Objective Classification of Dynamic Balance Using a Single Wearable

Sensor

William Johnston

1,2

, Martin O’Reilly

1,2

, Kara Dolan

1,2

, Niamh Reid

1,2

Garrett F Coughlan

and Brian Caulfield

1,2

Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

School of Public Health, Physiotherapy & Sports Science, University College Dublin, Dublin, Ireland

Connacht Rugby, The Sportsground, College Road, Galway, Ireland

Keywords: Dynamic Balance, Inertial Measurement Unit, Y Balance Test, Fatigue, Lumbar.

Abstract: The Y Balance Test (YBT) is one of the most commonly used dynamic balance assessments in clinical and

research settings. This study sought to investigate the ability of a single lumbar inertial measurement unit

(IMU) to discriminate between the three YBT reach directions, and between pre and post-fatigue balance

performance during the YBT. Fifteen subjects (age: 23±4, weight: 67.5±8, height: 175±8, BMI: 22±2) were

fitted with a lumbar IMU. Three YBTs were performed on the dominant leg at 0, 10 and 20 minutes. A

modified Wingate fatiguing intervention was conducted to introduce a balance deficit. This was followed

immediately by three post-fatigue YBTs. Features were extracted from the IMU, and used to train and evaluate

the random-forest classifiers. Reach direction classification achieved an accuracy of 97.80%, sensitivity of

97.86±0.89% and specificity of 98.90±0.56%. “Normal” and “abnormal” balance performance, as influenced

by fatigue, was classified with an accuracy of 61.90%-71.43%, sensitivity of 61.90%-69.04% and specificity

of 61.90%-78.57% depending on which reach direction was chosen. These results demonstrate that a single

lumbar IMU is capable of accurately distinguishing between the different YBT reach directions and can

classify between pre and post-fatigue balance with moderate levels of accuracy.

1 INTRODUCTION

Dynamic balance requires the maintenance of

equilibrium during tasks that involve movement of

the centre of mass outside of the base of support

(Gribble et al., 2012). The Star Excursion Balance

Test (SEBT) is one of the most commonly used

dynamic balance assessment tools (Holden et al.,

2016, Doherty et al., 2016, Gribble et al., 2012, Smith

et al., 2015). It assesses many facets of the

sensorimotor spectrum, including strength, proprio-

ception and dynamic balance, closely mimicking the

functional demands required for optimal sports

performance. The SEBT requires the individual to

maintain their balance, while reaching as far as

possible in eight directions (Gribble et al., 2012).

Large bodies of research have demonstrated

dynamic balance deficits, as measured by the SEBT,

between control and pathological groups with

conditions such as acute ankle injuries (Doherty et al.,

2015), chronic ankle instability (Doherty et al., 2016)

and anterior cruciate ligament injuries (Herrington et

al., 2009). Additionally, researchers have attempted

to establish the role these assessments play in the

detection of risk factors that may predispose

individuals to lower limb injuries (Gribble et al.,

2015, Plisky et al., 2006). Despite this, there are a

number of limitations to the SEBT which should be

considered. These include the non-standard stance

surface, the lack of a definite starting point reference,

the time consuming nature of completing eight reach

directions and the requirements of the assessor to

visually monitor the stance foot, while marking the

maximal reach distance (Gribble et al., 2012, Plisky

et al., 2009). In an attempt to address some of these

limitations, improve the reliability and the uptake of

dynamic balance tests in clinical practice, the

redundancy of five of the eight reach directions was

demonstrated. This resulted in the development of the

commercially available Y Balance Test (YBT)

(functionalmovement.com, Danville, VA) which

incorporates the anterior (ANT), posteromedial (PM)

and posterolateral (PL) reach directions of the SEBT

(Plisky et al., 2009).

Johnston, W., O’Reilly, M., Dolan, K., Reid, N., Coughlan, G. and Caulﬁeld, B.

Objective Classiﬁcation of Dynamic Balance Using a Single Wearable Sensor.

DOI: 10.5220/0006079400150024

In Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support (icSPORTS 2016), pages 15-24

ISBN: 978-989-758-205-9

While the YBT does address some of these

aforementioned limitations, there are a number of

challenges which continue to restrict its use in clinical

practice. Firstly, while research has shown that these

assessments are capable of demonstrating statistically

significant differences in reach distances between

groups (Plisky et al., 2006, Gribble et al., 2015,

Doherty et al., 2016, Doherty et al., 2015, Herrington

et al., 2009), it has been difficult to determine

clinically relevant cut off points. Plisky and

colleagues (2006) and Smith and colleagues (2015)

reported that a right/left asymmetry of greater than

4cm on the ANT reach direction of the SEBT and

YBT respectively is associated with an increased risk

of a lower limb injury. While Gribble and colleagues

(2015) reported that a reduced ANT reach distance, in

combination with high BMI, is associated with

increased risk of lower limb injury. Schaefer and

colleagues (2012) reported that the minimally

detectable change for normalised reach distances

ranged from 4.9-5.4% for the different reach

directions, while Munro et al (2010) showed that the

smallest detectable difference ranged from 6.87-

8.15% of leg length depending on the reach direction.

While these thresholds provide guidance for

clinicians on the reach distances that can be

considered clinically relevant, they are population

specific, and only provide a small amount of

clinically relevant information. Another is the time

consuming nature of the YBT testing protocol, which

requires the individual to complete 4 practice trials

followed by 3 recorded trials in order to obtain a

reliable and repeatable score (Gribble et al., 2012).

An additional strategy which has been employed

to improve the accuracy and objectivity of the SEBT

and YBT is the use of marker based motion analysis

and force platform systems, providing information on

the control of movement and balance strategy

employed during the task (Coughlan et al., 2012,

Fullam et al., 2014, Doherty et al., 2015). However,

these methods have a number of major limitations,

restricting their application in clinical practice.

Firstly, the set-up is time intensive and requires

training, increasing the overall testing time and

limiting the number of clinicians with the experience

required to use the systems with efficacy. The

systems are expensive (> €100,000). They are

commonly not accessible outside of a laboratory

environment. The application of markers may hinder

natural movement during dynamic tasks (Bonnechère

et al., 2014, Ahmadi et al., 2014). The data recorded

from such systems also requires extensive processing

and analysis, which is time consuming.

In recent times, there has been a shift away from

traditional motion capture systems towards

unobtrusive systems that incorporate inertial

measurement units (IMUs) (Ahmadi et al., 2014).

Such systems address some of the aforementioned

limitations of traditional motion capture, as they

allow for inexpensive, accessible quantification of

human movement, in an unconstrained environment

(Giggins et al., 2013). These IMU systems have been

used in the objective quantification of a range of

activities, from static balance tasks (King et al., 2014,

Alberts et al., 2015, Furman et al., 2013), to dynamic

tasks such as the squat (O'Reilly et al., 2015) and

single leg squat (Whelan et al., 2015), walking

(Zijlstra and Hof, 2003, Yang et al., 2013) and

running (Lee et al., 2010). Early work investigating

the use of IMUs in balance assessment has shown that

a static balance assessment, instrumented with an

IMU mounted on the lumbar spine, was not as

effective as the traditional subjectively scored

assessment in identifying balance deficits post-

concussion (Furman et al., 2013). More recently,

King et al (2014) demonstrated improved levels of

sensitivity and specificity from the instrumented

balance error scoring system (BESS). It is likely that

the conflicting results are due to the different

quantified variables selected in the two studies. King

et al (2014) utilised root mean squared acceleration,

whereas Furman et al (2013) used sway path length,

which may not be capable of detecting subtle changes

in balance, when measured using a lumbar mounted

IMU. While these initial studies have demonstrated

the ability of IMUs to detect differences in static

balance between groups, there is a paucity of

evidence surrounding their ability to classify dynamic

balance performance during tasks such as the YBT.

Previous research has established the effect

various forms of muscle fatigue such as high intensity

intermittent exercise (Whyte et al., 2015), lower limb

functional exercises (Gribble et al., 2009) and isolated

muscle fatigue (Gribble and Hertel, 2004, Gribble et

al., 2009) have on dynamic balance. The combined

physiological effects of central and peripheral fatigue

mechanisms may result in changes to the integration

of sensorimotor information from the balance

subsystems, leading to decreased balance

performance. Therefore, this research sets out to

evaluate the ability of a single lumbar mounted IMU

to objectively quantify dynamic balance

performance. It is hypothesised that a single IMU

system has the potential to accurately differentiate the

three reach directions (ANT, PM and PL) and

distinguish “normal” and “abnormal” balance as

influenced by fatigue.

icSPORTS 2016 - 4th International Congress on Sport Sciences Research and Technology Support

2 METHODS

2.1 Subjects

Fifteen healthy participants aged between 18 and 40

(age: 23±4, weight: 67.5±8, height: 175±8, BMI:

22±2) who actively participate in sport were recruited

from the wider university population. Participants

were excluded from the study if they suffered from

chronic ankle instability, had sustained a lower limb

injury in the last six months, had vestibular, visual or

balance impairment, cardiovascular disease, any

neurological disease, or answered yes to any

questions on the PAR-Q (Warburton et al., 2011).

Ethical approval was obtained from the University

Human Research Ethics Committee and all

participants provided informed consent prior to

participating in the study.

2.2 Measures

2.2.1 Y-Balance Test

The YBT is an instrumented alternative to the SEBT,

capable of measuring dynamic postural control. The

YBT utilises three of the eight original SEBT reach

directions (ANT, PM and PL) and was developed in

order to provide a more objective reach distance

measurement, allowing for more accurate results,

collected in a less time consuming manner. The YBT

has been reported to demonstrate excellent intra-

tester (0.85-0.89) and inter-tester (0.97-1.00)

reliability (Plisky et al., 2009). The YBT requires

participants to stand on one leg, with their hands on

their hips, and slide a block as far as possible in the

three specified directions, with the contralateral limb,

before returning to bilateral stance. A fail is recorded

if the participant (1) uses the block for support, (2)

raises the stance heel from the platform, (3) makes

ground contact, (4) kicks the block forward to gain

extra distance or (5) removes one or both hands from

the hips during the task. The reach distances are then

normalised against the participant’s leg length using

the formula:

ℎ =

(ℎ) ⁄ (ℎ)

(1)

Leg length is obtained by the same investigator

for each study participant by measuring the distance

from the anterior-superior iliac spine to the most

distal aspect of the medial malleolus (Gribble and

Hertel, 2003). The average YBT reach distances

scores used for analysis were obtained by finding the

mean of the three normalised maximal YBT scores in

each reach direction. The YBT testing protocol was

developed and conducted according to the guidelines

outlined by Gribble and colleagues (2012).

2.2.2 Modified Wingate Test

The Wingate test is traditionally used in the

measurement of peak anaerobic power and anaerobic

capacity (Smith and Hill, 1991). A modified version

of the extended Wingate test protocol employed by

Carey and Richardson (2003) was used during this

study in order to maximally fatigue participants. The

modified test requires participants to cycle at

maximal intensity for 60 seconds, rather than the

traditional 30 second protocol. The cycle ergometer

resistance is set to 0.075 g·kg-1 as per previously

published methods (Kraemer et al., 2000, Laurent et

al., 2007). Prior to commencement of the Wingate

test, participants completed a 5-minute warm-up

cycling at 50-60 RPM, which included 3 x 5 second

sprints. Following the 5-minute warm-up,

participants commenced cycling at a cadence of 50-

60 RPM for 30 seconds. At the end of the 30 second

period, the 60 second Wingate test commenced, and

participants were encouraged to maintain a maximal

effort for the duration of the 60 seconds in order to

ensure maximal fatigue.

2.2.3 Inertial Measurement Unit

A Shimmer3 IMU (Shimmer, Dublin, Ireland) was

mounted at the level of the fourth lumbar vertebra

(Figure 1). The IMU was calibrated and configured to

stream tri-axial accelerometer (±2 g), gyroscope

(±500 ◦/s) and magnetometer (±1 gauss) data at 102.4

Hz via Bluetooth to an Android tablet, using Multi-

Shimmer sync software (Shimmer, Dublin, Ireland).

These data acquisition parameters were chosen based

on previous work carried out by our research group

Figure 1: Illustrates the mounting location of the Lumbar

IMU.

Objective Classiﬁcation of Dynamic Balance Using a Single Wearable Sensor

investigating the use of IMUs in the evaluation of

exercise technique during similar movements, such as

the single leg squat (O'Reilly et al., 2015).

2.3 Procedure

On arrival to the performance laboratory, the

experimental protocol was explained to the

participants, and individuals completed 4 practice

trials in each direction, on their dominant leg (all right

leg dominant). Leg dominance was obtained by

asking the participants which leg they would use to

kick a ball (Wilkins et al., 2004). Following

completion of the practice trials, each participant was

fitted with the IMU as described above. Participants

then completed three recorded YBT in each direction

(randomised order) on the dominant limb. This was

repeated at 0, 10 and 20-minutes in order to provide a

pre-fatigue baseline measurement of dynamic

balance. YBT maximal reach distances and IMU data

were collected for each YBT attempt. If a participant

failed to complete the test as described above, the

individual reach direction was repeated, and an

annotation was recorded in the IMU data to denote a

failed and repeated reach direction.

Following the baseline assessment, participants

completed the modified Wingate protocol in order to

elicit maximal anaerobic fatigue. Immediately

following the Wingate protocol, participants

completed the YBT to capture the reduced dynamic

balance performance elicited by maximal anaerobic

fatigue.

2.4 Data Analysis

Nine signals were collected from the IMU;

accelerometer x, y, z, gyroscope x, y, z and

magnetometer x, y, z. Data were analysed using

MATLAB (2012, The MathWorks, Natwick, USA).

To ensure the data analysed applied to each

participant’s movement and in order to eliminate

unwanted high-frequency noise, the nine signals were

low pass filtered with an 8

order Butterworth filter

with a 20Hz cut-off. Nine additional signals were then

calculated. The 3-D orientation of the IMU was

computed using the gradient descent algorithm

developed by (Madgwick et al., 2011). The resulting

W, X, Y and Z quaternion values were also converted

to pitch, roll and yaw signals. The pitch, roll and yaw

signals describe the inclination, measured in radians,

of each IMU in the sagittal, frontal and transverse

planes respectively. The magnitude of acceleration

was also computed using the vector magnitude of

accelerometer x, y, z. The magnitude of acceleration

describes the total acceleration of the IMU in any

direction. This is the sum of the magnitude of inertial

acceleration of the lumbar spine and acceleration due

to gravity. Additionally, the magnitude of rotational

velocity was computed using the vector magnitude of

the gyroscopes x, y and z.

Each reach direction from each completed

YBT was extracted from the IMU data and resampled

to a length of 1000 samples; this was undertaken to

minimise the influence of the speed of repetition

performance on signal feature calculations. It ensures

the computed features related to differences in

movement patterns and not the participant’s exercise

tempo. Descriptive features were computed in order

to characterise the pattern of each of the eighteen

signals as the YBT was completed. These features

were namely 'Mean', 'RMS', 'Standard Deviation',

'Kurtosis', 'Median', 'Skewness', ‘ Range', ‘Variance',

'Max', ‘Index of Max’, 'Min', ‘Index of Min’,

'Energy', '25th Percentile', '75th Percentile', 'Level

Crossing Rate' and' Fractal Dimension' (Katz and

George, 1985) . This resulted in 17 features for each

of the 18 available signals producing a total of 306

features. These features were then used to develop

and evaluate a classifier for the automated detection

of reach direction in the YBT and a separate classifier

for the detection of pre-fatigue or fatigued YBT

performance. The random-forests method was

employed to perform classification of reach direction

and for the detection of fatigued YBT performance

(Breiman, 2001). This technique was chosen as it has

been shown to produce superior accuracy, sensitivity

and specificity scores in analysing exercise technique

with IMUs in comparison to the Naïve-Bayes and

Radial-basis function network techniques (Mitchell et

al., 2015). Four hundred decision trees were used in

each random-forest classifier.

The quality of the exercise classification

system was established using leave-one-subject-out-

cross-validation (LOSOCV) and the random-forests

classifier with four hundred trees (Fushiki, 2011).

Each participant’s data corresponds to one fold of the

cross validation. At each fold, one participant’s data

is held out as test data while the random forests

classifier is trained with all other participants’ data.

The held out data is used to assess the classifier’s

ability to correctly categorise unseen data. The use of

LOSOCV ensures that there is no biasing of the

classifiers, meaning the test subjects data is

completely unseen by the classifier prior to testing.

Previous research by Taylor et al (2010) has shown

that not employing this method of testing can skew

results significantly. In our system, each individual

reach direction was classified.

icSPORTS 2016 - 4th International Congress on Sport Sciences Research and Technology Support

The scores used to measure the quality of

classification were total accuracy, average sensitivity

and average specificity. Accuracy is the number of

correctly classified observations divided by the total

number of observations completed; this is calculated

as the sum of the true positives (TP) and true

negatives (TN) divided by the sum of the true

positives, false positives (FP), true negatives and false

negatives (FN):

 =





(2)

The sensitivity and specificity were calculated for

each of the reach directions, sequentially treating

each label as the ‘positive’ class, and then the mean

and standard deviation across the five values was

taken. Sensitivity and specificity were computed

using formulas 3 and 4 below:

 =





(3)

 =





(4)

Sensitivity measures the effectiveness of a classifier

at identifying a desired label, while specificity

measures the classifiers ability to detect negative

labels. In the detection of fatigued balance, single

sensitivity and specificity scores were calculated,

treating pre-fatigued balance as the positive class and

fatigued balance as the negative class.

In reviewing the accuracy, sensitivity and

specificity scores produced by each classifier, 90% or

over was considered an excellent result, 80-89% was

considered a 'good' quality result, 60-79% was

considered a 'moderate' result and anything less than

59% was deemed a poor result. These values were

chosen by the authors after reviewing existing

literature on identifying exercises with IMUs. In

reviewing such literature, an existing accepted

standard for a good, moderate or poor classifier could

not be found. Therefore, the above system was agreed

on by the authors to facilitate interpretation of our

range of results.

3 RESULTS

ICC values for the three normalised reach directions

ranged from 0.976 – 0.986, indicating excellent test-

retest reliability across the pre-fatigue measures. Due

to the excellent ICC scores observed, the final pre-

fatigue measure was considered representative of the

pre-fatigue state, and was used in the comparison pre

and post-fatigue. The SEM ranged from 0.792-1.48

for the three YBT reach directions. The average

decrease in YBT reach distances following the fatigue

protocol was 2.65 ± 4.91 (ANT), 2.44 ± 3.06 (PM)

and 3.57 ± 4.27 (PL). Paired samples t-tests

demonstrated statistically significant differences (p <

0.05) between the final pre-fatigue YBT

measurement and the first post-fatigue measurement

in all reach directions (Table 1).

Table 1: Comparison of ICC, SEM and paired sample t-tests

for the YBT normalised Reach Direction for all three

directions. The level of significance was set to p < 0.05 and

statistically significant values were denoted with an*.

Reliability Analysis

Pre01, Pre02 & Pre03

Level of Significance

(p Values) for Post-hoc

Paired t-test

Reach

Distance

ICC SEM

Pre03 vs

Post01

ANT

0.986 0.792 0.049*

0.976 1.482 0.008*

0.978 1.134 0.006*

The classification algorithm for a single lumbar

mounted IMU was capable of differentiating the three

reach directions in the pre-fatigue baseline measures

with an accuracy of 97.80%, Sensitivity of 97.86 ±

0.89% and specificity of 98.90 ± 0.56%. Figure 2

presents a confusion matrix that illustrates the exact

percentage of reach direction repetitions that were

classified correctly and incorrectly. The rows

represent the actual reach direction recorded and the

columns show the classifier’s predicted reach

direction.

Figure 2: A confusion matrix showing multi-class

classification results for the three reach directions. The

percentage of reach direction attempts classified correctly

are marked in bold.

A single lumbar mounted IMU was capable of

discriminating pre and post-fatigue balance

performance with an accuracy of 61.90%-71.43%,

Objective Classiﬁcation of Dynamic Balance Using a Single Wearable Sensor

sensitivity of 61.90%-69.04% and specificity of

61.90%-78.57% depending on which reach direction

was chosen (Table 3). When all reach directions were

considered together, balance performance was

classified with an accuracy of 70.24%, sensitivity of

64.28% and specificity of 76.19%.

Table 2: The accuracy, sensitivity and specificity results of

the classification algorithm in the detection of baseline and

fatigued dynamic balance.

ANT PM PL

All

Directions

Accuracy

61.90 71.43 70.24 70.40

Sensitivity

61.90 69.04 61.90 64.28

Specificity

61.90 73.80 78.57 76.19

4 DISCUSSION

The purpose of this study was to determine if data

derived from a single lumbar mounted IMU is

capable of accurately differentiating the individual

reach directions of the YBT, and classifying pre and

post-fatigue dynamic balance performance.

The traditional normalised YBT reach distance

results presented demonstrate that the modified

Wingate protocol had a detrimental effect on the

participant’s dynamic balance. The ICC values for the

pre-fatigue baseline assessments presented suggest

that the normalised YBT reach distance scores for

each reach direction possess excellent test-retest

reliability. The paired sampled t-test results (Table 1)

demonstrate that there was a statistically significant

difference between the final pre-fatigue measurement

and the post-fatigue measurements, suggesting that

the fatigue intervention had a detrimental effect on

the YBT reach distances for all three reach directions.

Additionally, the SEM results for all reach directions

was smaller than the average decrease in reach

distance between the final pre-fatigue and the post-

fatigue measurement, indicating that the fatigue

intervention had a negative effect on reach distance

scores. When the SEM is viewed in conjunction with

the ICC, it allows us to be sure that any deviation from

the baseline is as a result of the fatiguing intervention,

and not a consequence of natural biological variation.

The results presented in this paper support

previously published ones indicating that dynamic

balance is heavily influenced by isolated muscle

fatigue (Gribble et al., 2004, Gribble et al., 2009),

lower limb fatiguing exercises (Gribble et al., 2009),

treadmill running (Wright et al., 2013) and high

intensity intermittent exercise protocols (Whyte et al.,

2015). Whyte and colleagues (2015) investigated the

effect of high intensity intermittent exercise on

dynamic balance, as measured by the SEBT. It was

reported that the percentage reduction in SEBT reach

distance, for the ANT, PM and PL directions were

marginally lower than those presented in our study.

Importantly, these differences may be a result of the

different fatiguing interventions influencing the

sensorimotor system to different extents (Whyte et

al., 2015). Additionally, different methods of

dynamic balance assessments were utilised in the two

studies. Whyte and colleagues (2005) used the SEBT,

whereas the YBT was implemented in our study,

potentially explaining the difference in the magnitude

of change (Coughlan et al., 2012). These past

findings, combined with the results from this study,

demonstrate that at a group level, the fatigue

intervention had a negative effect on dynamic

balance.

The IMU classification system was capable of

differentiating individual YBT reach directions with

excellent levels of accuracy, sensitivity and

specificity. The confusion matrix (Figure 2)

illustrates the percentage of the reach directions

classified correctly and incorrectly, indicating where

the confusion occurred. The ANT reach direction was

classified with the greatest success rate of 99%,

followed by PM (98%), and then PL (97%). These

results may be expected as the three reach directions

utilise different strategies to complete a maximal

reach. The ANT reach direction involves a single

planar movement which incorporates a single leg

squat type movement, while the individual reaches

outside of their base of support. In contrast, the PM

and PL movements involve multi-planar movements,

requiring the individual to enter a single leg squat,

while rotating at the pelvis and trunk in order to

achieve a maximal reach distance. Indeed, previous

research conducted by Kang and colleagues (2015)

investigating trunk, pelvic and lower limb kinematic

strategies utilised during the YBT. The results

presented by their group demonstrate that the ANT

reach direction requires a largely different strategy to

the PM and PL directions. The ANT direction

requires minimal trunk and pelvic kinematic

movements, with 1° trunk extension, 4° trunk

ipsilateral flexion, 9° anterior pelvic tilt, and 1° of

pelvic ipsilateral rotation. The majority of the

movement strategies stem from sagittal plane

movements at the hip (30° flexion), knee (62°

flexion) and ankle (39° dorsiflexion). In contrast, the

PM and PL reach directions require large changes in

trunk and pelvic kinematics, with the PM reach

direction requiring 43° trunk flexion, 21° trunk

icSPORTS 2016 - 4th International Congress on Sport Sciences Research and Technology Support

ipsilateral flexion, 39° anterior pelvic tilt and 0° of

pelvic contralateral rotation, and the PL reach

direction requiring 48° trunk flexion, 16° trunk

contralateral flexion, 38° anterior pelvic tilt and 11°

of pelvic contralateral rotation. These results clarify

the similarities and differences between the

movement strategies utilised during each reach

direction, contextualising how the classification

algorithm was capable of classifying the individual

reach directions with such high degrees of accuracy.

The YBT reach direction classification results

presented in this study are in line with previously

published IMU exercise identification results which

range between 85-95% depending on the exercises

and IMU setups (Giggins et al., 2014, Pernek et al.,

2015, Chang et al., 2007). Giggins and colleagues

(2014) demonstrated that a single IMU location could

differentiate between seven basic rehabilitation

exercises with an accuracy of between 93-95%

depending on the mounting location. Additionally,

Pernek and colleagues (2015) reported that a single

IMU system can correctly identity upper limb free

weight exercises with 85% accuracy. This is

significant as the excellent levels of accuracy (98%)

presented in this study were achieved using just 252

observations. In contrast, the exercise classification

work presented above used a greater number of

observations to train the classifiers, with Giggins et al

(2014) utilising 3940 observations and Pernek et al

(2015) using 440 observations per exercise.

The lumbar IMU classification algorithm was

capable of differentiating dynamic balance

performance, as influence by fatigue, with and

accuracy of between 62% and 71%, depending on the

reach direction (Table 3). The PM reach direction

demonstrated the highest classification accuracy

(72%), followed by the PL (70%) and then the ANT

(62%) reach direction. When all reach directions were

considered together, the classification algorithm was

able to differentiate normal and abnormal balance

with an accuracy of 70%.

These results would be expected, because as we

previously discussed above, the three reach directions

require different levels of movement strategy

complexity. The ANT reach direction presented with

the lowest degree of classification accuracy. The

ANT reach direction is the least complex movement,

predominantly requires sagittal plane movement of

the stance limb (Kang et al., 2015). It may be that the

ANT reach direction movement does not sufficiently

challenge the sensorimotor system in all individuals

to elicit a balance deficit large enough to be

consistently detected by the lumbar mounted IMU. In

contrast, the higher degree of accuracy observed in

the detection of abnormal balance during the PM and

PL reach directions are expected as these movements

require the individual to implement a more complex

multi-planar movement strategy. Both the PM and PL

reach directions require the individual to reach

outside of their base of support while utilising their

trunk as a mobile counter-lever, involving a

combination of complex multi-planar movements

occurring at the trunk, pelvis, hips, knee and ankle

(Kang et al., 2015, Fullam et al., 2014, Doherty et al.,

2016). This complex multi-planar movement may

more comprehensively challenge the integration of

the sensorimotor subsystems, resulting in more

pronounced strategy changes following the

introduction of a balance deficit, thus leading to

differences in the IMU data.

To the best of the authors knowledge this is the

first research study that has attempted to classify

dynamic balance performance using an IMU.

Previous research has investigated the ability of

single and multiple IMUs to detect technique

breakdown during compound lower limb exercises

such as the squat (O'Reilly et al., 2015) and single leg

squat (Whelan et al., 2015). Lower limb exercises

such as the single leg squat incorporate many of the

requirements involved during the YBT reach

directions, such as maintaining one’s balance while

executing a dynamic task on a single leg. Whelan and

colleagues (2015) reported that a single lumbar based

IMU mounted on the lumbar spine was capable of

classifying correct and incorrect single leg squat

technique with an accuracy of 92%. While the

classification accuracy presented by Whelan and

colleagues is higher than that of the YBT balance

performance classification presented in our study, it

is probable that the YBT classification performance

would be greatly improved by increasing the number

of observations used to train and test the classifier.

The results presented in this paper demonstrate

the potential of a single lumbar mounted IMU to

automatically classify YBT reach direction and

balance performance. This lays the groundwork for

the development of an accurate dynamic balance

performance classification system that can provide

accessible, in depth, clinically relevant information,

surrounding an individual’s dynamic balance, outside

of the constraints of a laboratory. Future work will

allow us to detect changes in movement and balance

strategy during the YBT, characterising the dynamic

balance defects. This would provide clinicians with

more in depth information which can be used to

comprehensively and objectively assess the

integration of the sensorimotor subsystems, in an

accessible manner. This has the potential to provide

Objective Classiﬁcation of Dynamic Balance Using a Single Wearable Sensor

information in areas such as lower limb injuries,

identification of lower limb injury risk factors,

assessment of the motor function domain post-

concussion, as well as balance training in strength and

conditioning and rehabilitation.

A number of limitations to the study must be

acknowledged. The sample size and resultant number

of observations that could be used to train and

evaluate the classification algorithms were relatively

small, potentially resulting in decreased levels of

accuracy. It can be expected that as the number of

participants and observations are increased, there will

be a resultant increase in the accuracy of the balance

performance identification. Secondly, no gold

standard motion capture system was employed in this

study. However, YBT reach directions are commonly

accepted as the standard in clinical balance

assessments, and each participant was educated and

supervised by a Chartered Physiotherapist throughout

the duration of the study.

Extensive future work is required to improve the

classification results presented in this paper. Firstly, a

greater number of participants is required to increase

the size of the data set in order to establish a

normative dataset. Additionally, a classification

system with improved accuracy, sensitivity and

specificity will be developed. This may be achieved

through investigating the effectiveness of a single

IMU located at different anatomical positions,

collecting a larger data set to allow for more training

data for the classification algorithms and the

identification of new features to input into the

classifiers which enable further distinction of normal

and abnormal balance. Novel classification

techniques for IMU data may also be employed such

as the application of deep learning on the data. This

will also require a larger data set to be collected.

5 CONCLUSION

To conclude, the results presented in this paper

demonstrate that a lumbar mounted IMU is capable

of accurately distinguishing the three YBT reach

directions, as well as classifying balance performance

as influenced by a maximal anaerobic fatigue. This

work lays the foundations for the development of a

single IMU system, that can accurately differentiate

the YBT reach directions, as well as detect changes in

balance strategy, characterising and classifying

dynamic balance performance.

ACKNOWLEDGMENTS

This study was funded by the Science Foundation of

Ireland (12/RC/2289).

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