Evaluation of the Foot Performance in “Single Leg Squat” Test of

Female Athletes using Smart Socks

Anna Januskevica

, Guna Semjonova

, Alexander Oks

, Alexei Katashev

and Peteris Eizentals

Department of Morphology, Faculty of Medicine, Riga Stradins University, Dzirciema 16, Riga, Latvia

Institute of Design and Technology, Riga Technical University, Kalku 1, Riga, Latvia

Institute of Biomedical Engineering and Nanotechnology, Riga Technical University, Kalku 1, Riga, Latvia

Keywords: Foot Performance, “Single Leg Squat” Test, Plantar Pressure Measurement, Smart Socks, Injury Prevention.

Abstract: Increased plantar pressure on the medial side of the plantar surface of the foot in female athletes is one of the

risk factors for lower extremity injuries. Functional tests single-leg squat tests (SLST), are one of the ways to

assess changes in foot plantar pressure. The main disadvantage of clinical functional tests is their subjectivity.

Moreover, as a rule, these tests are performed under laboratory conditions, which is expensive and time-

consuming. This paper demonstrates the evaluation of lower foot behaviour in several SLST variations by the

DAid Pressure Sock System (DPSS). The research was based on the cross-sectional study, where a group of

healthy female athletes was requested to perform SLST exercises under the supervision of a physiotherapist,

while simultaneously the feet plantar pressure was measured with the DPSS. Based on the observations of the

physiotherapist, the participants were sorted in the test group and control group, depending on their ability to

perform the exercises with or without increased inversion of the foot. Meanwhile, the application of the DPSS

provided an estimate of the lateral-medial deviations of the centre of plantar pressure (COP) for evaluation of

the feet functionality during the SLST. A clear correlation between the medial shift of the COP value, obtained

from the DPSS measurement, and the physiotherapist’s decision on the quality of the SLST was observed. It

was observed that the average COP value for the test group was shifted medially, while for the control group

the position of COP was shifted laterally. Therefore, the application of DPSS with SLST has a potential for

athlete functional testing, as well as for the development of feedback-based training aid in the training

environment to help coaches and athletes to monitor the accuracy of the foot position in various squat

exercises.

1 INTRODUCTION

One of the risk factors for lower extremity injuries in

female athletes is altered lower limb biomechanics,

characterized by the redundant increase of the

pressure on the medial side of the foot plantar surface

(Numata et al., 2018). Such alteration could cause

overuse injuries, such as tibial stress syndrome

(Razak et al., 2012; Neal et al., 2014; Buldt et al.,

2018), iliotibial syndrome, m. tibialis posterior

dysfunction, anterior cruciate ligament rupture

(Kagaya et al., 2015; Ugalde et al., 2015; Hughes et

al., 2019) and patellofemoral pain syndrome, and

plantar fasciitis (Razak et al., 2012; Neal et al., 2014;

2015; Buldt et al., 2018).

In clinical practice, to assess the risk of lower

limb injuries and to assess the potential risk factors

associated with future injuries, lower limb functional

tests are used. Among them is a variety of single leg

squat tests (SLST) used in periodic health

examinations in the clinical setting to identify

problems of the lower extremity biomechanics and

reveal incorrect movement patterns (Ugalde et al.,

2015; Khuu et al., 2016; Kagaya et al., 2015; Hughes

et al., 2019; Khuu et al., 2019).

The typical distorted movement patterns during

SLST include increased internal rotation and

adduction of the hip joint, internal rotation of the

lower leg, a medial deviation of the knee joint or knee

position of the knee joint, and increased foot

pronation, also known as dynamic valgus position

(Ugalde et al., 2015; Khuu et al., 2016; Wyndow et

al., 2016; Kagaya et al., 2017; Hughes et al., 2019).

The main drawback of the SLST is that the tests

are based on subjective visual assessment. This limits

observable parameters and makes test results depend

Januskevica, A., Semjonova, G., Oks, A., Katashev, A. and Eizentals, P.

Evaluation of the Foot Performance in “Single Leg Squat” Test of Female Athletes using Smart Socks.

DOI: 10.5220/0010146701610168

In Proceedings of the 8th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2020), pages 161-168

ISBN: 978-989-758-481-7

161

on the experience of the involved clinician. Also,

functional tests are generally performed under

laboratory/clinic conditions, which can be

comparatively time-consuming and expensive.

Development of reliable lower limb movement

tracking systems for sports, medicine, or

rehabilitation could enable the measurement of limb

movement, identification of altered movement

patterns, and analysis of long-term data, and,

therefore, could help to significantly reduce the risk

of lower limb injuries (Kianifar et al., 2017; Khuu et

al., 2019). One such system, DAid Pressure Sock

System (DPSS), is based on the application of entirely

textile smart socks with integrated knitted-in plantar

pressure sensors (Oks et al., 2016, Eizentals et al.,

2019). DPSS was demonstrated to be an effective tool

for gait analysis in a wide range of gait types and

velocities (walking, race walking, jogging, fast

running) (Oks et al., 2017). One of the main

advantages of the DPSS is the ability to be used as a

gait monitoring system both barefoot and with

different types of shoes.

The aim of the present research was an

assessment of the applicability of the DPSS for

objective evaluation of the feet functionality during

an SLST through quasi-static measurements of the

foot plantar pressure. A modified version of the center

of pressure (COP) calculation was applied for

quantification of the characteristic plantar pressure

during the SLST exercise. The obtained result

demonstrated that a clear difference in the

characteristic COP measurement can be observed if

the SLST exercise is performed with increased foot

pronation when compared to the result from the

control group.

2 MATERIALS AND METHODS

2.1 DAid Pressure Sock System

The DAid Pressure Sock System, used in the present

research, consists of pair of socks with 6 pressure

sensors, knitted into the sole part of each sock: two on

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

metatarsals (Fig.1a). Such positioning of sensors

enables monitoring of temporal gait characteristics as

well as detection of the supination/ pronation of lower

feet. Conductive pathways are designed to provide

the connection between sensors and the data

acquisition units. The sampling frequency of data

acquisition is up to 200 Hz. A more comprehensive

description of the system is presented in (Eizentals et

al., 2019, Oks et al., 2019).

Figure 1: Smart Sock. a-sole part, b-conductive pathways

with contact snaps, c - placement of sensors (Eizentals et

al., 2019).

2.2 Participants

The study involved volunteers – healthy female

athletes from the FS Metta football club. The

inclusion criteria were age (range 18-25 years) and

experience in sports (at least 10 years). The exclusion

criteria were health-related issues, including:

- Pain in any knee joint during movement

- Lower limb disease, deformity, injury, and/or

surgery in the last 12 months.

- Vestibular disorders

On the base of these criteria, 20 participants were

included in the study. The mean age of the

participants was 20.4 years (SD 2.1) years, the mean

body mass index (BMI) was 21.0 (SD 1.7). Informed

consent was obtained from all individual participants

included in the study. The study was conducted

following ethical standards comparable to the

Declaration of Helsinki and its later amendments. The

study protocol was approved by the Ethics Committee

of Riga Stradins University (6-2/11, 19.12.2019).

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Figure 2: Body position in SLST: a- single leg squat-front

test; b- single-leg squat-middle test; c- single-leg squat-

back test (Khuu et al., 2019).

2.3 Trial Design and “Single Leg

Squat” Functional Tests

Description

SLST was performed with smart socks and sports

shoes. To provide visual observation, surface markers

were placed symmetrically on both lower extremities

- spina iliaca anterior superior, the midpoint between

femur condylus medialis and condylus lateralis, the

midpoint between malleolus medialis and malleolus

lateralis (Ugalde et al., 2015; Khuu et al., 2016;

Hughes et al., 2019).

Three different variations of the “Single Leg

Squat” test were performed (see Fig. 2). The

participants were asked to place their hands on their

hips or along their sides and stand on one leg, while

the other leg (not the supporting one) was placed in

one of three positions. From this position, they were

instructed to squat the supporting leg to

approximately 60-degree flexion position in the knee

joint and return to the starting position by

straightening the knee joint.

Three variations of SLST were:

- the single leg squat-front test (further SLS-F) was

performed with the non-supporting leg stretched fully

forward in front position (Fig. 2a);

- the single leg squat-middle test (further SLS-M)

was performed with the non-supporting leg bent 90

degrees in the knee joint and aligned along the

supporting leg, in the middle position (Fig. 2b);

- the single leg squat-back test (further SLS-B) was

performed with the non-supporting leg bent 90

degrees in the knee joint and pulled behind the back

in a backward position (Fig. 2c).

In every test, at the beginning of the first squat, the

test (supporting) leg was lifted from the floor to

indicate the starting point of the squat, after which the

participant performed three squats in a row. In total,

three squats were performed for each leg for each

variation of SLST.

The squats performed by participants were

visually inspected by a certified physiotherapist, who

assessed participants' feet performance. Alongside,

the data from DPSS were recorded, however, the

results of recording were not communicated to the

physiotherapist. On the base of visual inspection, the

physiotherapist provided

reference conclusion

concerning test results, hereby separating participants

into two groups: control group (6 athletes), where

participants demonstrated correct SLST foot

performance, and study group (14 athletes), where

participants performed tests with excessive pronation

of the lower feet. In the control group, the mean age

was 20.6 years (SD 2.2 years), a mean BMI was 20.7

(SD 1.7), while in the study group, the mean age was

20.3 years (SD 2.2 years), and the mean BMI was

21.1 (SD 1.8).

Figure 3: The raw measurement from each sensor during

three separate SLST exercises, the sum of all signals, and

the manually selected time moments for analysis.

Evaluation of the Foot Performance in “Single Leg Squat” Test of Female Athletes using Smart Socks

163

Figure 4: COP coordinates with the center fixed to the

center of the foot (a, not to scale), and an example of the

calculated COP for 3 SLST measurements (b).

2.4 Data Processing

The evaluation of the change of the plantar pressure

of the load-bearing foot during the exercise was

performed by analyzing the variation of the center of

pressure (COP), which is a widely applied method in

gait analysis. Due to a variation of the textile sensor

location on the foot, a modified version of COP was

used for this application, where sensor positions are

defined relative to the center of the foot (P. Eizentals

et al., 2018). This technique of the COP calculation

assigns dimensionless arbitrary positions of the

separate sensors to avoid COP data dependence on

the foot size, consequently, the COP coordinates are

expressed in arbitrary units.

Before the calculation of the COP, the

measurement from DPSS was pre-processed

according to the following algorithm. First, the

measured resistance of the sensors is converted to the

conductance through equation (1):





=





(1)

where R

is the i-th measurement of the raw signal.

The calculated values are then filtered with a zero

phase-shift low-pass filter (Chebyshev type II, cut-off

frequency 10Hz). The signal from all sensors (Fig.3a)

then was then summed to obtain the total pressure

measurement during the exercise (Fig 3b), which was

employed for manual selection of the start and end

moment of each SLST exercise (Fig 3c).

For each of the selected periods, the COP was

calculated for the whole movement through the

equations (2) and (3):





∑





 cos 







(2)





∑





sin







(3)

where  is the measurement from each sensor

obtained from equation (1), =

[1, 1, cos  , cos  , 1, 1] is a vector holding weight

coefficients assigned to each sensor, and =

[

75°, 105°, 0°, 180°, 285°, 255°

]

is the angle

assigned to each sensor in the arbitrary coordinate

system. The sensors are numbered in the order

presented in Fig. 1c. The coordinate system and an

example of a COP for 3 consecutive squats are

presented in Fig 4. Figure 4b indicates that only the

COP component on X-axis (COPx) is important for

determining the over-pressure on the medial part of

the foot, which is important for this study, and

therefore only this component was used further in the

statistical analysis. For the selected coordinate

system, the positive values of COPx correspond to the

medial shift of COP, negative values of COPx – to the

lateral shift of COP.

2.5 Data Analysis

Statistical analysis of the calculated COPx values

included two parametric methods – two-factor

ANOVA with replication, and unpaired t-test. The

independent factors in ANOVA were the participant

(identified by the ID number) and the variation of

SLST. For each “participant – SLST variation”

combination three replicas, corresponded to separate

squats were used to estimate repeatability of the

DPSS measurement. ANOVA was performed to the

control and the study groups separately, and for right

and left leg tests, resulting in four separate ANOVA

tables. Unpaired t-test was applied to compare the

average COPx values of the control group and the

study group, separately to each variation of the SLST

test for the right and the left leg.

COPy

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3 RESULTS AND DISCUSSION

The results of COPx calculations for the control

group and study group are summarised in Table 1.

The table presents data for separate squats, performed

in a series of three different variations of the SLST

exercises, separately for left and right legs.

3.1 Validation of DPSS Applicability

for Evaluation of the SLST Foot

Performance

Data in Table 1 demonstrates that the COPx value

varies between separate squat measurements. To

compare these variations with the differences

between individual athletes as well as with variation

due to modification of the SLST technique, two-

parametric ANOVA with replications was used.

Table 2 summarizes P–values, associated with the

ANOVA test and indicating the contribution of the

corresponding source of variance to the overall

variability of COPx. The data from Table 2 indicates,

that contribution of the SLST variant to the total

variance of COPx was significant (P < 0.05) in half

cases, i.e. for the control group left leg test and for the

study group right leg test. This implies that

differences in COPx, caused by the change of the

SLST mode could be comparable to, or even greater

than the differences between COPx for separate

squats, caused by repeatability error of the DPSS

measurement. In turn, the contribution of the athlete

Table 1: Results of COPx calculations for control group and study group, the mean value for each of three exercises is given

in the table.

Participant

Left leg Right leg

Front test Middle test Back test Front test Middle test Back test

(SLS F Left) (SLS M Left) (SLS B Left) (SLS F Right) (SLS M Right) (SLS B Right)

Control group

1 -0.02 -0.02 -0.02 -0.06 -0.09 -0.02 -0.09 -0.06 -0.09 -0.11 -0.1 -0.08 -0.05 -0.06 -0.04 -0.04 -0.03 -0.04

2 -0.09 -0.04 -0.03 -0.04 -0.07 -0.04 -0.09 -0.06 -0.07 -0.04 -0.02 -0.01 -0.02 -0.03 -0.03 -0.06 -0.03 -0.04

3 -0.06 0.00 0.04 -0.07 -0.16 -0.07 -0.08 0.00 -0.04 -0.04 -0.06 -0.06 -0.02 -0.07 -0.01 -0.01 -0.09 -0.05

4 -0.06 -0.03 -0.04 -0.03 -0.02 0.00 0.00 -0.05 -0.06 -0.11 -0.04 -0.08 -0.06 -0.07 -0.1 -0.04 -0.05 -0.06

5 -0.06 0.00 0.04 -0.07 -0.16 -0.07 -0.08 0.00 -0.04 -0.09 -0.06 -0.09 -0.15 -0.11 -0.07 -0.02 -0.06 -0.05

6 -0.03 -0.04 -0.05 -0.02 -0.03 -0.01 -0.06 -0.08 -0.07 -0.04 -0.02 -0.01 -0.02 -0.03 -0.03 -0.06 -0.03 -0.04

Study group

1 0.02 0.09 0.09 0.03 0.19 0.09 0.09 -0.01 0.06 0.02 0.08 0.04 0.04 0.06 0.05 0.03 0.09 0.08

2 0.21 0.16 0.24 0.14 0.16 0.20 0.16 0.23 0.21 -0.02 0.05 0.04 -0.04 0.02 0.03 0.00 0.02 -0.01

3 0.11 0.11 0.07 0.05 0.00 0.01 -0.01 0.03 0.06 0.06 0.07 0.03 0.06 0.00 0.00 0.11 0.04 -0.04

4 0.04 0.03 -0.02 -0.01 0.05 0.01 0.01 0.00 0.05 0.02 0.04 -0.03 0.01 0.06 0.02 0.00 0.02 0.05

5 0.05 0.05 0.08 0.05 0.03 0.06 0.01 0.03 0.00 0.07 0.05 0.10 0.04 0.06 0.05 0.07 0.06 0.07

6 0.05 0.06 0.07 0.03 0.02 0.04 0.04 0.03 0.02 0.05 0.08 0.06 0.08 0.07 0.09 0.06 0.11 0.06

7 0.10 0.13 0.16 0.07 0.09 0.06 0.11 0.10 0.09 0.07 0.07 0.02 0.05 0.05 0.05 0.07 0.09 0.06

8 0.04 0.03 -0.02 0.11 0.13 0.16 0.06 0.15 0.17 0.03 0.04 0.03 0.03 0.19 0.09 0.03 0.09 0.08

9 0.21 0.16 0.24 0.14 0.16 0.20 0.16 0.23 0.21 0.07 0.05 0.10 0.14 0.16 0.20 0.07 0.06 0.07

10 0.05 0.05 0.08 0.05 0.03 0.06 0.01 0.03 0.00 -0.02 0.05 0.04 0.05 0.00 0.01 0.00 0.02 -0.01

11 0.10 0.13 0.16 0.11 0.13 0.16 0.06 0.15 0.17 0.07 0.07 0.02 0.05 0.03 0.06 0.07 0.09 0.06

12 0.11 0.11 0.07 0.05 0.00 0.01 -0.01 0.03 0.06 0.02 0.04 -0.03 0.03 0.02 0.04 0.00 0.02 0.05

13 0.02 0.09 0.09 0.02 0.08 0.04 0.14 0.16 0.20 0.02 0.08 0.04 0.07 0.09 0.06 0.03 0.09 0.08

14 0.03 0.02 0.04 0.06 0.07 0.03 0.11 0.13 0.16 0.06 0.07 0.03 0.11 0.13 0.16 0.11 0.04 -0.04

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165

Figure 5: Values of average COPx for (a) left and (b) right foot for different SLST exercises.

Table 2: Summary of ANOVA analysis: P- values associated with influence of the factors.

Factor

Control group Study group

Left leg Right leg Left leg Right leg

SLST variant 0.01 0.14 0.12 0.04

Athlete 0.62 6.4×10

-5

1.28×10

-23

2.7×10

-8

Interaction 0.01 0.006 7.2×10

-7

0.03

factor was significant in three cases out of four. This

means, that individual variations between athletes are

generally higher, then intra-athlete variation due to

DPSS repeatability error.

Additionally, it is worth noting that only a “non-

significant” case corresponds to the left leg squats in

the control group. This may be explained by the fact,

that athletes in the control group were right-handed

persons, having the leading left leg, therefore they

performed squats in a highly stable manner with small

variation in the position of the COP. The interaction

between SLST mode and athlete factors was

significant for all four ANOVA tests. This could be

interpreted, that variations in COPx, caused by

different combinations “SLST mode – athlete” are

generally greater, then variations, caused by DPSS

repeatability error. Summarising these arguments,

one could conclude, that accuracy of the DPSS

measurements is adequate to reveal individual

variations between athletes. In other words,

differences between athletes' COPx measurements

are higher than differences, caused by DPSS

repeatability error in single athlete’s measurements.

This conclusion validates DPSS applicability for

evaluation of the SLST foot performance. Alongside,

in the following analysis, one could use COPx,

averaged over three squats for characterization of

separate athletes.

3.2 Comparative Analysis of COPx

Coordinate Deviation during SLST

Figure 5 summarises data on average COPx

parameter for the control and study groups for

different SLST variations, presenting separately data

for the left and right leg. The figure demonstrates that

the average COPx in the control group is negative, i.e.

center of plantar pressure is placed closer to the lateral

side of the foot. This corresponds to the proper,

supinated position of the foot. In contrast, in the study

group, the average COPx of all athletes was positive,

it means that center of pressure is shifted medially,

under the foot arch. Indeed, athletes in the study

group were prone to excessive pronation. The

application of the t-test to compare data in the control

and study group for different SLST modes just

confirmed the conclusion made by visual analysis of

the Figure 5 data (Table 3). Generally, there was no

significant difference between left and right leg COPx

data both in control and study groups (Table 4). The

only case with P < 0.05 (front test in the study group)

may be a coincidence.

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Table 3: P–values for the t-test of differences in COPx

between control and study groups.

Left leg Right leg

Front test

5.0×10

-6

1.3×10

-4

Middle test

1.8×10

-5

2.8×10

-5

Back test

2.2×10

-6

2.4×10

-9

Additional observation made from Figure 5 data

is the difference in variability of COPx data for

control and study group: the variability in the study

group is noticeably higher. This observation

correlates well with the general opinion, that athletes

in the control group have proper foot functioning and

therefore perform SLST exercise in a more stable and

controllable manner, while athletes in the study group

have difficulties in sustaining proper balance due to

poor foot functioning.

To compare COPx data in SLST variations,

single-factor ANOVA was applied. For all

combinations of co-factors “control/study group” and

“left/right leg”, the P–values, associated with

ANOVA were in the range 0.13 to 0.82. This

demonstrates that there was no significant difference

in the COPx values, obtained for variations of SLST.

Moreover, in the estimation of the foot performance

using COPx values, the same criteria could be used

for all variations of SLST exercises.

Table 4: P–values for the t-test of differences in COPx

between left and right leg in control and study groups.

Control group Study group

Front-test 0.07 0.02

Middle-test 0.87 0.46

Back-test 0.20 0.07

3.3 Discussion

The obtained results demonstrated that the COPx

parameter, derived from the series of DPSS

measurements in SLST exercises is a good indicator

of the foot performance. The COPx values are

negative for athletes with proper foot performance

and positive for athletes, prone to excessive foot

pronation during the squat exercise. For the present

research group, there were no athletes in the control

group with positive average COPx, and there were no

athletes with negative average COPx in the study

group. Therefore, one could claim that the foot

performance test, based on the calculation of average

COPx over three sequential squats would have 100%

sensitivity and 100% specificity. In practice,

however, it would be useful to estimate the prognostic

value of the test, based on the single squat. This is

important for the development of training aid, that

provides real-time feedback to an athlete during each

squat. To estimate the characteristics of the single –

squat test, the distribution of the COPx values over

athletes could be approximated with a normal

distribution. Bearing in mind results of COPx

comparison between left and right foot (no

differences) and between variations of SLST (no

differences), average COPx and sample standard

deviation, calculated using all pool of data, could be

used as estimates of the mean and standard deviation

of this normal distribution. The corresponding

parameters for control group are μ = -0.050, σ = 0.027

and for study group are μ = 0.068, σ = 0.051. The

sensitivity of the single squat test, estimated as a

probability for an athlete with poor foot performance

to get the positive value of COPx in a single squat, is

equal to 0.906. The specificity, estimated as a

probability for an athlete with good foot performance

to get the negative value of COPx in a single squat, is

equal to 0.968. These parameters demonstrate the

good ability of the COPx measurement-based test to

distinguish between good and poor foot performance

in a single squat exercise. Hereby, the proposed

equipment and technique has the potential to be used

for feedback that helps athlete to train correct foot

position during the single-leg squat exercise.

4 CONCLUSIONS

1. The DAid Pressure Sock System can be

employed for quasi-static measurements to collect

objective results of functional tests of lower

exterminates biomechanics. Its application also

essentially simplifies data collection and registration.

2. The study demonstrated that the application of

the DAid Smart Sock system for COP monitoring

during Single leg squat functional tests provides the

possibility to diagnose athletes with an excessive

medial deviation of COP position, i.e. increased

potential risk of lower exterminates injury. Thus, the

application of the DAid Smart Sock system could

become a base of simple and inexpensive express

tests for lower exterminates injury risk prevention.

3. The method has the potential to be used for

feedback that helps athletes to train correct foot

positions during the single-leg squat exercise.

Evaluation of the Foot Performance in “Single Leg Squat” Test of Female Athletes using Smart Socks

167

ACKNOWLEDGEMENTS

This work has been supported by the European

Regional Development Fund within the Activity

1.1.1.2 “Post-doctoral Research Aid” of the Specific

Aid Objective 1.1.1 “To increase the research and

innovative capacity of scientific institutions of Latvia

and the ability to attract external financing, investing

in human resources and infrastructure” of the

Operational Programme “Growth and Employment”

(No. 1.1.1.2/VIAA/1/16/153).

REFERENCES

Buldt, A.K., Forghany, S., Landorf, K.B., Levinger, P.,

Murley, G.S., Menz, H.B. 2018. Foot posture is

associated with plantar pressure during gait: A

comparison of normal, planus and cavus feet. Gait

Posture. May;62:235-240

Cates, W., Cavanaugh, J. 2009. Advances in Rehabilitation

and Performance Testing. Clinics in Sports Medicine.

28(1), 63–76

Eizentals, P., Katashev, A., Oks, A. 2019. A Smart Socks

System for Running Gait Analysis. In: Proceedings of

the 7th International Conference on Sport Sciences

Research and Technology Support, Austria, Vienna, 20-

21 September 2019. Vienna: 2019,.47-54.

Eizentals, P., Katashev, A., Oks, A., Pavare, Z., Balcuna,

D. 2018 Detection of Excessive Pronation and

Supination for Walking and Running Gait with Smart

Socks. In World Congress on Medical Physics and

Biomedical Engineering 2018: IFMBE Proceedings,

Volume 68/2, Čehija, Prague, 3.-8. jūnijs, 2018.

Singapore: Springer Nature Singapore Pte Ltd, 2019,

.603-07.

Hughes, T., Jones, R.K., Starbuck, C., Picot, J., Sergeant,

J.C., Callaghan, M.J. 2019. Are tibial angles measured

with inertial sensors useful surrogates for frontal plane

projection angles measured using 2-dimensional video

analysis during single leg squat tasks? A reliability and

agreement study in elite football (soccer) players.

Journal of Electromyography and Kinesiology.

Feb;44:21-30.

Kagaya, Y., Fujii, Y., Nishizono, H. 2015. Association

between hip abductor function, rear-foot dynamic

alignment, and dynamic knee valgus during single-leg

squats and drop landings. Journal of Sport and Health

Science. Jun 4 (2), 182-187.

Khuu, A., Foch, E., Lewis, C.L. 2016. Not all single leg

squats are equal: a biomechanical comparison of three

variations. International Journal of Sports Physical

Therapy. Apr;11(2):201-11.

Khuu, A., Lewis, C.L. 2019. Position of the non-stance leg

during the single leg squat affects females and

males differently. Human Movement Science.

Oct;67:102506.

Kianifar, R., Joukov, V., Lee, A., Raina, S., Kulic, D. 2017.

Automated Assessment of Dynamic Knee Valgus and

Risk of Knee Injury During the Single Leg Squat. IEEE

J Transl Eng Health Med. 5: 2100213

Neal., B.S., Griffiths, I.B., Dowling, G.J., Murley, G.S.,

Munteanu, S.E., Franettovich Smith, M.M., Collins,

N.J., Barton, C.J. 2014. Foot posture as a risk factor for

lower limb overuse injury: a systematic review and

meta-analysis. Journal of Foot and Ankle Research.

Dec 19;7(1):55

Numata, H., Nakase, J., Kitaoka, K., Shima, Y., Oshima, T.,

Takata, Y., Shimozaki, K., Tsuchiya, H. 2018. Two-

dimensional motion analysis of dynamic knee valgus

identifies female high school athletes at risk of non-

contact anterior cruciate ligament injury. Knee Surgery,

Sports Traumatology, Arthroscopy. Feb;26(2):442-447.

Oks, A., Katashev, A., Bernans, E., Abolins, V. 2017. A

Comparison of the Accuracy of the Smart Sock System

to Force Platform and Optical System for Measurement

of Temporal Parameters of Locomotion. No: IOP

Conference Series: Materials Science and Engineering.

2017. United Kingdom, Bristol: IOP Publishing, 2017,

1.-6.lpp. ISSN 1757-8981. e-ISSN 1757-899X.

Oks, A., Katashev, A., Eizentals, P., Rozenstoka, S., Suna,

D. New Method to Analyze the Load Propagation on

the Plantar Foot Surface During a Walk/Run Using the

Smart Sock System. In: IFMBE Proceedings. Vol.76:

15th Mediterranean Conference on Medical and

Biological Engineering and Computing (MEDICON

2019), Portugal, Coimbra, 26-28 September 2019.

Cham: Springer, 2019,.604-09.

Razak, A.H.A., Zayegh, A., Rezaul K. Begg, R.K., Wahab,

Y. 2012. Foot Plantar Pressure Measurement System:

A Review. Sensors (Basel). 12(7): 9884–9912.

Ugalde, V., Brockman, C., Bailowitz, Z., Pollard, C.D.

2015. Single Leg Squat Test and Its Relationship to

Dynamic Knee Valgus and Injury Risk Screening. PM

R. Mar;7(3):229-35; quiz 235

Wyndow N., De Jong, A., Rial K, Tucker, K., Collins, N.,

Vicenzino, B., Russell, T., Crossley, K. 2016. The

relationship of foot and ankle mobility to the frontal

plane projection angle in asymptomatic adults. Journal

of Foot and Ankle Research. Jan 25;9:3.

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