A Smart Socks System for Running Gait Analysis

Peteris Eizentals

, Alexei Katashev

and Alexander Oks

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

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

Keywords: Gait Analysis, Smart Socks, Smart Textile, Textile Pressure Sensors.

Abstract: Running gait analysis is an often used tool for running performance improvement and injury prevention due

to an incorrect running style. The typical gait analysis methods are unavailable to amateur runners outside of

special clinics due to their relatively high cost. Smart socks are a relatively cheap gait analysis method that

can be used by amateur runners and professional athletes for running performance improvement. This paper

presents a smart socks system for feet plantar pressure measurement during running, as well as methods for

characterisation of the acquired plantar pressure measurement for running gait analysis. The validation of the

smart socks with a Pedar insole system is described, and the measurement analysis methods are demonstrated

by practical running tests. The validation tests demonstrated good temporal and pressure sensing

characteristics of the system, while the simplicity of the developed gait analysis methods was demonstrated

in the practical tests.

1 INTRODUCTION

Running is one of the most popular sport and

recreational activities worldwide. Besides its

beneficial effects on the health, it is also the cause of

numerous injuries, and up to half of the runners report

an injury annually (Fields et al., 2010). The most

frequent running related injuries are medial tibial

stress syndrome (incidence 13.6% – 20.0%,

prevalence 9.5%), Achilles tendinopathy (incidence

9.1% – 10.9%, prevalence 6.2% – 9.5%), plantar

fasciitis (incidence 4.5% – 10.0%, prevalence 5.2% –

17.5%), Patellar tendinopathy (incidence 5.5% –

22.7%, prevalence 12.5%), and ankle sprain

(incidence 10.9% – 15.0%, prevalence 9.5%) (Lopes

et al., 2012). Many of these injuries have high

recurrence rates (Bramah et al., 2018) and therefore

affect both daily life and training of the injured

person. Running related injuries are especially

frequent among amateur runners (De Araujo et al.,

2015), who often lack the understanding of a correct

running style. Although the connection between the

running style and the rate of injuries is still debatable

(Barton et al., 2016; Hamill & Gruber, 2017), running

gait analysis for amateur runners could be beneficial

for early detection of potentially harmful running

style or gait pathologies (Vincent et al., 2014), as it is

generally accepted that one of the main contributors

to running related injuries is abnormal running

kinematics (Barton et al., 2016). On top of that,

running gait analysis is a valuable tool for

performance improvement for professional runners

and amateurs alike. Unfortunately, there are no

simple and cheap tools for gait analysis that would be

affordable for non-professionals. The typical gait

analysis methods for feet plantar pressure analysis are

pressure sensing mats and insoles and gait analysis by

MEMS or 3D mapping (Taborri et al., 2016). All of

these methods are rather expensive and unavailable

for amateur runners outside of special clinics.

This paper describes running gait analysis by

custom-designed smart socks system, DAid®

Pressure Sock System (DPSS), and specially for this

system designed gait analysis methods. The smart

socks system was developed for solving some of the

inherent limitations of the conventional gait analysis

methods, as the socks are relatively cheap to produce,

if compared to insoles or pressure mats, they don’t

interfere with the performed activity, and can be used

with any type of shoes indoors and outdoors (Taborri

et al., 2016). The feasibility of walking gait analysis

by the DPSS has been demonstrated previously

(Eizentals, Katashev & Oks, 2018a), but the

performance of the system has not yet been verified

with a certified commercial gait analysis system, and

no tests had been done with running gait.

The system validation with the Pedar insole

system as a reference demonstrated that the smart

Eizentals, P., Katashev, A. and Oks, A.

A Smart Socks System for Running Gait Analysis.

DOI: 10.5220/0008070000470054

In Proceedings of the 7th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2019), pages 47-54

ISBN: 978-989-758-383-4

socks system has very good temporal characteristics,

as the average differences for the calculated step and

stride times between measurement by pedar and by

DPSS were 1.75% and 1.34% respectively. Practical

application of two dedicated running gait analysis

methods for smart socks is demonstrated in this paper.

2 SYSTEM DESCRIPTION

The developed smart socks system described in this

paper contains 6 pressure sensors on each sole, two

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

metatarsals (see Fig. 1 and Fig. 2). Such sensor

distribution enables monitoring of temporal walking

and running gait characteristics, and comparing the

features of plantar loading for normal

(asymptomatic), flat (pes planus) foot as well as

diagnosing supination and pronation conditions.

Conductive pathways are designed to provide the

connection between sensors and the data acquisition

units. The data acquisition units are attached to the

cuff of the sock by Velcro type tape and connected to

the sock by snap fasteners.

Figure 1: DAid Pressure Sock System, (a) sensors on the

insole, and (b) connectors for the data acquisition unit.

The main advantage of this technology is the

possibility to produce the DPSS socks using ordinary

sock knitting machines, thus greatly decreasing the

production cost. Moreover, the number and

placement of the sensors can be easily modified

according to the demands of a customer or

applications.

The present version of the data acquisition unit

collects the measurement simultaneously from all 6

pressure sensors and transmits them via Bluetooth to

a remote data processing device, where the

measurement is synchronized and saved to a file. The

sampling frequency of data acquisition is up to 200Hz

per channel, which is greater than 100Hz sampling

speed which is the recommended in the literature for

monitoring the change of feet plantar pressure during

running (Mann et al., 2016). The sampling frequency

can be adjusted to lower energy consumption, if

prolonged monitoring is required, providing with

more than 8h of continuous measurement.

Additionally, the device allows changing the

sensitivity range of the sensors and resetting the

timer. The sensors are numbered according to Fig. 2:

(1) front medial, (2) front lateral, (3) middle medial,

(4) middle lateral, (5) heel medial, and (6) heel lateral.

Figure 2: Sensor distribution and numbering on the Smart

Socks system.

3 SYSTEM VALIDATION

To assess the performance of the DPSS, verification

of the measurement was performed by the Pedar®

system insoles (Novel GmbH, Munich), which is

considered the golden standard for feet plantar

pressure measurement for gait analysis (Tamura &

Chen, 2018). Each Pedar insole contains 99 pressure

sensors, and the measurement is sampled at 50Hz or

100Hz, and transmitted to the computer via Bluetooth

connection. To analyze the performance of the Smart

Socks system, 6 participants were requested to walk

several times approx. 10m long distance in a normal

gait, while the feet plantar pressure measurement was

obtained simultaneously with both systems. A heel

strike was performed before each walk for providing

a sharp peak for signal synchronization. The Pedar

measurement was obtained at the rate of 100Hz,

which is the maximal sampling rate of the system,

while the smart socks system sampling rate was

approx. 200Hz.

For validation of the temporal accuracy of the

DPSS, step and stride times were calculated and

compared to those obtained by the Pedar system. The

stride time is the time between two successive foot to

ground contacts of the same foot, while the step time

is the time from the first foot-to-ground contact of one

icSPORTS 2019 - 7th International Conference on Sport Sciences Research and Technology Support

Figure 3: Test participant with the Pedar and the DPSS

attached.

foot to that of the other foot. The foot contact

detection threshold for Pedar was set to 50kPa of the

total pressure, where the total pressure was obtained

by summing all sensor measurement. For the DPSS,

the contact threshold was selected as 20% of the total

pressure, and it was adjusted adaptively with a

moving window algorithm. The adaptive adjustment

was performed to account for possible sensor

sensitivity change caused by feet sweating.

All calculated mean step and stride times are

given in Table 1. As it can be seen, the temporal

values obtained by the smart socks system are

remarkably close to the ones obtained from the Pedar

system, with the average difference of the mean

values being 9.8ms (1.75% of the mean step time) and

14.9ms (1.34% of the mean stride time) for step and

stride time respectively.

Direct comparison of pressure values between

both systems is not possible, as calibration of textile

pressure sensors is rather complicated due to the

hysteresis and nonlinearity of the textile sensors, and

is not performed for this application. As a result, the

measured electrical resistance of the sensors was not

converted to pressure units. Additionally, the size of

the textile sensors is considerably larger than that of

the Pedar insole sensors. To allow comparison of

pressure change over time for both systems, the

measurement was processed as follows. First, the

Table 1: Comparison of step and stride times obtained by

the Pedar and DPSS.

Nr.

DPSS Peda

Ste

Stride Ste

Stride

Mean

STD

Mean

STD

Mean

STD

Mean

STD

1 0.599 0.052 1.197 0.059 0.604 0.028 1.2 0.04

2 0.668 0.033 1.33 0.044 0.665 0.0275 1.327 0.043

3 0.541 0.075 1.07 0.109 0.569 0.075 1.105 0.021

4 0.525 0.048 1.042 0.033 0.523 0.2023 1.057 0.049

5 0.527 0.035 1.044 0.042 0.522 0.0211 1.038 0.026

6 0.527 0.111 1.054 0.106 0.538 0.0303 1.073 0.035

average pressure was calculated for Pedar system for

six zones in the approximate position of each textile

sensor (see Fig. 4, the chosen sensors on the Pedar

insole are marked). These calculated values, as well

as the values from the smart socks system, were

normalized by using a sliding window normalization

according to the equation:













min







max







min







(1)

  0.5

(2)

    0.5

(3)

where u

is the corresponding measurement and w is

the normalization window size, which was selected to

be 250 for Pedar ad 500 for DPSS. The width of the

window was selected to include 3-5 steps at a normal

walking speed.

Figure 4: The sensors in the marked area were selected for

plantar pressure measurement comparison with that of the

Smart Socks System

An example of the measurement from both

systems after the normalization is provided in Fig 5.

As can be seen, both measurements show a good

similarity of the activation time and relative pressure

change over the time. The middle sensors are not

shown in the figure as they typically have low

A Smart Socks System for Running Gait Analysis

pressure values, and the values after normalization do

not represent the real measurement.

Figure 5: Example of normalized measurement for both

systems, the sensor numbers for the DPSS sensors are

according to Fig. 2 while the Pedar areas for each textile

sensor are given in the Fig. 4.

Figure 6: An example of a measurement comparison of

several consecutive steps for Pedar and smart socks

systems. A linear relationship can be observed between

measurement of both systems.

An example of the raw measurement value

comparison for both systems is provided in Fig. 6. A

linear relationship between the measurement

obtained by DPSS and that of the Pedar system can

be observed for all sensors except those under the

arch of the foot, which are not activated due to the

relatively low pressure in the area under the arch of

the foot. This signifies that a calibration of the smart

socks system could be performed by obtaining the

calibration coefficients from these measurements.

The results obtained from simultaneous

measurements with both the Pedar and the Smart

Socks system confirmed that the developed system

has good temporal accuracy. The step and stride times

calculated from the measurement of the smart socks

system were reasonably close to those obtained from

the measurement of the Pedar system. Moreover, the

measured pressure change over time was in a good

agreement between both systems, as shown in Fig. 5.

It can be concluded from these results that the smart

socks system is a reliable tool for gait temporal

parameter measurement, and the system calibration

for absolute measurements is possible.

4 RUNNING GAIT ANALYSIS

Two experiments were performed for testing the

feasibility of the developed smart socks system and

the dedicated methods for running gait analysis. For

the first experiment, two amateur runners performed

a test run on a treadmill, starting with a 2min warm-

up at 5kmh, proceeded with 8-10min run at 10kmh,

and finishing with 1min cooldown at 5kmh. For the

second experiment, one amateur runner performed a

30min run at 10kmh followed by a 2min cooldown.

The plantar pressure measurement was performed

with the smart socks system. The obtained

measurement from both experiments was analyzed by

gait analysis methods developed exclusively for the

smart socks system – Force Vector and Pressure

Wave methods.

The Force Vector is a graphical gait

characterization method that is comparable to the

center of pressure method typically used for gait

analysis (Eizentals et al., 2018b). For each

measurement, a point is calculated, which is derived

from the measured pressure values of all sensors, and

the positions of each sensor on the sock as a unit

vector. All points calculated for any step together

make a line or trajectory, which describes the

respective step. The Y-axis of the graph represents the

foot in posterior (Y < 0) and anterior (Y > 0)

directions while X-axis represents the medial (X < 0)

and lateral (X > 0) directions (see Fig. 7). The Force

Vector values were calculated according to the

following equations 4 and 5:

icSPORTS 2019 - 7th International Conference on Sport Sciences Research and Technology Support















cos







(4)















sin







(5)

where ′ is the normalized measurement value for

each sensor obtained from equation (1), 

1,1,cos,cos,1,1 is a weight coefficient

assigned to each sensor and 



75°,105°,0°,180°,285°,255°



is the assigned

angle of each sensor. The sensor order is according to

that presented in Fig. 2.

Figure 7: The approximate position of a foot on the Force

Vector axis (not to scale).

The Pressure Wave diagram is a graphical gait

representation method that attempts to visualize all

sensor values for the whole step duration at each

moment of the step. To achieve this, an image is

created, where each sensor measurement during a

step is displayed as a color bar, the color intensity

representing the normalized value of the sensor (0 –

1), and on Y-axis normalized time of the step (0 –

100%) (see Fig 8). Sensors in the image are

distributed in following order (from left to right): left

lateral metatarsal, left lateral tarsus, left lateral heel,

left medial heel, left medial tarsus, left medial

metatarsal, right medial metatarsal, right medial

tarsus, right medial heel, right lateral heel, right

lateral tarsus, and right lateral metatarsal. Such

distribution allows analyzing the sensor activity

during the whole step in a comprehensible way,

giving information about which parts of the foot were

in contact with the ground at which moment of the

step and what was the relative pressure.

Figure 8: Example of a Pressure Wave diagram. The sensor

measurement during the stance phase is represented as a

color-bar plot with the colors representing the sensor

measurement amplitude at each moment. The sensor

numbering is according to that given in Fig. 2.

An example of the Force Vector lines for several

steps acquired in the first running measurement is

given in Fig. 9 and 10. Several conclusions can be

drawn from this result. First, both amateur runners are

rearfoot runners, as the vector line goes through the

lower part of the graph. The cooldown phase

measurement also shows less variation between the

steps as the force vector lines in Fig. 9c and Fig. 10c

are noticeably closer to each other compared to those

in Fig. 9a and Fig. 10a. The trajectory of force vector

lines in the cooldown phase is resemble the trajectory

of running phase more than that of the warmup phase.

The conclusion about rearfoot running style is also

supported by the image obtained from the Pressure

Wave diagram (see Fig. 11 and 12). According to

these images, for both participants, the initial contact

during this experiment was on the heel, as both heel

sensors (sensors 5 and 6) activated slightly before the

middle sensors.

In the second experiment, a participant was

requested to run for 30min at 10kmh to analyze both

the performance of the socks in a prolonged

monitoring, and possible effect of fatigue on the

running style. The results of this measurement

indicate that the style of running for the participant

gradually

changed from rearfoot running to midfoot

running. The Force Vector diagram shows that the

center of pressure value during each step shifted from

both heel (X < 0) and toe (X > 0) directions to the

center (see Fig. 13).

A Smart Socks System for Running Gait Analysis

Figure 9: Example of the calculated Force Vector result for

few steps of participant #1, (a) warmup, (b) running and (c)

cooldown.

Figure 10: Example of the calculated Force Vector result

for few steps of participant #2, (a) warmup, (b) running and

a b

Figure 11: Pressure Wave result for participant #1, (a)

warmup, (b) running and (c) cooldown.

a b

Figure 12: Pressure Wave result for participant #2, (a)

warmup, (b) running and (c) cooldown.

The same result can be observed in the Pressure

Wave diagram (Fig. 14), where initially each sensor

reached its peak value at a noticeably different time,

but with time the overlap gradually increased. At the

20min mark the calculated center of pressure is

mostly at the center of the feet, implying the midfoot

running style, which differs from the initial rearfoot

running.

This could signify that the person might

have naturally found the running pattern with the least

energy consumption.

icSPORTS 2019 - 7th International Conference on Sport Sciences Research and Technology Support

Figure 13: The calculated Force Vector diagrams for the

30min running measured at (a) 5min, (b) 10min, (c) 15min,

(d) 20min, (e) 25min and (f) 30min.

No significant change in the sensitivity of the

textile sensors during the 30min run was observed,

however, no strong conclusions could be drawn as the

participant didn’t sweat too much. It is expected that

wet from the sweat would affect the sensitivity of the

textile sensors.

5 CONCLUSIONS

Feet plantar pressure measurement and running gait

analysis with a smart socks system has been

demonstrated in this paper. The measurement

accuracy of the developed system was evaluated by

comparing it to the measurement obtained by the

Pedar measurement system. The mean differences

between step and stride times obtained by both

systems were 9.8ms and 14.9ms.

Two gait analysis methods were demonstrated in

practice for short (10min) and medium (30min) long

runs. The demonstrated methods were developed for

analysing the plantar pressure measurement obtained

by the SPSS and enable simple running gait

characterization. It was demonstrated how these

methods can be applied to evaluation of the plantar

pressure variation during running.

Figure 14: Pressure Wave diagrams for the 30min running

measured at (a) 5min, (b) 10min, (c) 15min, (d) 20min, (e)

25min and (f) 30min.

ACKNOWLEDGMENTS

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).

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icSPORTS 2019 - 7th International Conference on Sport Sciences Research and Technology Support