeme University of Applied Sciences, LV-4201, Valmiera, Latvia

Statistical Unit, Faculty of Medicine, Riga Stradins University, 16 Dzirciema Street, LV-1007 Riga, Latvia

Keywords: Lower Extremity, Biomechanical Variables, Youth Football League Players, One Leg Squat,

FIFA 11+, Wireless Sensor Systems, Smart Socks.

Abstract: Background: Football carries substantial injury risks, especially for youth players. Providing biofeedback of

lower limb motion during functional tasks is a crucial part of injury prevention programs such as FIFA 11+.

While the FIFA 11+ warm-up program providing individualised feedback remains challenging, wireless

sensor systems such as the DAid® Pressure Sock system, NOTCH® Inertial Sensor System, and PLUX

Wireless Biosignals (muscleBAN kit) System offer potential solutions. Aim: This study aims to explore the

correlation of lower limb biomechanical variables during the FIFA 11+ Part 2 exercise "One Leg Squat" in

youth football players using wireless sensor systems and video recordings. Methods: Using wireless sensor

systems and video recordings, we analysed lower limb biomechanics during the "One Leg Squat" exercise in

youth football players. Results: Strong positive correlations were identified between hip joint adduction and

the changes in the centre of pressure of the plantar surface of the foot (COP1y), as well as between hip joint

internal rotation and COP1y. COP2x correlated strongly with gluteus medius activity. Conversely, COP2y

showed a negative correlation with gluteus maximus activity. Conclusions: The results support the potential

of wireless sensor systems in monitoring the biomechanical changes of the lower extremity movements and

lay the groundwork for future biofeedback methods based on the DAid® smart socks system technology for

evaluating lower limb motion, especially the changes in the centre of pressure of the plantar surface of the

foot, during functional tasks in Football Youth League players.

1 INTRODUCTION

Football, a globally renowned sport boasting

approximately four hundred million players across

208 countries, carries a significant injury risk for

participants of all ages, both professional and

amateur. Injuries not only lead to player withdrawals

https://orcid.org/0000-0002-3141-7998

https://orcid.org/0000-0002-6554-0716

https://orcid.org/0000-0001-8894-3748

https://orcid.org/0000-0001-6925-1842

https://orcid.org/0000-0003-0524-5106

https://orcid.org/0000-0002-7836-2672

https://orcid.org/0000-0001-9120-5869

but also impede team performance across various

levels (Sadigursky et al., 2017). Among youth aged

9-21, non-contact injuries account for 53% to 72% of

all injuries, with lower extremity injuries prevailing

in 72% to 93% of cases (Jones et al., 2019).

Today, the leading preventive exercise program

for correctness of the movement in football is the

Davidovica, A., Davidovics, S., Semjonova, G., Katashev, A., Oks, A., Lancere, L., Tomsone, S. and Zolovs, M.

Biomechanics of the Lower Extremity in Youth Football League: FIFA 11+ One Leg Squat Analysis.

DOI: 10.5220/0012894900003828

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2024), pages 131-139

ISBN: 978-989-758-719-1; ISSN: 2184-3201

131

FIFA 11+ warm-up program, the application of which

reduces the risk of injuries by 30% (Sadigursky et al.,

2017), where the FIFA 11+ program 2nd Part’s task

“One Leg Squat” is targeted entirely at the

development of the correct lower limb motion pattern

and balance (Bizzini et al., 2015). Simultaneous

assessment by coaches of the entire team during

movement execution does not provide an individual

approach to each athlete, but an individual approach

is essential for the athlete to progress to the more

advanced set of exercises within the FIFA 11+

program and more intensive training (Bizzini et al.,

2015). The individualized objective approach could

be realized using objective motion capture

biofeedback systems (Kim et al., 2021; Hribernik et

al., 2022; Di Paolo et al., 2023).

Most golden standard objective biofeedback

methods for motion capturing, such as optical

systems, Microsoft Kinect camera systems (Bawa et

al., 2021), 3D kinematic analysis with optical motion

capture (OMCs) (Longo et al., 2022) and force plates

(Chen et al., 2021) have drawbacks that make them

challenging to implement in football players' daily

motion evaluation routines. Optical motion capture

systems, for example, are unsuitable for field

applications, requiring a stationary setup with a

limited spatial field of view (Suo et al., 2024). Force

plates used for measuring ground reaction forces and

foot plantar pressure are applicable only in laboratory

environments (Ahn et al., 2024), rendering them

impractical for daily football practice.

An alternative approach for providing

biofeedback of lower limb motion and biomechanical

parameters to athletes could be based on the use of

wireless smart sensor systems. For example, Inertial

Measurement Unit (IMU) systems measure a body's

specific force, angular rate, magnetic field, and

acceleration of a body in real-time (Khan et al., 2024).

Muscle activity capturing wireless sensors providing

real-time data on muscle performance and condition

based on electromyographic (EMG) signals (Tanaka

et al., 2022). For lower extremity distal segment

observation the smart insole systems (Khandakar et

al., 2022), such as Pedar (Brindle et al., 2022),

PODOSmart

(Ziagkas et al., 2021) and smart socks

such as the DAid® Pressure Sock systems could be

used, enabling the measurement of biomechanical

parameters of the foot, including the centre of

pressure (CoP) and pressure on various parts of the

foot plantar surface (Brindle et al., 2022; Oks et al.,

2020; Januskevica et al., 2020).

However, sensor systems present challenges,

particularly when multiple wireless devices are used

simultaneously. Synchronising data flow can be

complex and time-consuming, with potential signal

overlap leading to inaccuracies. Managing device

activation timing adds further complexity (Masalskyi

et al., 2024). The numerous sensors required for

comprehensive monitoring can restrict an athlete's

movement and impact biomechanics of lower

extremity. For instance, smart insoles, though useful

for gait analysis, are often rigid, causing discomfort

and limiting natural movement (Masalskyi et al.,

2024). So far DAid® smart socks, which incorporate

piezoresistive knitted textile pressure sensors

embedded in the sole, have demonstrated several

advantages to use them: enhanced comfort,

unobtrusiveness, accurate pressure measurement,

have no impact on foot biomechanics, and ease of use,

making them a promising tool for monitoring and

improving athletic performance (Oks et al., 2020;

Januskevica et al., 2020; Semjonova et al., 2022).

There is a correlation between lower limb

biomechanical variables during functional tasks

(Claiborne et al., 2006). For instance, increased hip

abductor (musculus gluteus medius) strength is

associated with reduced knee valgus angle during

functional exercises (Neamatallah et al., 2020). For

foot biomechanical variables there is a positive

correlation between greater foot pronation and

increased front to medial plantar pressure during

functional squat activities (Ahn et al., 2024).

Therefore, the aim of this study is to investigate

the correlation of biomechanical variables of the

lower extremity measured by separate wireless sensor

systems during the exercise “One Leg Squat” in a

population of youth league football players. We

hypothesised that by using three separate sensor

systems (DAid®, NOTCH® and PLUX®) during the

FIFA11+ Part 2 exercise “One Leg Squat”, strong

correlations will be observed among main

biomechanical variables: joint angles, muscular

activation and foot plantar pressure, supporting the

possibility of using smart socks as the sole

biofeedback system for evaluating lower limb

motion, during functional tasks.

2 MATERIALS AND METHODS

2.1 Study Design and Participant

Recruitment

A cross-sectional correlation study involved 32 male

and female soccer players from the Latvian Youth

Soccer League (U-14 and U-15).

Participants met the following inclusion criteria:

at least 5 years of experience in the sport, no pain or

icSPORTS 2024 - 12th International Conference on Sport Sciences Research and Technology Support

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previous knee injuries or surgeries, no current knee

pain, no lower extremity injuries or surgeries in the

past six months, no lower extremity deformities, and

no vestibular dysfunction.

They performed the FIFA11+ Part 2 “One Leg

Squat” exercise using the DAid®, NOTCH® and

PLUX®, along with video analysis.

Informed consent was obtained, and the study was

approved by the Research Ethics Committee of Riga

Stradiņš University (approval received on 21 March

2023, No. 2-PĒK-4/294/2023).

Participants wore tight T-shirts or undershirts and

shorts that did not cover the knees and performed the

measurements in sports shoes with proper insoles.

Video recordings were made simultaneously with

the sensor data while participants performed the

FIFA11+ “One Leg Squat”. Three independent sports

physical therapists evaluated the correctness of the

movements, comparing their assessments with the

sensor data and verifying with video records.

2.2 Daid® Pressure Sock System

The DAid® Pressure Sock System consists of a pair

of socks, each with six pressure sensors in the sole:

two under the heel, two under the arch, and two under

the metatarsals, labelled as: (1) front medial, (2) front

lateral, (3) middle medial, (4) middle lateral, (5) heel

medial, and (6) heel lateral (Figure 1). This setup

monitors gait and detects supination/pronation.

Conductive pathways link the sensors to a data

acquisition unit that transmits data via Bluetooth to a

smartphone at up to 200 Hz per channel. For more

details see (Oks et al., 2019; Januskevica et al., 2020;

Semjonova et al., 2022).

Figure 1: DAid® smart socks sensor placement.

2.2.1 Calculating Centre of Pressure Using

Daid® Pressure Sock System Data

The data recorded from DAid® Pressure Sock

System provide monitoring of relative pressure

values under each sensor and calculation of

coordinates of centre of pressure (COP) for each foot

along mediolateral (COPx) and anteroposterior

(COPy) axes, as well as summarised COP position

(COPw).

Positive COPx values indicate a medial shift,

while negative values denote a lateral shift. Positive

COPy values signify an anterior shift, while negative

values indicate a posterior shift. COPw values

provide a general indication of COP location. The

COPx component on the X-axis (COPx) is crucial in

assessing the overpressure on the medial plantar

surface of the foot.

In this study, two methodologies COP1 and COP2

were used to calculate COP, where COP1x, COP1y,

COP1w and COP2x, COP2y, COP2w were obtained,

using DAid® Pressure Sock System data.

The socks generate a dataset consisting of the

resistances of sensors R1 to R6, measured in

kiloohms (kOhms) and recorded as ADC0 to ADC5

in the consolidated files. As applied pressure

increases, the sensor resistances decrease.

Therefore, two values were introduced for the

calculation of COP:

Ui = 100 – Ri (1)

Vi = 100 / Ri (2)

Values U and V increase when plantar pressure

increases.

The position of sensors over the foot were defined

by six vectors (see Figure 2):

= 0.26, y

= 0.97

= -0.26, y

= 0.97

= 0.26, y

= 0

= -0.26, y

= 0

= 0.26, y

= -0.97

= -0.26, y

= -0.97

where coordinates are defined in arbitrary units,

accounting the length of the foot is equal to 2 arbitrary

units. Therefore, the coordinates of COP are also

measured in arbitrary units.

The COP1 (COP

1, COP

1) are

calculated using values U

= 100 – R

as follows:

𝐶𝑂𝑃





∑

𝑈



𝑥



∑

𝑈



𝐶𝑂𝑃





∑

𝑈



𝑦



∑

𝑈



𝐶𝑂𝑃





∑

𝑈



The COP2 (COP

2, COP

2) are calculated

using values V

= 100 / R

as follows:

𝐶𝑂𝑃





∑

𝑉



𝑥



∑

𝑉



Biomechanics of the Lower Extremity in Youth Football League: FIFA 11+ One Leg Squat Analysis

133

𝐶𝑂𝑃





∑

𝑉



𝑦



∑

𝑉



𝐶𝑂𝑃





∑

𝑉



These two types of processing were used to determine

which method better distinguishes differences in COP

positions.

Figure 2: Nominal position of sensors over the foot plantar

surface.

2.3 Notch® Inertial Sensor System

The NOTCH ® IMUs system (Wearnotch by Notch

Interfaces, Inc., NJ, USA) used in this study features

wireless IMUs with nine-axis inertial sensors (three-

axis gyroscope, accelerometer, and magnetometer).

The Notch Pioneer app, installed on an iPhone 12

(iOS Version 1.7.1.1), processes data and sends it to

LabVIEW software, which computes average angles

and angular acceleration, then transfers the results to

an Excel sheet. Data recording occurs at 40 Hz.

During "One Leg Squat" recordings, the "Lower body

+ hip" configuration was selected in the app.

2.4 PLUX Wireless Biosignals

(MuscleBAN Kit) System

The wireless electromyography PLUX Wireless

Biosignals (muscleBAN kit) system was prepared to

monitor four muscles. The Plux application on a

computer captured muscle activity readings via

Bluetooth. Motor points were located using SENIAM

guidelines (Hermens et al., 2000). After electrode

placement, participants performed a maximum

voluntary contraction (MVC) to assess activities of

the musculus quadriceps femoris vastus lateralis,

musculus gluteus maximus, musculus gluteus medius

and musculus biceps femoris. In this study, the

normalisation of electromyographic (EMG) signals

(Halaki et al., 2012) from a specified muscle utilized

the EMG recorded from the same muscle during a

maximal voluntary isometric contraction (MVC) as

the reference value. Subsequently, the EMG signals

underwent processing by calculating the root mean

square from the rectified signal, the window for root

mean square calculation was 0.2 sec.

2.5 Task Description for Participants

Each participant was informed about the procedures

of the study (Bizzini et al., 2015). Before executing

the “One Leg Squat” exercise. Participants were

instructed to execute the "One Leg Squat", by

wearing a wireless DAid®, NOTCH®, PLUX® and

sports shoes (see Figure 3). Each participant executed

“One Leg Squat” 10 times 2 repetitions on both sides.

Figure 3: Study participants interact with the DAid®,

PLUX® and the NOTCH®, performing “One Leg Squat”.

2.6 Systems Synchronisation

The following steps were taken to synchronise the

three systems before starting “One Leg Squat'' task.

Each "One Leg Squat" was recorded by video camera.

The used data acquisition systems provided data

record at the sample rate 40 Hz (NOTCH® system)

and 140 Hz (socks data acquisition system). During

the measurement, all systems reported data together

with internal device time. The data from each system

were recorded in separated files. The all signal

processing was performed after recording, but not in

real time. The systems were not synchronised;

therefore, synchronisation was made at

postprocessing. The participants were asked to make

specific movement – bending the supporting leg knee,

the accelerometric and sock signal were aligned,

using the pattern of this movement as a marker. The

inaccuracy of time alignment does not exceed 25

milliseconds. The potential delay for output signals

was compensated by alignment procedure.

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2.7 Expert Protocol

Each FIFA11+ "One Leg Squat" was analysed by

three certified physiotherapists with over five years of

experience working with athletes. They assessed the

performance by watching video recordings at 0.25x

slow motion. The experts determined whether an

adduction and internal rotation of the thigh, knee

abduction, lower leg external rotation, ankle eversion,

and excessive foot pronation was noted in 6 out of 10

squats, the full set was classified as incorrect. In total

of 32 videos from 32 participants were analysed.

2.8 Statistical Methods of Research

Leveraging LabView software, data synchronisation

was achieved from three systems. Each of the 32

participants yielded 64 data files, encompassing

separate sets from all three systems for each lower

extremity during the FIFA11+ "One Leg Squat"

exercise. A total of 2,048 data files were collected and

consolidated into a single Database.txt file for

correlation analysis. Quantitative analysis was

conducted using Microsoft Excel v.16.77.1 and

"jamovi" v.2.3.28.0, an open-source graphical

interface for R programming. Descriptive statistics

such as mean and standard deviation were employed

to characterize participant data and results.

Spearman's correlation analysis, a non-parametric

method, was applied to explore relationships between

biomechanical variables derived from the three

systems. Statistical significance was considered at p

< .05.

3 RESULTS AND DISCUSSION

3.1 Participant Description

Thirty-two participants, comprising 16 women and 16

men, who were athletes and youth league football

players, took part in this correlational investigation.

Participants meeting specific inclusion and exclusion

criteria were selected for the study (see Table 1).

3.2 Prevalence of Dynamic Knee

Valgus in “One Leg Squat”

From the analysis of all the observed videos, in which

participants each performed the exercise "One Leg

Squat" 10 times for 2 repetitions with each leg, all

experts noted that a dynamic knee valgus position

characterized by adduction and internal rotation of the

Table 1: Characteristics of the study participants.

Mean Median Standard

deviation

(SD)

Minimum Maximum

BMI

20.8 20.8 2.065

17.3

27.2

Age

(years)

14.6 15.0 0.495 14 15

Weight

(kg)

59.8

58.0

6.413

50.0

78.6

Height

(cm)

169.4

169.0

4.662

159

181

EU size

40.6 40.0 1.961 37.0 44.0

thigh, knee abduction, lower leg external rotation,

ankle eversion, and excessive foot pronation was

observed in 68.75% of cases. In 31.25% of cases, a

dynamic knee valgus position was not observed (see

Figure 4).

Figure 4: Physiotherapists' assessment of the watched

videos for the left and right lower extremity.

3.3 Correlation Analysis of Lower

Limb Biomechanics

The study analysed various correlations involving the

right and left lower limbs, with significant findings

indicated by correlation coefficients (r) and

corresponding p-values. Correlations were

categorized as weak (r ≤ 0.2), moderate (0.2 < r ≤ 0.5),

or strong (0.5 < r ≤ 1) (refer to Appendix (Table 2 and

Table 3).

3.3.1 Left Lower Extremity

Strong Positive Correlation

A statistically significant strong positive correlation

was found between the changes in the centre of

pressure of the plantar surface of the foot COP2x,

which represents the position of the centre of pressure

in the medial part of the plantar surface of the foot,

Biomechanics of the Lower Extremity in Youth Football League: FIFA 11+ One Leg Squat Analysis

135

and the electrical activity of the musculus gluteus

medius (r=0.543; p < .001).

Moderate Positive Correlation

A statistically significant moderate positive

correlation was found between hip adduction and hip

joint internal rotation (r=0.408; p < .001); between

changes in the centre of pressure of the plantar surface

of the foot COP1x, which represents the position of

the centre of pressure in the medial part of the plantar

surface of the foot, and the electrical activity of the

musculus quadriceps femoris vastus lateralis

(r=0.401; p < .001) (see Figure 5).

Figure 5: Moderate positive correlation between COP1x

and the electrical activity of the musculus quadriceps

femoris vastus lateralis (left leg).

3.3.2 Right Lower Extremity

Strong Positive Correlation

A strong positive correlation was found between hip

internal rotation and hip adduction (r=0.591; p <

.001); between internal rotation of the hip joint and

changes in the centre of pressure of the plantar surface

of the foot, COP1y (r=0.599; p < .001) (see Figure 6).

Figure 6: Strong positive correlation between internal

rotation of the hip joint and COP1y (right leg).

Strong Negative Correlation

A strong negative correlation was found between

changes in the centre of pressure of the plantar surface

of the foot COP2y, and the electrical activity of the

musculus gluteus maximus (r=-0.603; p < .001) (see

Figure 7).

Figure 7: Strong negative correlation between COP2y and

the electrical activity of the musculus gluteus maximus

(right leg).

3.4 Discussion

The study's results demonstrate that the COPx, COPy,

and COPw parameters derived from the DAid®

during "One Leg Squat" exercise are reliable

indicators of the changes in the centre of pressure of

the plantar surface of the foot. Additionally, joint

angles measured by the NOTCH® and muscle

activity recorded by the PLUX® serve as reliable

indicators of lower limb performance during the task.

These findings underscore the potential of using one

smart sensor systems to provide an individualised

approach during functional tasks.

The statistically significant strong correlations

between hip joint adduction, hip joint internal rotation

angles, gluteus muscular activation, and COPx, COPy,

and COPw values highlight the interconnectedness of

lower limb dynamics measured by sensor systems. The

consistency of these findings with those of Kim et al.

(2021), which demonstrated a relationship between

increased dynamic valgus position of the knee joint and

increased foot pronation, hip joint adduction, and

internal rotation, further validates the use of these

parameters in assessing lower limb performance. The

use of advanced measurement techniques such as two-

dimensional video analysis in previous studies

complements our use of smart sensor systems, offering

robust evidence for these biomechanical relationships.

However, the practical application of multiple

smart sensor systems in real-time biofeedback

monitoring presents challenges. The setup of these

systems is often time-consuming and restricts the

freedom of movement necessary for executing

activities (Masalskyi et al., 2024). To address these

limitations, the development of smart clothing with

integrated motion-monitoring functionality combined

with mixed reality (MR) approaches offers a promising

solution. By using MR head-mounted displays

(HMDs), athletes can receive visual and auditory

icSPORTS 2024 - 12th International Conference on Sport Sciences Research and Technology Support

136

feedback in real-time, enhancing the utility of

biofeedback during training sessions.

Moreover, the integration of smart sensor system

information into Virtual Reality (VR) environments

can create immersive and interactive 3D simulations.

These VR systems can simulate real-world scenarios,

providing athletes with biofeedback in a controlled

environment conducive to movement and skill

development (Hamad et al., 2022). By offering a

dynamic and engaging training platform, VR systems

can potentially revolutionise how athletes train and

develop their skills.

Despite these promising findings, this study has

several limitations. First, the sample size was

relatively small, which may affect the generalizability

of the results.

Additionally, the study was conducted in a

controlled environment, which may not fully replicate

the complexities and variabilities of real-world

athletic settings. Future research should aim to

address these limitations by including larger, more

diverse populations and by conducting studies in

more varied and realistic environments. Investigating

the long-term effects of using MR and VR systems

for biofeedback on athletic performance and injury

prevention would also be beneficial.

4 CONCLUSIONS

Study findings support using the DAid® smart sock

system as the sole biofeedback system for evaluating

lower limb motion during functional tasks and

highlight the potential of wireless sensors in

monitoring the biomechanical changes of the lower

extremity movements for Football Youth League

players.

ACKNOWLEDGEMENTS

This research is funded by the Latvian Council of

Science, project Smart textile solutions as biofeedback

method for injury prevention for Latvian football youth

league players, project No. lzp-2023/1-0027.

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APPENDIX

Table 2: Correlation of left lower extremity.

Correlation

type

Parameter 2

Parameter Coefficient Significancy

(p)

Strong positive COP1x COP2x r= 0,506

< .001

COP2x Musculus gluteus medius electrical activity r= 0,543 p < .001

COP1w COP2w r= 0,836 p < .001

Moderate

positive

Hip joint flexion Musculus biceps femoris electrical activity r=0,408 p < .001

Hip joint adduction Hip joint internal rotation r=0,408 p < .001

usculus

luteus maximus electrical activity

usculus gluteus medius electrical activity r= 0,462

< .001

COP2x Musculus gluteus maximus electrical

activity

r= 0,418 p < .001

Knee joint flexion Musculus quadriceps femoris vastus

lateralis electrical activity

r= 0,469 p < .001

COP1x COP1y r= 0,479

< .001

COP1x Musculus quadriceps femoris vastus

lateralis electrical activity

r= 0,401 p < .001

COP1y COP2y r= 0,456

< .001

Strong negative COP1w Musculus gluteus maximus electrical

activity

r= -0,517 p < .001

icSPORTS 2024 - 12th International Conference on Sport Sciences Research and Technology Support

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Table 2: Correlation of left lower extremity. (cont.)

Moderate

negative

Knee joint flexion Hip joint flexion r= -0,45 p < .001

Musculus quadriceps femoris vastus lateralis

electrical activity

Hip joint flexion r= -0,469 p < .001

COP2w Musculus gluteus maximus electrical

activity

r= -0,42 p < .001

Musculus quadriceps femoris vastus lateralis

electrical activity

Musculus biceps femoris electrical activity r= -0,427 p < .001

Table 3: Correlation of right lower extremity.

Correlation

type

Parameter 2

Parameter

Coefficient Significancy

(p)

Strong

positive

Hip joint flexion Musculus biceps femoris electrical activity r= 0,585 p < .001

Hip joint adduction Hip joint internal rotation r= 0,591 p < .001

Hip joint adduction COP1y r= 0,836 p < .001

Hip joint internal rotation COP1y r= 0,599

< .001

Knee joint flexion Musculus quadriceps femoris vastus

lateralis electrical activity

r= 0,654 p < .001

COP1w COP2w r= 0,783

< .001

Strong

negative

Knee joint flexion Hip joint flexion r= -0,639

< .001

Musculus quadriceps femoris vastus lateralis

electrical activity

Hip joint flexion r= -0,599 p < .001

COP2w Hip joint adduction r= -0,588

< .001

Hip joint internal rotation COP1w r= -0,662

< .001

COP1w

usculus gluteus maximus electrical activity r= -0,658

< .001

COP2y

usculus gluteus maximus electrical activity r= -0,603

< .001

COP2w

usculus gluteus maximus electrical activity r= -0,637

< .001

Biomechanics of the Lower Extremity in Youth Football League: FIFA 11+ One Leg Squat Analysis

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