Apparel Concept Design for Analysing Range of Motion at the Hip to

Prevent Injury

B. Land, C. Morgan and R. S. Gordon

Faculty of Computing Engineering and Science, University of South Wales, Treforest, Pontypridd, CF37 1DL, U.K.

Keywords: Gait Analysis, Athletic Performance Apparel, Injury Detection, Real Time Injury Diagnosis, Range of Motion,

Hip Joint, Intra-articular, Extra-articular.

Abstract: Range of Motion (RoM) testing can identify the underlying causes of an athlete’s pain at the hip, be it

muscular (extra-articular) or damage to the joint itself (intra-articular). The purpose of this study was to design

a device which could detect characteristics of hip injuries from the motions and forces applied to the joint.

Hence supplying a coach with a method to analyse and diagnose injuries in real time. A design to measure

the RoM and gait at the hip was developed and later manufactured for testing on recreational athletes. Findings

supported the device in its potential to identify gait events and competitive motion at the hip, despite the

accuracy measuring less than that of the two-degree accuracy of the goniometer, competitive performance

analysis within the study is evidence of a conceptual design. With development, apparel such as ours has the

potential to supplement a coach’s quantitative analysis, identifying responsible motions and performance

metrics at hip responsible for injuries at the joint and the lower limbs using correlative data between motion

and the onset of injuries.

1 INTRODUCTION

The hip joint plays a central role in an athlete’s

performance across many sports, however, its

condition is often overlooked. In a study into

collegiate athlete hip and groin injuries, Kerbel et al.

(2018) found the hip to be a common location of

injury, accounting for 6% of all athletic injuries.

Because the synovial joint at the hip assists in all

movement below the waist, it is subject to some of the

most intensive demands of the body during exercise.

As a result, damage to the hip can risk an athlete’s

performance, or their career.

Mcgurran (2017) depicts how athletes find their

self-worth derived from their performance, and how

they would tend to endure the immediate pain of

injury, ignoring many serious injuries, particularly at

the hip, for substantial periods of time. With a

majority of hip injuries originating during adolecence

Siebenrock et al. (2011) suggests young high-level

athletes increase their risk of injury when subjecting

the hip to repeated high stresses and directional

loading while the skeleton is still developing.

https://orcid.org/0000-0003-1419-2771

An athlete’s fear of injury has shaped training

programmes to strengthen and protect the most

vulnerable areas on the body. Consequently attempting

to prevent injuries, fitness evaluations have become

common practice in all sports from a young age, as

coaches seek to identify potential areas of weakness.

Relevent theory is based upon correlations identified

between physical chara-cteristics and performance, for

example, poor flexibility. Noonan and Garrett (1999)

describe how a ‘weak, stiff’ muscle will significantly

inhibit its energy-absorbing capabilities, increasing its

susceptibility to strain injury. These fitness evaluation

tests however are not discipline specific and are not

always reflective of an unpredictable competitive

scenario.

In the event of an injury whilst competing,

evaluation is performed retrospectively, this becomes

an issue when related to the hip and lower limbs.

Misdiagnosis, due to the complex composition of the

hip and lower limbs, has become extremely common.

To better our understanding of the capability/

demands of the body, there has been an increase in

performance monitoring technology, identifying

patterns and trends for a coaches interpretation.

Land, B., Morgan, C. and Gordon, R.

Apparel Concept Design for Analysing Range of Motion at the Hip to Prevent Injury.

DOI: 10.5220/0008165600640075

In Proceedings of the 7th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2019), pages 64-75

ISBN: 978-989-758-383-4

Although vitals can be measured with relative ease,

the physical demands on the joints, particularly at the

hip, remain in the realm of theory. Consulting with

Williams (2018) for his expertise on lower extremity

injuries, he stated that there was no way to measure

RoM at the hip in a competitive scenario. Such

information would prove insightful for a coach’s

consideration in preparing an athletes training, to best

prepare the athletes body for the demands they face in

competition. Clearly, the analysis of the RoM has the

potential to aid in the diagnosis of intra/extra-articular

injuries and performance analysis of the hip.

Supporting an athlete’s pursuit of greater

performance and reduced absence time due to injury.

2 LITERATURE REVIEW

2.1 Athletes’ Approach to Hip Injuries

After a single week of inactivity an athlete can begin

to experience muscular atrophy, and with an

anticipated 6-8 week break in the event of a serious

hip injury, these are feared amongst athletes due to

the potential career setbacks.

In the analysis of injuries, it is unreliable to use

anecdotal reports to indicate injury patterns, because

of the number of external risk factors which may

influence an injury mechanism. Whilst in more

detailed studies additional independent factors can be

considered, initial consideration begins with the

broader picture of incidence rates, requiring a definite

distinction between competitive and training

scenarios. Although the two at times may overlap,

they require different demands which can influence

the likelihood of injury. Hootman et al. (2007)

investigated over one million young athletes between

1988 and 2004 covering a range of sports. The

findings support the premise that competitive

situations exhibit a greater risk of injury.

There remains no universally adopted definitions

for classifying injuries in competitive scenarios,

typically varying in classification by the depth and

focus of study. For example, in papers such as Cloke

et al. (2010), ‘non-contact’ refers to an athlete’s

injury mechanism when an opponent is not physically

interfering with play.

2.2 Musculoskeletal Analysis of Hip

Injuries

Most hip injuries share overlapping symptoms, often

resulting in a vague diagnoses in the absence of an

experienced professional. As such the design should

be able to assist in supporting a clinical diagnosis of

a hip injury and identification of the onset of

symptoms.

The Kerbel et al. (2018) study into the

epidemiology of hip and groin injuries, reports

muscular injuries as the most common. Whilst this

may be the case, the more severe injuries are intra-

articular, with damage or deformities to the skeletal

system carrying a longer absence. These are the more

feared injuries at the hip. They explain that intra-

articular injuries only become symptomatic after a

significant period, leaving substantial damage in their

wake. Intra-articular injuries often require surgery to

rectify and achieve a timely return to participation.

Thus, research conducted in the early identification of

correlations of intra-articular injuries is becoming

highly valued.

If properly utilized, simple tests such as

identifying the RoM of the hip can narrow the list of

possible injuries. For example, the Siebenrock et al.

(2011) investigation into femoroacetabular

impingement in adolescents, uses the premise that a

decreased internal rotation indicates a ‘structural

abnormality’ as the underlying cause. RoM testing

however not limited to the identification of intra-

articular injuries. Neumann (2010) describes how

reduced motions at the hip might suggest damage to

those muscles responsible however, the composition

of the muscles in the region of the hip make

identifying a single damaged muscle difficult. Byrd

(2007) claims that differencing the onset of pain

between active and passive motion of the hip can

identify the intra/extra-articular nature of the injury.

Should the injury be extra-articular, specific motions

of the hip can be used to further narrow down the

nature of the injury.

2.3 Clinical Measurement of the Hip

Manual handheld goniometry is both a low cost and

simple procedure, making measurements highly

accessible and easy for physiotherapy clinics. Yet, it

is suggested that inaccuracies in the traditional

method of measuring the hip’s RoM remain, making

hip injuries difficult to correlate and compare.

(Yazdifar et al., 2013). Here, repeatability errors in

traditional methods, compared with more

contemporary video tracking methods have been

reported. Still neither method allows for an easy

method of performance comparison between athletes.

Elson and Aspinall (2008) identify the ‘neutral’

position of the pelvis additionally to be a key issue.

Claiming that when lying prone, the posture of the

Apparel Concept Design for Analysing Range of Motion at the Hip to Prevent Injury

pelvis is altered with respect to its position to the

couch plane. Because both the pelvis and femur can

move relative to one another it is imperative that

measurement of the positional relation of both in a

competitive scenario is taken.

2.4 Technology of Performance Data

Acquisition

Fahrenberg (1997) suggests that the use of a

piezoresistive accelerometer could help to distinguish

between the postures and motions of test subjects, and

ultimately, he concludes that such an approach is

viable. However, he notes that the lack of any

universal standardized guidelines for the positioning

of such sensors prevents cross-laboratory

comparisons between athletes.

Analysis of the human walking/running pattern in

phases can directly identify the functional

significance of the different motions generated at the

individual joints. Tao (2012) explains this in the

breakdown of the eight stages in a walking pattern, as

the sequential motion completes three tasks; ‘weight

acceptance, single limb support and limb

advancement’. It is suggested that gait phases may

each be detected by identifying the orientations of the

leg segments at any one time, with the use of angular

rate data derived from a gyroscopic sensor. Meaning

our design should be capable of identifying the stages

and characteristics of the individual’s gait cycle so

that together with the RoM data and force readings

sound conclusions may be drawn as to the motion of

the lower body in high velocity competitive

scenarios. This data then paired with additional

external analysis could help to build a better

understanding of the demands of the lower limbs

performing certain motions.

3 DESIGN

3.1 Femur Movement

The design proposed and discussed herein uses an

accelerometer to measure the RoM at the hip. In the

same way the RoM measures femur rotation away

from a midline designated from an initial stationary

stance, an accelerometer can measure the independent

inclination of each axis away from its initial position.

By attaching an accelerometer to the upper leg, it is

expected that the angle through which the leg turns

and hence the angle through which the femur rotates

within the acetabulum may be measured. This

accelerometer may, thus, measuring flexion,

extension, internal and external rotation as well as

abduction and adduction.

Concerning the selection of a sensor for the

design, an accelerometer was deemed most suitable.

Firstly because of its linear relationship with

changing temperature. The minimal linear

acceleration and zero-g deviation sensitivity of the

sensor when under varying temperatures, suggests a

change in body temperature, due to muscle exertion

or change in environment will minimally impact our

data accuracy in comparison to other sensors.

A smaller power supply would also be beneficial

for the design, reducing unnecessary weight and hence

reducing the likelihood that the design may interfere

with the performance of the athlete. The power

consumption of the accelerometer is significantly

lower than its counterparts, making it the favoured

sensor to minimize the power supply in the design.

Furthermore, noting all sensors are subject to

unwanted influence imbedded in the device’s nature.

The raw accelerometer data is also likely to suffer

from noise due to mechanical vibrations and

calibration errors. However, accelerometer errors do

not diverge with time and can be handled effectively;

a stark contrast to a gyroscope which when subject to

sudden movement will result in large drift errors.

Because of the capability to constructively handle the

errors which may arise from accelerometers, the

design of a sole accelerometer inertial measurement

unit would seem most promising for the design.

Alongside the exact orientation of the

accelerometer, the ability to determine what phase of

the gait cycle the hip is in, such as whether the leg is

planted or free, will aid in our understanding of

motion at the hip. This understanding can be achieved

using the vertical acceleration profile measured by the

accelerometer.

Further important considerations relate the

frequency domain characteristics of the

accelerometer, and associated data collection

hardware and software. It is necessary to tailor the

dynamics of the measurement system to extract

accurate, meaningful data, whilst rejecting sources of

noise and ensuring aliasing is not a factor. Seeing to

at least match the accuracy of a goniometer the

system must be capable of measuring a Minimal

Detectable Change (MDC) of at least 2⁰ . It is noted

that many previous studies such as Turcot et al.

(2008), used sensors with a sampling rate of 100Hz

and this can be deemed the minimum requirement for

the sensor to begin testing.

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3.2 Sensor Location

For the location of the sensor on the upper leg, it was

important to locate the sensor in a position where it

will experience minimal movement because of

muscle contractions during dynamic motion. This is

to be found along the anterior of the upper leg on the

Vastus Lateralis as described by Backhouse (No

date). Tong and Granat (1999) noted that provided the

sensor remains along the line of the landmarks the

sensor reading will be replicable (Figure 1), and

hence independent of the user. Potentially initiating a

standardised methodological approach for cross-

comparison experiments. For ease of positioning, the

sensor will be located at the lower end of the upper

leg towards the knee, and in line with the Lateral

Epicondyle, thus following the clinical positioning of

the goniometer. Similar to that of Turcot’s (2008)

experimental positioning when investigating

Osteoarthritis patients.

Figure 1: Vastus Lateralis in the Sagittal Plane and the

Dotted Line indicating where Tong and Granet (1999)

suggests the same experimental data from the

accelerometer is obtained. (Muscolino 2018).

3.3 Pelvic Movement

In the same way that the femur moves from its datum,

so too will the pelvis from its datum (Elson and

Aspinall, 2008), particularly in vigorous dynamic

motion. The pelvis has a natural inclination known as

pelvic tilt that needs to be measured statically prior to

dynamic measurements, and accounted for in

subsequent processing. However, pelvic tilt in the

sagittal plane can be determined by measuring the

angle between a line intersecting the ASIS and PSIS

landmarks, and the horizontal (Transverse) plane.

Whilst, in the Coronal Plane, a line between the two

ASIS landmarks across the pelvis, compared to the

transverse plane indicates the natural pelvic tilt.

Measuring the rotation of the pelvis using the change

in inclination of the gravity vector from its initial

stationary reading will yield the change in pelvic

angle relative to all three-axes, allowing for full 360-

degree monitoring of the pelvis. Because of the

compression shorts ability to secure the sensor close

to the skin, an additional accelerometer located

between the PSIS landmarks on the back will

minimise the adverse effects on performance,

locating the sensor weight close to the centre of

gravity of the human body, least influencing

performance.

3.4 Gait Analysis

Although not the focus of this study, accelerometers

can measure a range of performance metrics. The

likes of gait metrics (e.g. cadence, stride length and

forces through the leg) can complement the RoM

data, indicating the position of the leg and weight

distribution through the stride and across the lower

body, as indicated by research such as Turcot et al.

(2008). In addition, future developments may see the

range of recording metrics expand further with the

growing capabilities to interpret the recorded data.

3.5 Accelerometer

The accelerometer used is the Adafruit MMA8451

breakout. Its relatively small size facilitates the

sensors’ positioning for concept evaluation. The

Adafruit supported Arduino software is readily

adaptable to the manipulation of the sensor readings

for exporting in a convenient format. The time-stamp

of each reading will be marked in milliseconds due to

the 9.6kHz sensor refresh rate, later being converted

to a more traditional unit. For concept evaluation, an

SD card was used to record the delimited data and

provide a means of importing the data into Matlab for

processing.

4 METHODOLOGY

4.1 Participants

Prospective athletes were contacted and given an

information letter outlining the investigation’s aims,

testing protocol and hence the requirements of their

participation. Recreational athletes participating in

Football, American Football and Running

participated in the testing, and provided informed

written consent prior to testing and also completed a

brief survey to determine limb dominance and

Apparel Concept Design for Analysing Range of Motion at the Hip to Prevent Injury

suitability screening. Screening criteria for

participants included:

 No previous serious hip injury or defect

known within the last 12 weeks so not to

indicate significant injury

 Injury-free status at the time of testing

(Absence from training for no more than

the preceding three weeks, currently full

participation in training and/or not

recovering from participation in vigorous

exercise performed prior to testing.)

 Remained injury free for the duration of the

testing

Screening criteria was performed to ensure healthy

participants in order to capture data under conditions

of full uninhibited performance. All participants met

these criteria. Participants were asked to supply their

own sportswear to wear over the shorts to create a

traditional environment for regular performance

analysis. The footwear of each participant were also

recorded because of their capacity to affect the

elasticity of the forefoot region, changing contact

time and propulsive force with intensity.

4.2 Study Overview

RoM testing was performed in accordance with the

clinical specifications after participants performed a

warm up of their choosing with which they are

familiar and comfortable. Participants were

individually examined over a period of three days and

underwent dynamic testing individually. A total of

three participants were selected (3 Male) (age = 24.3

± 3.39 years, stature = 181.19 ± 7.62 cm, mass =

78.167 ± 6.8 kg, Body Mass Index (BMI): 24.45 ± 2.3

kg/m

) as this was deemed a suitable sample size for

proof of concept.

4.3 Testing Protocol

Participants wore instrumented compression shorts in

a size which they personally deemed comfortable,

and were adjusted so the midline of the elasticated

waist band aligned with the ASIS and PSIS

landmarks. These landmarks, as well as the greater

trochanter and lateral epicondyle midline, were also

scribed with a marker pen on the outside of the

compression shorts for reference. The shorts were

then returned for amending and stitching of the

accelerometers in line with the reference markings.

The accelerometers were stitched securely in position

and the control unit (Arduino) was secured between

the PSIS landmarks for dynamic testing by securing

the unit to an adjustable GoPro strap.

4.4 Procedure

The same pair of running shoes were worn by each

participant for all tests, preventing changes in limb

kinematic data during running and running economy.

All participants wore low rise running shorts to avoid

interference with the sensors above the compression

shorts. Tests were conducted indoors so that the

environmental conditions varied minimally. Running

surface conditions were dry and clear of interfering

debris. Accelerometers were calibrated prior to fitting

on the participants.

4.5 Anthropometric Data

Factors such as body composition, anatomy and

injury history can all predispose an athlete to risk of

injury, consequently, basic anthropometric data was

acquired prior to testing. Anthropometric data were

taken in an isolated first aid room, preceding the RoM

tests to correlate any plausible phenomena that may

compromise the results. A wall mounted tape

measure (GIMA 27335) and electronic scales

(Etekcity 4074s) were used to measure stature and

mass (±0.1cm and ±0.1kg respectively). The upper

leg circumference was measured using a fabric tape

measure around the point the accelerometer is

attached ±1mm. The anthropometric data gathered

here, was interpreted in excel to obtain averages and

bounds for the participants.

4.6 Clinical RoM Testing

As discussed in section 2, the static RoM at the hip

was measured using a goniometer (IDASS 12”

Goniometer) to the nearest degree for the base

reference readings. These were compared to the

sensor readings to verify the accuracy in the design’s

RoM measurements. An examination bench was used

to perform the stationary RoM tests and care was

taken to observe whether any soft tissue around the

hip restricted the motion of the joint below its full

range. Both dynamic and passive measurements were

taken, once without the shorts on, and again with the

shorts on, to provide a baseline the RoM readings.

Participants were constantly spoken to throughout the

tests to clearly define the requirements from the

participant.

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4.7 Treadmill – Gait Analysis

To compare the design’s capability in measuring

basic gait characteristics, treadmill (LifeFitness

9500HR) running at a constant pace was used. A 25 s

run at a comfortable continuous pace was selected to

replicate similar testing methods performed by Turcot

(2008). The participants were given as much time as

necessary to become accustomed to the pace before

testing commenced. They were then required to walk

for 30s before increasing the belt speed to their fastest

comfortable pace. Sensor positions were checked

before and after each repetition to ensure constant

functionality and that they remained in line with the

reference landmarks. The data collected here is used

to analyse the subject’s gait and compare with

published results hence, validating the design’s

performance. This comparison will confirm a

working model prior to the addition of further

programmes of activity allowing the collection of

further data. This validation comes in the form of the

tracing of Y-Axis acceleration in Matlab and

comparing the data to that of experiments conducted

in similar research ventures of gait analysis.

Participants were required to rest for two minutes

between all repetitions.

4.8 Shuttle Runs – Isolated Change in

Direction

As no identifiable research methods look to analyse

the more dynamic performance metrics, testing began

with one of the simpler movements. Performing 10x,

10m shuttle runs at a comfortable pace, looking to

isolate a basic 180-degree turn. The 10m line was

marked using electrical tape to prevent the participant

slipping on any foreign object whilst performing. The

data will be reviewed alongside frame by frame slow-

motion footage for time references (120fps, GoPro

Hero 5). The camera remained stationary throughout

the entirety of the testing, although the participant’s

velocity caused them to occasionally turn outside of

frame.

4.9 Illinois Agility Test – Unpredictable

Hip Movement

This test looks to recreate the RoM in a more

competitive scenario. Being initially unaware of the

kind of data the sensors might capture; this test was

more intended as a scope to the future developments

of the design. The test can be found commonly

performed as part of a fitness evaluation, measuring

agility and so recreates a basic athletic scenario where

the subject is competing against a stopwatch. The data

is predicted to be noisy however, will give us our first

insight into the type of data obtained in a competitive

scenario.

4.10 Data Analysis

All data captured was run through Matlab Software,

identifying the acceleration values for each axis and

the subsequent inclination angles of the sensor and

hence the femur and back orientations. The process of

the Matlab software is as explained in the Design

Section previously.

5 RESULTS

5.1 Gait Characteristics - Treadmill

Due to time restraints any filtering and manipulation

of data was minimal. Only a moving average filter

was applied due to its ease and ability to remove much

of the unwarranted noise.

From processing the Y-Axis Acceleration, it is

possible to visualise the gait cycle of each participant

in each test. In the increase in acceleration of the

treadmill, the consequential increase in stride length

and intensity shows a visible increase. This

observation was most noticeable in Participant 3’s Y-

Acceleration graph shown in Figure 2, by the sharp

increase in amplitudes when the pace increased

before and after the red line at 34s.

Figure 2: Participant 3 – Treadmill Leg Accelerometer - Y-

Axis Acceleration Plot (5km/h ->> 9km/h).

The increased acceleration values indicate a

greater force moving through the leg as the

participant looks to increase his stride velocity and

Apparel Concept Design for Analysing Range of Motion at the Hip to Prevent Injury

cadence to coincide with the belt’s increased velocity

from 5km/h to 9km/h.

Figure 3 presents a more detailed view of the

participants running strides. Exhibiting the type of

wave that would be expected prior to interpretation

using 3

party software for the analysis of further gait

characteristics i.e. toe off, heel contact and cadence

etc.

Figure 3: Participant 1 – Treadmill Leg Accelerometer - Y-

Axis Acceleration Plot, Showing a zoomed in look at the

stride pattern whilst at 9km/h.

5.2 Gait Characteristics – Shuttle Runs

Like the Treadmill Y-Acceleration graphs, it is

possible to identify a stride pattern and external

events, however the addition of the changing of the

stride has made the interpretation of the data more

difficult. Aligning the video with the data, shows each

of the negative peaks to be the increased force

experienced through the leg whilst changing

direction, each of the 10 times shown in Figure 4.

Figure 4: Participant 3 – Shuttle Run Leg Accelerometer -

Y-Axis Acceleration Plot.

Participant 3 performed the test the fastest and

exhibited more defined peaks when changing

direction suggesting that the increased peak definition

comes because of an increased force through the leg

whilst changing direction, implying Participant 3 to

be more agile than Participant’s 1 and 2.

5.3 Gait Characteristics – Illinois

Agility Test

The added change in direction with the agility test

makes the data harder to interpret. Participant 3

completed the test fastest in 16.77s, and their resultant

data makes for clear reading in Figure 5.

Figure 5: Participant 3 –Agility Test Leg Accelerometer -

Y-Axis Acceleration Plot.

The decrease in stride length between the initial

straights and corners when the participant decelerates,

is signified by the increased frequency and lower

negative acceleration peaks. Whilst the sharp positive

peaks indicate a lengthen in stride as the participant

drives the knee higher to accelerate as quickly as

possible along the straights to gain speed.

5.4 RoM against Goniometer

Flexion and extension data proved promising for

initial testing, carrying differences of 3.9º, -17.8º and

-5.0º for each Participant respectively. The limit of

maximum motion was held for an unspecified amount

of time to allow a plateau to generate in the sensor

data, enabling the angle of the hip to be clearly

identified, as shown in participant 1’s flexion

measurement in figure 6.

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Figure 6: Participant 1 – Static Flexion of the Hip.

Contrastingly sagittal plane motion proved very

poor. Examples of this came in the measurements of

participants 2 and 3 abduction and adduction

measurements (Figure 7).

The adduction measured from participant 3’s data,

exhibited a 33.00% difference from the goniometer

measurement while abduction heralds a higher

percentage difference of -57.00%. A high noise is

also noteworthy in the data, which is believed to

originate from the sensors high operating frequency

when the individual holds an uncomfortable hip

position at the maximum RoM limit resulting in the

recording of minor oscillations as the body tenses so

to hold the unnatural position.

Figure 7: Participant 3 - Static Abduction the hip.

Internal and external rotation for participants 2

and 3 exhibited the same inconsistencies and

inaccuracies. With participant 2’s graphs (Figure 8)

showing the plateau at angles greater than that

measured for internal rotation (+22.86%), whilst

external rotation seemed reasonably accurate

(+15.625%) compared to other internal and external

rotation measurements in comparison.

Figure 8: Participant 2 – Static External Rotation of the Hip.

5.5 Dynamic RoM Analysis

When performing dynamic tests, similar phenomena

to that in the gait and static RoM measurements were

identified. This is expected as the two naturally

coincide. The treadmill elicited a repeating similar

flexion and extension amplitude range for all three

participants in accordance with their personal running

form. The increase in velocity of the belt resulted in

the participants consequently increasing their

cadence and length of their stride to match the new

velocity of the belt. Participant 3’s data showed great

definition on the treadmill as did their Y-Acceleration

graph (Figure 9).

Figure 9: Participant 3 – Dynamic Flexion/Extension of the

Hip - Treadmill (5km/h ->> 9km/h).

Conversely to participants 1 and 3, participant 2

exhibited an inconsistent stride pattern, resulting in

occasional smaller amplitude breaks.

Apparel Concept Design for Analysing Range of Motion at the Hip to Prevent Injury

Abduction, adduction, internal and external

rotation also exhibited the same inconsistent and

inaccurate pattern as seen in the stationary

measurements. Evidenced most from cartesian angle

graphs. The data was far greater than that would be

commonly expected for a consistent pattern of

running, often peaking at values greater than 75 and

40 degrees for participants 1 and 2 respectively.

These inaccuracies left the data gathered in these

motions discounted from any further evaluation.

5.6 Dynamic Rom Analysis – Shuttle

Runs & Illinois Agility Test

When concerning the free dynamic testing, flexion

and extension data is very sharp and the peaks very

defined. Instances of changing direction can be

identified in the small periods of low amplitude in

between the large peaks caused because of the athlete

driving the knee forward to accelerate. This is evident

in both the shuttle runs and agility graphs and is

exhibited in Figure 10.

Figure 10: Participant 3 – Dynamic Flexion/Extension of

the Hip – Agility Test. Red dotted circle signifies low hip

angles when decelerating. This shape on the graph can be

used to identify deceleration phases and to evaluate the

actions of participant during testing.

The very sharp peaks and rapid changes in the

angle of the hip are shown even more in the agility

test data for that of participant 3. Again, the changes

in direction can be seen in the smaller amplitude

breaks however these are even smaller and harder to

identify between the greater peaks and angles of the

hip when the participant in driving their leg forward

to accelerate as quickly as possible.

6 DISCUSSION

6.1 Reliability of Results

The sewing and tape holding the sensors in place held

throughout testing, however the wiring to and from

the sensor had the nature to snap when put through

the more dynamic testing and so mid testing repairs

were needed. Leaving it necessary to perform repeats

as the wires would snap during the test. It was also

noticeable that the back sensor stitched into the elastic

waist band, remained stiff and upright, often losing

skin contact when the participant surpassed an angle

of approximately 30 degrees’ flexion at the waist.

Environmental errors came from the Treadmill

used, likely introducing errors between participants,

due to them being open access to the public. Belt

speed was unverified and so is likely not to be the

exact velocity output read off the dashboard due to

friction and wear in the machine. The dynamic tests

also saw occasional slipping which was evident upon

video review. The participants selected footwear, was

not always the most suitable for indoor flooring and

lacked the friction for a dynamic turn, which would

affect sprint performance and the agility test times.

6.2 Gait Characteristics and

Comparisons

The purpose of the treadmill testing phase was to first

initially validate the sensor’s capability to record

basic acceleration data. In doing so, allowing us to

evaluate and identify the stride phase the participant

is in.

Comparing the shape of our graph to that of other

gait analysis papers, a similar trend can be seen in the

vertical acceleration throughout the running strides

performed on the treadmill by the participants. The

acceleration pattern exhibited walking over the initial

30s in Figure 2 is like that of Yang et al (2012) study,

the repetitive similar amplitude peaks (+0.14g, -

0.40g) showing the participant walking at a consistent

pace. Figure 3, zooming in on the acceleration line for

Participant 1, shows a sharp acceleration pattern from

peak to peak (+0.38g, -0.41g), again like Yang’s

study. However, lacking the definition at the peaks to

that of Takeda et al (2008) study. Unlike Takeda’s

data, the accelerations exhibit a single peak

acceleration value, rather than a cluster of data points

around the peak producing a subtle curve around

maximum amplitude. This comes as a result of an

aliasing effect. With the athletes performing

movements at a rate greater than the sensor can

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

capture. In increasing the sensor frequency to greater

than 100hz it is believed that such aliasing would be

avoided, in turn improving the resolution of the data.

Less than 100Hz value being more suited to the

slower gait analysis experiments conducted by the

likes of Turcot et al. (2013), designed to analyse

slower motions. Increasing our sampling frequency

would provide us with additional data points at the

peaks once filtered, leaving a more defined

waveform, aiding in the identification gait events and

action detection

Using these peak accelerations however, changes

in running speed are identifiable, one such event is

evident in the increase from 5km/h to 9km/h in Figure

2 for Participant 3, as the peaks increase to a

consistent new amplitude. However, this can be used

to analyse the movement of the athlete for more than

single speed changes alone. Shown in the shuttle run

graph in Figure 12, the smaller amplitude

accelerations between the negative peaks signify the

decrease in stride length, decelerating before

changing direction 180 degrees. The sudden sharp

peaks then signify the greater forces experienced by

the sensor, as the athlete drives their knee forward

after changing direction looking to accelerate into a

sprint, heralding a greater force through the leg and

up through the hip.

Despite the resolution difficulties, the vertical

acceleration has allowed the identification of the gait

phases. This is possible when a relatively consistent

waveform is produced as the stride pattern remains

consistent, like that of our participants running on the

treadmill. However, in more dynamically demanding

competitive scenarios these consistent peaks will not

be observed (Figure 10), One example of such

difficulties are the changes in peaks when participants

performed repetitive dynamic actions like the turns in

the shuttle runs, leading to suspected variations in

participant intensity as they began to fatigue over

time. Participant 3’s shuttle runs shown in Figure 4

show lower peaks for turns eight and nine. It is

suspected that their muscles exhibited a lower force

to decelerate as they were running at a lower speed

towards the end of the 10 shuttle runs. Additional

testing, timing each length of the 10m sprint to

measure intensity may verify this, and if found true

can be used as an additional metric for a coach’s

consideration. However, the possibility remains that

this data could give us an insight not yet achieved into

competitive athletic performance.

6.3 RoM at the Hip

Static RoM at the hip yielded conflicting accuracies

for the different motions at the hip. Flexion and

extension measurements proved promising for an

initial concept, having an average difference of -6.3º

to that measured with the goniometer. A greater

difference than that of the 2° MDC of the goniometer

that design looks to match, showing the measurement

method and interpretation still requires work. The

differences also fluctuated between being greater than

that measured and less than the goniometer, therefore

eliminating a systematic error as the cause. A

variance of -6.3º from the goniometer is far from the

accuracy which is required in the evaluation of

athletic performance. Ideally this would be as small

as possible for accurate measurements to ensure

reliable conclusions can be drawn. Should an athlete

experience hyperextension of the hip joint for

example, then the results must be able to show this,

and to what degree has the hip joint over-extended. A

decrease in error could come with an increase in the

resolution of the data as discussed before.

It is possible that using a gyroscope in tandem

with the accelerometer may allow other motions of

the hip to be measured accurately. Abduction,

adduction, internal and external rotation, having

maximum percentage differences of -57.00% and

+65.11% respectively for each motion pairing. These

percentage differences in abduction, adduction,

internal and external rotation result in the data being

disregarded in any further processing due to their

unreliability.

A gyroscope can be tasked with exclusively

measuring the rotation of the hip in the coronal plane,

measuring abduction and adduction. This is likely

more accurate than the accelerometers single gravity

vector being used to measure all three axis changes in

angles respectively. The addition of an accelerometer

here may also help account for the gyroscopic drift

which may be experienced in the dynamic motions

but will require testing and further development to

evaluate its suitability.

However, it is the case that many papers focus on

the flexion and extension of the hip in gait analysis

alone. Alonge et al. (2014) graphically plots the

flexion of the hip through their gait motions. Once the

pace is increased to 9km/h for participant 3 (Figure 2)

the angles reflect that more of Alonge’s gait flexion

and extension results, peaking consistently around 40

degrees. It is very noticeable however, the peaks

greater than that of 80 degrees despite the use of a

moving average filter. At a comfortable pace ideally,

the stride pattern will remain consistent throughout.

Apparel Concept Design for Analysing Range of Motion at the Hip to Prevent Injury

However, as the participant relaxes throughout the

duration of the run, they may move back down the

belt of the treadmill and must move forward again.

This motion requires a larger stride and greater

flexion and extension of the hip reasoning these large

peaks. The participants related this inconsistent pace

to inexperience running on a treadmill.

Peak changes in flexion and extension enable us

to understand the stage of each test the participant was

in when conducting the shuttle and agility drills. The

lower peaks suggest smaller and lighter steps

associated with changing direction and speed in the

shuttle runs, and this is evident in the breaks in the

peak accelerations (Figure 4). This occurred prior to

the larger peak flexion and extensions of the hip

associated with driving the knee forward to accelerate

quickly. This ability to sense a change in direction is

also notable in the agility tests (e.g. figure 14),

suggesting it may be possible to identify actions of

the athlete in a competitive scenario and hence

measure the performance metrics of the hip required

to perform such a movement. Opening the area of

competitive scenario research to identify

performance metrics associated with actions

performed in play, serving as an additional method of

performance evaluation. Such as the likes of the

capability of muscles about the hip to produce

moments when shooting in football, associating

muscular performance to speeds obtained by the ball

in flight. However, this will take a substantial amount

of time and case studies to support this hypothesis. As

well as substantial number of case studies to support

the correlation study of hip RoM and consequential

injures.

7 CONCLUSIONS

It was hypothesised that the measurement of RoM

and gait in a competitive scenario could identify the

position, motion and force through the hips and legs

prior to and at the time of injury. In doing so

supporting the real time injury analysis and the

diagnosis of injuries, by using motions at the hip and

their correlated driving muscles to identify possible

muscle damage and causes of pain and injury.

Both extra and intra articular injuries can be

identified by a change in the RoM at the hip.

However, large differences (-6.3º) in the sensor’s

readings, means that sound conclusions drawn as to

the exact angular position of the hip joint cannot be

made. However, it is possible to visualise the motion

of the upper leg. In cross examining video references

to the captured data, it is possible to identify

characteristics of an athlete’s form which may impact

performance. One such possible identification is from

the force measured through the leg in figure 10.

Showcasing participant 3’s fatigue over time with

lower peaks for turns eight and nine. Suspecting that

their muscles exhibited a lower force to decelerate as

they ran at a lower speed towards the end of the 10

repetitions. Such an example is relatively basic

however, showcases the desired foundations of

analysis of form and hip motion.

In testing on recreational athletes, it was possible

to differentiate form and gait characteristics in a

competitive scenario, unlike motion capture, giving a

closer insight into the demands of the lower limbs.

One such obvious example was the comparison of an

athlete’s acceleration and deceleration patterns.

Increased driving angle (Figure 10), cadence (Figure

5) and the forces exerted through the leg (Figure 5),

build a picture of the competitive performance of the

athletes. Whilst testing in this research is limited, the

findings are encouraging to show that a more detailed

analysis of the hip and the lower limbs is possible

when using our design. The shorts considerable lower

pricing point and ease of use make the design more

accessible to the general athletic market, laying the

foundations to better our understanding of the

competitive demands of the hip and lower limbs.

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Apparel Concept Design for Analysing Range of Motion at the Hip to Prevent Injury