Measurement of Heel-Rise Test Results using a Mobile Device

Ivan Miguel Pires

1,2

, Márcia Andrade

, Nuno M. Garcia

1,3

Rute Crisóstomo

4,5,6

and Francisco Florez-Revuelta

Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal

Altranportugal, Lisbon, Portugal

ECATI, Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal

Laboratório de Biomecânica e Morfologia Funcional (LBMF), Faculdade de Motricidade Humana,

Universidade de Lisboa, Lisbon, Portugal

Centro Interdisciplinar Para o Estudo da Performance Humana (CIPER), Faculdade de Motricidade Humana,

Universidade de Lisboa, Lisbon, Portugal

Instituto Politécnico de Castelo Branco, Escola Superior de Saúde Dr. Lopes Dias, Castelo Branco, Portugal

Faculty of Science, Engineering and Computing, Kingston University, Kingston upon Thames, U.K.

Keywords: Mobile Application, Heel-Rise Test, Measurement of Fatigue, Physiotherapy, Physical Exercise, Heel,

Android, iOS.

Abstract: The heel-rise test measures the ability to perform eccentric and concentric muscle actions of the plantar

flexor muscles with unilaterally consecutive elevations of the heel. This test is easy to administer and is a

non-invasive test for strength and endurance of the calf muscle. Despite the part that this test has proven

reliability, it has been difficult to measure its results, as it depends on the subjectivity of the examiner and

conditions of the place of testing. This research consists in the design and development of a mobile

application for the heel-rise test. The algorithm makes use of sensors to measure the exercise, implementing

the rules to detect a pattern of the accelerometry sensors during the test. The heel-rise test consists in

detecting periodically the number of correct exercise repetitions. Then, physiotherapists can use this heel-

rise automatic test in their examinations.

1 INTRODUCTION

Technology is nowadays widely used in people’s

daily life to support their work and improve their

quality of life. During the last years, the use of

smartphones or portable devices has increased

(eMarketer, 2014), connecting people to the digital

world anywhere at anytime. Most of these devices

incorporate embedded sensors that capture signals at

real-time and that can be used to improve health,

detect falls and monitor activities of daily living.

These technologies and services establish what is

usually named as Ambient Assisted Living.

Mobile applications are software components

with less functionality than desktop software and

that are specially designed for a specific task, due to

the fact that mobile devices have some limitations

(Biel et al., 2010). Mobile applications have an

important impact on society, but their use depends

on several factors, such as screen resolution,

hardware limitations, expensive data usage,

connectivity issues, and limited interaction

possibilities. (Islam et al., 2010).

Sensors can help to identify physical activities

and their consequent effects such as fatigue, cardiac

arrhythmia, among others (Moran and Marshall,

2006). On the other hand, the regular physical

activity is important to reduce the risk of some

diseases, such as obesity, cardiovascular accidents,

diabetes, Parkinson, chronic diseases… (Thompson

et al., 2003). Mobile devices acquire data related to

the activities performed and analyse that data to

identify the type of activity performed by the user.

The activity detection consists of different phases,

such as pre-processing, minimization of noise, and

classification of the collected data, related to some

task, using statistical methods or other methods,

such as machine learning or pattern recognition.

Usually, the equipment must be positioned in a

specific location of the user’s body for better

Miguel Pires I., Andrade M., M. Garcia N., Crisóstomo R. and Florez-Revuelta F..

Measurement of Heel-Rise Test Results using a Mobile Device.

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

recognition. Adequate automatic methods allow, in

some cases, monitoring and treating health problems

easier and more accurate than manual methods.

However, for an acceptance by the medical

communities, these technologies must be medically

validated (Barton, 2012, Brusco, 2012).

In physiotherapy, the use of some tests to assess

health dysfunction and physiological parameters is

common. For instance, the Heel-Rise Test is used to

detect muscular fatigue. The Heel-Rise Test consists

in the ability to perform eccentric and consecutive

concentric actions of the ankle plantar flexor

muscles with maximum heel elevation possible

(Sman et al., 2014, Yocum et al., 2010). Currently,

due to the inexistence of automatic methods using

technological devices, the exercise related to the

Heel-Rise Test needs the presence of an examiner

that observes and validates the exercise performed

by the patient. The measurement of the heel-rise test

needs external devices to control the time between

the repetitions, for example a metronome or other

counting device, and needs a previous evaluation

and preparation of some physical conditions.

This work presents the design of an automatic

algorithm for the Heel-Rise Test and its

implementation in a mobile application. The

developed mobile application makes use of pattern

recognition techniques to measure the number of

correct repetitions performed during the test with the

mobile device attached to the user’s waist. This test

is easy, inexpensive and non-invasive. The

development of this mobile application has a

validated algorithm. The validation of the algorithm

consists in the readings from the mobile device’s

accelerometer comparing the results the readings

from an external tri-axial accelerometer and a force

platform.

This study uses data collected with the

collaboration of the Assisted Living computing and

Telecommunications Laboratory (ALLab), which is

part of the Institute of Telecommunications, located

in the University of Beira Interior, in Covilhã, and

the Escola Superior de Saúde Dr. Lopes Dias, which

belongs to the Polytechnic Institute of Castelo

Branco.

This paper is organized as follows: in section 2,

some research studies, carried out by other authors,

about the Heel-Rise Test are presented. Section 3

presents the details about the automatic method for

Heel-Rise Test. In section 4, the current results

about the mobile application and algorithm created

are discussed. The conclusions about this study are

presented in the section 5.

2 RELATED WORK

Physiotherapists, use the Heel-Rise Test (Veilleux et

al., 2012a) to identify muscle fatigue by studying the

number of repetitions of heel-rise movements until

the user achieves muscular fatigue This test allows

them to relate the results with specific health

problems.

The Heel-Rise Test can be used to assess muscle

strength, endurance, fatigue, and balance and

equilibrium of the whole body (Sole et al., 2010).

The protocol to be applied in this test is an

elevation as high as possible with knee test member

in extension (Segura-Orti and Martinez-Olmos,

2011) with people’s barefoot, every two seconds

(Silbernagel et al., 2010), controlled with a

metronome. The contralateral foot is kept just above

the ground (Segura-Orti and Martinez-Olmos, 2011).

Before the test, the examiner will do 5 repetitions for

exemplification and then the participants will try to

maintain balance on one foot, touching the wall with

his fingertips, flexed arms to shoulder height to

avoid leaning hard against the wall, and, finally, they

will perform transfers weight (Österberg et al., 1998,

Segura-Orti and Martinez-Olmos, 2011, Yocum et

al., 2010). Then, the participants make 2 repetitions

to identify the maximum elevation obtained during

these repetitions, followed by a 5-minute-break to

start the test (Yocum et al., 2010). In the training

repetitions, the maximum height achieved by the

participant will be marked on the wall above the

participant's head, giving the objective of reaching

the mark in each repetition (Yocum et al., 2010).

Thus, the measurement of the Heel-Rise Test

consists of the number of repetitions that the

exercise is correctly performed (Yocum et al., 2010).

So, according to (Segura-Orti and Martinez-Olmos,

2011, Yocum et al., 2010), the test will be suspended

when:

 The participant says to stop;

 The force of the knee flexion, according to

the observation of the examiner;

 The member is pushing against a wall,

according to the observation of the

examiner;

 The participant fails to reach 50% of height

marked on the wall;

 The contralateral limb touches the ground

or misses the rhythm of the metronome on

2 consecutive repetitions.

This test has some variations, such as the single

heel-rise test and the double heel-rise test. The

Single-Leg Heel-Rise Test may also be a useful

performance measure regarding footwear or other

PhyCS2015-DoctoralConsortium

therapeutic interventions, as it incorporates elements

of both the late stance phase when sensory input is

gathered and the more challenging heel-off phase of

the gait.

It is also important to assess the calf-muscle

performance (Crossley et al., 2007). During this test,

participants maximally raised their heel off the floor,

returning to the floor at a rate of 1 heel rise every 2

seconds. Five practice heel rises were performed

followed by a 1-minute rest prior to the actual test.

The participant was asked to perform as many heel

rises as possible. Repetitions were counted each time

the foot went back to the ground. The test was

terminated if the participant leaned forward with a

force greater than 2% of the body weight, the

participant’s knee flexed, or the participant failed the

time with contact to the ground during three

consecutive heel rises.

Chen et al. (Chen et al., 2012) created a three-

dimensional musculoskeletal finite element model of

the foot to quantify the precise role of the

gastrocnemius–soleus complex in terms of

biomechanical response of the foot, which

corresponds to a muscle-demanding posture during

heel rise, with simulated activation of major

extrinsic plantar flexors. This model can measure the

force necessary to do the exercise and the posture

during the exercise. In (Dutta et al., 2012),

electromyography is employed in the heel-rise test,

measuring the muscular fatigue with good accuracy.

The heel-rise test rates ankle plantar flexor

strength from 0 to 5 according to the number of heel

rises that the subject is able to complete (Caudill et

al., 2010). Performance of 20 repetitions

corresponds to a maximum score of 5, whereas a

score of 2 corresponds to full range of antigravity

ankle plantar flexion motion. Therefore, the

recommended number of heel-rise repetitions for

adult people is 25 (Lunsford and Perry, 1995, Gefen

et al., 2002), as the standard for a score of 5, until

the person becomes fatigued. In (Lunsford and

Perry, 1995) the average number of standing heel-

rise repetitions completed was 28.

The results of Heel-Rise Test consist in the

number of repetitions of the exercise. Only 25

consecutive repetitions are recommended (Bennett et

al., 2012). The test has different stop criteria, such as

the occurrence of the knee flexion, the elbow or

wrist flexion during three times, the failure to

contact the string for three consecutive repetitions,

or the inability to continue due to fatigue (Bennett et

al., 2012).

The authors of (van Uden et al., 2005) verified

that patients with severe chronic venous

insufficiency had a significantly lower number of

heel rises, indicating decreased calf muscle

endurance.

Besides, results of the test can be influenced by

using a medial hind foot wedge in the barefoot

condition, which causes a decrease of the

performance during the task (Sole et al., 2010). The

rheumatoid arthritis on foot function also affects the

delay of heel-rise, causing the decreasing of the

performance too (Turner et al., 2006).

Other studies were performed to test the

reliability of the Heel-Rise Test and obtain the

muscle endurance in patients with chronic diseases

(Pieper et al., 2008), cerebral palsy (Russell et al.,

2007), lateralized neuromuscular perturbation

(Vuillerme and Boisgontier, 2010), Achilles Tendon

Rupture (Silbernagel et al., 2010), hemodialysis

(Segura-Orti and Martinez-Olmos, 2011), peripheral

arterial occlusive disease (Monteiro et al., 2013),

and other diseases (Veilleux et al., 2012b).

Sensors can help in the measurement of the

Heel-Rise Test results, e.g. measuring the angle and

knee motions with kinetic measuring devices

(Hastings et al., 2013, Haber, 2004, Sman et al.,

2014) and electromyograms (Kasahara et al., 2007),

showing that the repeated motion method estimates

muscle endurance rather than the muscle power. The

accelerometry signal is useful to detect the pattern of

Heel-Rise Test and validate the exercise, as showed

in (Schmid et al., 2011, Österberg et al., 1998), with

good accuracy, without interaction of the examiner.

3 AUTOMATIC HEEL-RISE TEST

The proposed solution consists in a mobile

application to perform the Heel-Rise Test with good

accuracy. The application uses an accelerometer

embedded in a mobile device and shows the

processed results of the Heel-Rise Test.

3.1 Detection of Activities using

Accelerometers

The accelerometer is an instrument capable of

capturing the instant acceleration of a subject’s body

or an object. This data can later be processed to

identify activities of daily living using different

techniques, such as pattern recognition or machine

learning techniques. The captured data needs to be

pre-processed, smoothed (reducing the noise) and

classified.

Different authors have made studies about the

relation of the accelerometry data and the detection

MeasurementofHeel-RiseTestResultsusingaMobileDevice

of daily activities. In (Chernbumroong et al., 2011)

neural networks and decision trees are used to detect

the daily activities using only a wrist-worn

accelerometer with an accuracy of 94.13%.

In (Andreas et al., 2014) both accelerometers and

gyroscopes are used in different body locations for

activity recognition. Best results were obtained using

a single sensor in the hand and the worst ones when

located in the arm.

Fulk et al. (Fulk et al., 2012) proposed a novel

shoe-based sensor system to identify different

functional postures in people with stroke disease,

that consists of five force sensitive resistors built

into a flexible insole and an accelerometer on the

back of the shoe. The system measures the pressure

and accelerometer data and sends the data via

Bluetooth to a smartphone, identifying sitting,

standing and walking activities with an accuracy,

precision and recall greater than 95% (Fulk et al.,

2012).

3.2 Method Implemented

The mobile application receives the values of the

outputs (X, Y and Z) of the tri-axial embedded

accelerometer and calculates the magnitude of the

vector at every moment.

The implemented algorithm incorporates the

conditions of the regular test (supervised by an

examiner) and some rules specifically obtained by

the accelerometry signal. In the mobile device’s

accelerometer, the values of the outputs and the

values of the magnitude of vectors are in m/s

A correct exercise should present variations of

the acceleration similar to the example in figure 1.

Figure 1: Representation of the acceleration values of a

repetition of the exercise during the Heel-Rise Test. The

vertical axis represents the values of the magnitudes of the

vectors of all the outputs acquired by the accelerometer

(values in m/s

). The horizontal axis represents time in

milliseconds.

The creation of the algorithm requires a learning

phase in order to identify the relevant features. The

collection of data starts with a beep signal that

indicates the time to start. After the beep signal, the

user has 2 seconds to perform the exercise that is

part of the Heel-Rise Test. This test consists in a

repetition of consecutive maximum heel elevations

(Sman et al., 2014, Yocum et al., 2010), performing

eccentric and concentric muscle actions of the

plantar flexor muscles. This algorithm consists in the

validation of each repetition, stopping the algorithm

and playing an acoustic signal after the user fails the

exercise in 2 consecutive repetitions.

The accelerometer data is collected at time

intervals of 2 seconds. After this time, the algorithm

implemented in the mobile application evaluates the

collected data to verify the validity of the exercise.

The validation is done with a recursive algorithm for

peak detection, to detect the maximum peak

acceleration values and the minimum depressive

acceleration values.

The exercise is validated with a sequence of

rules, which are:

1. Collect the data of the smartphone

accelerometer, after beep signal (figure 2-

A);

2. Verify if the value of the point at the start

of data collection is comprised between

Earth’s gravity with a margin of the

correction value (9.81±1 m/s

). If the value

is out of this range the algorithm assign a

fail. This comparison was performed to

verify if people is in movement, when they

should start an repetition of the exercise of

Heel-Rise Test;

3. Verify if the value of the point at the end of

data collection is comprised between

Earth’s gravity with a margin of the

correction value (9.81±1 m/s

). If the value

is out of this range the algorithm assign a

fail. This comparison was performed to

verify if people is in movement, when they

should has in a static position, because they

ended an repetition of the exercise of Heel-

Rise Test;

4. Detect the maximum peak acceleration

values, recursively, until obtain only one

point of maximum peak is obtained (figure

2-D);

5. Detect the minimum depressive

acceleration values, recursively, until obtain

only one point of minimum depression

(figure 2-C);

6. Verify if during the time interval exists

points higher or smaller than the values

comprised between Earth’s gravity with a

margin of the correction value (9.81±1

m/s

). If all peaks are in the range, the

PhyCS2015-DoctoralConsortium

algorithm assign a fail;

7. Verify if the instant of time related to the

point of maximum peak (figure 2 - D) is

higher than the instant of time related to the

point of minimum depression (figure 2 - C).

If the algorithm doesn’t pass in this

condition, a fail is assigned;

8. Verify if the time interval between the

instant of beep signal (figure 2 - A) and the

instant of landing (figure 2 - D) is smaller

than 2 seconds. If the algorithm doesn’t

pass in this condition, a fail is assigned.

Figure 2: Representation of the acceleration values (in

m/s

) of a repetition of the exercise of Heel-Rise Test with

points marked in the graph. The point A represents the

time instant (in milliseconds) of the beep signal. The point

B represents the time instant (in milliseconds) of take-off.

The point C represents the time instant (in milliseconds) of

peak. The point D represents the time instant of landing.

The validation is done on each repetition of the

exercise. During this sequence of analysis, the

algorithm stops when 2 consecutive failed

repetitions of the exercise of Heel-Rise Test are

verified.

For further analysis, various time intervals are

identified, such as the time of preparation, time of

take-off and the time of landing (figure 3). Each

repetition of the exercise consists of the sequence of

these time intervals. The time of preparation is the

time interval between the instant of the beep signal

and the instant when the user starts the movement.

The time of take-off is the time interval between the

instant when the user starts the movement and the

instant when the user ends the heel elevation,

corresponding to the rise time. The time of landing

is the time interval between the instant when the user

ends the heel elevation and the instant when the heel

of the user returns to the ground.

During the repetitions of the exercise, the time of

preparation and the time of take-off increase, and

this increases the probability of fails during the

Heel-Rise Test. On the other hand, the time of

landing decreases throughout time, because the user,

due to fatigue, falls faster to the ground than at the

beginning of the test.

Figure 3. Representation of the acceleration values (in

m/s

) of a repetition of the exercise of Heel-Rise Test with

time intervals marked in the graph. The time interval (in

milliseconds) between points A and B is considered the

time of preparation. The time interval (in milliseconds)

between points B and C is considered the time of take-off.

The time interval (in milliseconds) between points C and

D is considered the time of landing.

When the test is finished, the results are showed

to the user and saved for future analysis. The results

consists of the time elapsed when the algorithm is

stopped, the number of repetitions of the exercise

and the time that the user has been active. The

algorithm shows more reliability and accuracy than

the regular method with the check of the examiner,

because, with an accelerometer, it is possible to

detect movements impossible to detect by an

examiner observing the exercise, e.g., during the

tests the person moves slightly against the wall and

the examiner doesn’t see, but the mobile application

detects this and considers it as a fail, invalidating the

exercise.

3.3 Mobile Application

The mobile application for this work was developed

for Android and iOS operating systems, using hybrid

development technologies. The platform used for the

development, named Cordova (developed by

Apache) (Foundation, 2014), allows to develop the

application using Web programming languages,

such as HTML, JavaScript and XML, which works

as a native application running in a WebView using

native components to access to the sensors data.

One of the requirements is that the application

should have a friendly interface and good usability.

The measurement is activated with an easy selection

using a big button in the center of the screen, which

also shows the state of the capture.

The screens of the mobile application developed

are showed in Figure 4.

MeasurementofHeel-RiseTestResultsusingaMobileDevice

(a) (b) (c)

(d) (e)

Figure 4: Interfaces of the mobile application.

When the application starts, the state of the

mobile application is “Standby”, as showed in

Figure 4a. When the user press the start button,

showed in figure 4a, the status of the mobile

application switches to “Waiting” (figure 4b), and

this state remains while the user positions the mobile

device on the waist, before the beep signal. After

this time, the capture and processing of the

accelerometer signal will start, changing to the

“Capturing” state (figure 4c). During this time, the

user must perform the exercises. During the test, the

number of the repetitions of the exercise performed

will be showed. A beep signal is issued every 2

seconds to control the time when the user must

perform the repetitions of the exercise.

In the settings screen, showed in figure 4d, the

user is able to change the language of the mobile

application, enable or disable the sound and change

the time interval that elapses before the capture is

started (after pressing the start button). The

languages available are English, Portuguese, Spanish

and French. The labels related to the state of the

capture switch between “Standby”, “Waiting”,

“Capturing” and the number of repetitions of the

exercise performed.

In figure 4e, the results of the tests performed are

showed and the user can remove the history data of

the Heel-Rise Tests performed.

4 DISCUSSION

The Heel-Rise Test has easy administration and

inexpensive costs. It is used to evaluate the muscle

fatigue with simple movements, performing

eccentric and concentric muscle actions of the

plantar flexor muscles with consecutive elevations

maximum heel (Sman et al., 2014, Yocum et al.,

2010). This research was done in collaboration

between the Assisted Living Computing and

Telecommunications Laboratory (ALLab), at

Institute of Telecommunications, at the University of

Beira Interior (UBI), in Covilhã, and Escola

Superior de Saúde Dr. Lopes Dias (ESALD), at the

Polytechnic Institute of Castelo Branco.

The experiments of this study were done in

several phases. These were: a) the initial phase to

identify and validate the accelerometry signal related

to the heel-rise movement, and b) a second phase to

validate the mobile application.

In the beginning many hypotheses were studied

about the measurement of the heel-rise test using

sensors. For initial measurements and to verify the

reliability of the accelerometer signal to measure the

results of the heel-rise test, the participants were

tested with a external tri-axial accelerometer,

attached to the participant’s waist, and a pressure

sensor attached to the heel, connected to a bioPlux

device (PLUX, 2010), that sends, over Bluetooth,

the collected data to a computer device. These

sensors were used at the same time that the

accelerometer signal was captured with the

smartphone, attached to the waist. This allowed to

compare the values obtained by external sensors and

the accelerometer sensor embedded in a smartphone.

Data that was collected by external sensors has a

frequency of 1kHz. With these experiments, it was

verified that when the value of the magnitude vector

is higher than Earth’s gravity it corresponds to the

time interval that the heel is not on the ground, i.e.

time of heel-rise repetition, as showed in figure 5.

Figure 5: Data collected by a pressure sensor and a tri-

axial accelerometer related to a heel-rise repetition.

In continuation, the participants did a few

experiences to try to identify the pattern of the

accelerometry data related to an exercise of the

Heel-Rise Test. The manual measurement of the

experiments for this study was performed using a

metronome. The participants did the exercises with a

PhyCS2015-DoctoralConsortium

mobile device placed on the waist to collect the

accelerometry data. The frequency of the collection

of the accelerometer data by the mobile application

was not possible to manage, but the application

collects the data as fast as possible, approximately

every 10 milliseconds. The accelerometry data was

compared between the experiments to identify a

pattern of a heel-rise repetition, which is showed in

figure 6.

Figure 6: Smartphone Accelerometer Data related to a

heel-rise repetition.

In the mobile device, an application previously

developed by the author of this document, named as

iAccelerometer Capture (Google, 2014a, Apple,

2014a), was used for collecting the accelerometer

data and save it to text files.

Then, the automatic algorithm was designed and

implemented as a mobile application.

In the last phase, this mobile application, named

as iFatigue Detector (Apple, 2014b, Google, 2014b),

was tested in a real environment with the same

participants of the start phase. During these tests,

some issues appeared related to the measurement of

the Heel-Rise Test. These were:

 Sometimes the repetition is considered valid

by the examiner, but with the accelerometer

signal it is invalidated. This was because

there was a movement to the front instead of

a correct heel-rise exercise with vertical

movement;

 Sometimes the person continues in movement

after performing the repetition and the mobile

application invalidates the exercise; and

 Sometimes, during the movement, the person

has some vibrations and invalidates the

accelerometry signal.

In the mobile application, the collected data is

processed and validated after each two seconds and

before the next acoustic signal, applying the

validation rules. The time of two seconds between

heel-rise has a variation of some milliseconds that

corresponds to the delay of the data processing and

this is evaluated to accept or reject the exercise and

continue or stop the test. Thus, sometimes the

algorithm, implemented in the mobile application,

stops the test before the examiner, detecting the

irregular data in the accelerometry sensor. On the

other hand, the mobile application shows to be more

reliable than the measurement of the examiner,

because it detects some variations not visible to the

examiner.

During the experiments, another problem found

was where to locate the mobile device at it can

vibrate or move during the exercise. It must be

attached in a fixed position related to the user’s

body, preferably on people’s waist.

In general, the application implements the

algorithm that has been proved correct, but the

mobile application needs a more extensive

validation with different people, increasing the range

of ages, heights, physical conditions, lifestyle and

health problems. As a futures work, the validation of

the mobile application will be done in order to prove

the acceptance of the mobile application by the

medical communities.

5 CONCLUSIONS

In recent years, the use of mobile equipment has

increased. These equipments have embedded many

sensors, such as accelerometer, gyroscope and

proximity sensors, built-in GPS receiver, camera and

others, including possibility to connect to other

sensors via Bluetooth. The two most used platforms

in mobile devices, such as smartphones, tablets,

among others, are Android operating system (owned

by Google) and iOS operating system (owned by

Apple),.

The Heel-Rise Test consists in an easy,

inexpensive and non-invasive test, used to evaluate

the muscle fatigue with simple movements,

performing eccentric and concentric muscle actions

of the plantar flexor muscles with consecutive

maximum heel elevations. This test is able to detect

some health dysfunctions, but needs preparation of

the place and the examiner needs extra attention in

order to validate test performance, making use of a

metronome to measure the time intervals between

heel-rise repetitions.

However, the heel-rise test can be measured

using accelerometry and electromyography sensors

embedded in a mobile device, which in most of

cases are more reliable than the conventional

method. During the test, the mobile device should be

in a static position related to the body. During this

work, a mobile application was created to measure

the heel-rise test, which identifies a performance

MeasurementofHeel-RiseTestResultsusingaMobileDevice

pattern and implements the validation rules related

to the accelerometer data and the conventional rules.

For the creation and tests of the mobile application,

collaboration between ESALD and ALLab was

established, which allowed improving the relation

between the experts in the health and the computer

science areas.

The mobile application created in this research

work is multiplatform. The pre-version created

during this work is available for iOS operation

system (Apple, 2014b) and Android operating

system (Google, 2014b). This is a pre-version

because this mobile application needs an exhaustive

validation in order to be accepted by the medical

communities.

Besides, other tests employed in the

physiotherapy area can also make use of

accelerometry data and other embedded sensors, in

order to identify some other health problems.

In conclusion, these tests may have more accuracy

than the human eyes to examine the exercises, being

the technology very useful to offer support in this

type of measurements.

ACKNOWLEDGMENT

This work was supported by FCT projectPEst-

OE/EEI/L A0008/2013 (Este trabalho foi suportado

pelo projecto FCT PEst-OE/EEI/LA0008/2013).

The authors would also like to acknowledge the

contribution of the COST Action IC1303 –

AAPELE – Architectures, Algorithms and Protocols

for Enhanced Living Environments.

REFERENCES

Andreas, B., Ulf, B. & Bernt, S. 2014. A Tutorial On

Human Activity Recognition Using Body-Worn

Inertial Sensors. Acm Comput. Surv. %@ 0360-0300,

46, 1-33. Doi: 10.1145/2499621.

Apple. 2014a. Iaccelerometer Capture On The App Store

On Itunes [Online]. Apple. Available:

Https://Itunes.Apple.Com/Us/App/Iaccelerometer-

Capture/Id860264551?Mt=8 [Accessed May 23rd

2014].

Apple. 2014b. Ifatigue Detector On The App Store On

Itunes [Online]. Apple. Available: Https://

Itunes.Apple.Com/Ca/App/Ifatigue-Detector/

Id875088059?Mt=8 [Accessed May 23rd 2014].

Barton, A. J. 2012. The Regulation Of Mobile Health

Applications. Bmc Med, 10, 46. Doi: 10.1186/1741-

7015-10-46.

Bennett, J. E., Reinking, M. F. & Rauh, M. J. 2012. The

Relationship Between Isotonic Plantar Flexor

Endurance, Navicular Drop, And Exercise-Related

Leg Pain In A Cohort Of Collegiate Cross-Country

Runners,

Biel, B., Grill, T. & Gruhn, V. 2010. Exploring The

Benefits Of The Combination Of A Software

Architecture Analysis And A Usability Evaluation Of

A Mobile Application. Journal Of Systems And

Software, 83, 2031-2044. Doi:

10.1016/J.Jss.2010.03.079.

Brusco, J. M. 2012. Mobile Health Application

Regulations And Compliance Review. Aorn J, 95,

391-4. Doi: 10.1016/J.Aorn.2011.12.010.

Caudill, A., Flanagan, A., Hassani, S., Graf, A.,

Bajorunaite, R., Harris, G. & Smith, P. 2010. Ankle

Strength And Functional Limitations In Children And

Adolescents With Type I Osteogenesis Imperfecta.

Pediatr Phys Ther, 22, 288-95. Doi:

10.1097/Pep.0b013e3181ea8b8d.

Chen, W. M., Park, J., Park, S. B., Shim, V. P. & Lee, T.

2012. Role Of Gastrocnemius-Soleus Muscle In

Forefoot Force Transmission At Heel Rise - A 3d

Finite Element Analysis. J Biomech, 45, 1783-9. Doi:

10.1016/J.Jbiomech.2012.04.024.

Chernbumroong, S., Atkins, A. S. & Yu, H. Activity

Classification Using A Single Wrist-Worn

Accelerometer. Software, Knowledge Information,

Industrial Management And Applications (Skima),

2011 5th International Conference On, 2011. Ieee, 1-6.

Crossley, K. M., Thancanamootoo, K., Metcalf, B. R.,

Cook, J. L., Purdam, C. R. & Warden, S. J. 2007.

Clinical Features Of Patellar Tendinopathy And Their

Implications For Rehabilitation. J Orthop Res, 25,

1164-75. Doi: 10.1002/Jor.20415.

Dutta, A., Khattar, B. & Banerjee, A. 2012. Nonlinear

Analysis Of Electromyogram Following Gait Training

With Myoelectrically Triggered Neuromuscular

Electrical Stimulation In Stroke Survivors. Eurasip

Journal On Advances In Signal Processing, 2012, 153.

Doi: 10.1186/1687-6180-2012-153.

Emarketer. 2014. Smartphone Users Worldwide Will Total

1.75 Billion In 2014 [Online]. Emarketer. Available:

Http://Www.Emarketer.Com/Article/Smartphone-

Users-Worldwide-Will-Total-175-Billion-

2014/1010536 [Accessed May 23rd 2014].

Foundation, T. A. S. 2014. Apache Cordova [Online]. The

Apache Software Foundation. Available: Http://

Cordova.Apache.Org/ [Accessed May 23rd 2014].

Fulk, G. D., Edgar, S. R., Bierwirth, R., Hart, P., Lopez-

Meyer, P. & Sazonov, E. 2012. Identifying Activity

Levels And Steps Of People With Stroke Using A

Novel Shoe-Based Sensor. J Neurol Phys Ther, 36,

100-7. Doi: 10.1097/Npt.0b013e318256370c.

Gefen, A., Megido-Ravid, M., Itzchak, Y. & Arcan, M.

2002. Analysis Of Muscular Fatigue And Foot

Stability During High-Heeled Gait. Gait & Posture,

15, 56-63. Doi: 10.1016/S0966-6362(01)00180-1.

Google. 2014a. Iaccelerometer Capture - Aplicações

Android No Google Play [Online]. Google. Available:

PhyCS2015-DoctoralConsortium

Https://Play.Google.Com/Store/Apps/Details?Id=Com

.Imspdev.Iaccelerometer [Accessed 23rd May 2014].

Google. 2014b. Ifatigue Detector - Aplicações Android No

Google Play [Online]. Google. Available:

Https://Play.Google.Com/Store/Apps/Details?Id=Com

.Imspdev.Ifatiguedetector [Accessed 23rd May 2014].

Haber, M. 2004. Reliability Of A Device Measuring

Triceps Surae Muscle Fatigability. British Journal Of

Sports Medicine, 38, 163-167. Doi:

10.1136/Bjsm.2002.002899.

Hastings, M. K., Sinacore, D. R., Woodburn, J., Paxton, E.

S., Klein, S. E., Mccormick, J. J., Bohnert, K. L.,

Beckert, K. S., Stein, M. L., Strube, M. J. & Johnson,

J. E. 2013. Kinetics And Kinematics After The Bridle

Procedure For Treatment Of Traumatic Foot Drop.

Clin Biomech (Bristol, Avon), 28, 555-61. Doi:

10.1016/J.Clinbiomech.2013.04.008.

Islam, R., Islam, R. & Mazumder, T. A. 2010. Mobile

Application And Its Global Impact. International

Journal Of Engineering & Technology, 10.

Kasahara, S., Ebata, J. & Takahashi, M. 2007. Analysis Of

The Repeated One-Leg Heel-Rise Test Of Ankle

Plantar Flexors In Manual Muscle Testing. Journal Of

Physical Therapy Science, 19, 251-256. Doi:

10.1589/Jpts.19.251.

Lunsford, B. R. & Perry, J. 1995. The Standing Heel-Rise

Test For Ankle Plantar Flexion: Criterion For Normal.

Physical Therapy, 75, 694-698.

Monteiro, D. P., Britto, R. R., Lages, A. C., Basilio, M. L.,

De Oliveira Pires, M. C., Carvalho, M. L., Procopio,

R. J. & Pereira, D. A. 2013. Heel-Rise Test In The

Assessment Of Individuals With Peripheral Arterial

Occlusive Disease. Vasc Health Risk Manag, 9, 29-35.

Doi: 10.2147/Vhrm.S39860.

Moran, K. A. & Marshall, B. M. 2006. Effect Of Fatigue

On Tibial Impact Accelerations And Knee Kinematics

In Drop Jumps. Med Sci Sports Exerc, 38, 1836-42.

Doi: 10.1249/01.Mss.0000229567.09661.20.

Österberg, U., Svantesson, U., Takahashi, H. & Grimby,

G. 1998. Torque, Work And Emg Development In A

Heel-Rise Test. Clinical Biomechanics, 13, 344-350.

Doi: 10.1016/S0268-0033(98)00100-4.

Pieper, B., Templin, T. N., Birk, T. J. & Kirsner, R. S.

2008. The Standing Heel-Rise Test: Relation To

Chronic Venous Disorders And Balance, Gait, And

Walk Time In Injection Drug Users. Ostomy/Wound

Management, 54, 18-22, 24, 26-30 Passim.

Plux 2010. Bioplux Research User Manual, Lisboa, Plux.

Russell, S. D., Bennett, B. C., Kerrigan, D. C. & Abel, M.

F. 2007. Determinants Of Gait As Applied To

Children With Cerebral Palsy. Gait Posture, 26, 295-

300. Doi: 10.1016/J.Gaitpost.2006.09.079.

Schmid, S., Hilfiker, R. & Radlinger, L. 2011. Reliability

And Validity Of Trunk Accelerometry-Derived

Performance Measurements In A Standardized Heel-

Rise Test In Elderly Subjects. The Journal Of

Rehabilitation Research And Development, 48, 1137.

Doi: 10.1682/Jrrd.2011.01.0003.

Segura-Orti, E. & Martinez-Olmos, F. J. 2011. Test-Retest

Reliability And Minimal Detectable Change Scores

For Sit-To-Stand-To-Sit Tests, The Six-Minute Walk

Test, The One-Leg Heel-Rise Test, And Handgrip

Strength In People Undergoing Hemodialysis. Phys

Ther, 91, 1244-52. Doi: 10.2522/Ptj.20100141.

Silbernagel, K. G., Nilsson-Helander, K., Thomee, R.,

Eriksson, B. I. & Karlsson, J. 2010. A New

Measurement Of Heel-Rise Endurance With The

Ability To Detect Functional Deficits In Patients With

Achilles Tendon Rupture. Knee Surg Sports

Traumatol Arthrosc, 18, 258-64. Doi:

10.1007/S00167-009-0889-7.

Sman, A. D., Hiller, C. E., Imer, A., Ocsing, A., Burns, J.

& Refshauge, K. M. 2014. Design And Reliability Of

A Novel Heel Rise Test Measuring Device For

Plantarflexion Endurance. Biomed Res Int, 2014,

391646. Doi: 10.1155/2014/391646.

Sole, C. C., Milosavljevic, S., Sole, G. & Sullivan, S. J.

2010. Exploring A Model Of Asymmetric Shoe Wear

On Lower Limb Performance. Phys Ther Sport, 11,

60-5. Doi: 10.1016/J.Ptsp.2010.02.002.

Thompson, P. D., Buchner, D., Pina, I. L., Balady, G. J.,

Williams, M. A., Marcus, B. H., Berra, K., Blair, S.

N., Costa, F., Franklin, B., Fletcher, G. F., Gordon, N.

F., Pate, R. R., Rodriguez, B. L., Yancey, A. K.,

Wenger, N. K., American Heart Association Council

On Clinical Cardiology Subcommittee On Exercise,

R., Prevention, American Heart Association Council

On Nutrition, P. A. & Metabolism Subcommittee On

Physical, A. 2003. Exercise And Physical Activity In

The Prevention And Treatment Of Atherosclerotic

Cardiovascular Disease: A Statement From The

Council On Clinical Cardiology (Subcommittee On

Exercise, Rehabilitation, And Prevention) And The

Council On Nutrition, Physical Activity, And

Metabolism (Subcommittee On Physical Activity).

Circulation, 107, 3109-16. Doi:

10.1161/01.Cir.0000075572.40158.77.

Turner, D. E., Helliwell, P. S., Emery, P. & Woodburn, J.

2006. The Impact Of Rheumatoid Arthritis On Foot

Function In The Early Stages Of Disease: A Clinical

Case Series. Bmc Musculoskelet Disord, 7, 102. Doi:

10.1186/1471-2474-7-102.

Van Uden, C. J. T., Van Der Vleuten, C. J. M., Kooloos, J.

G. M., Haenen, J. H. & Wollersheim, H. 2005. Gait

And Calf Muscle Endurance In Patients With Chronic

Venous Insufficiency. Clinical Rehabilitation, 19,

339-344. Doi: 10.1191/0269215505cr809oa.

Veilleux, L., Rauch, F., Lemay, M. & Ballaz, L. 2012a.

Agreement Between Vertical Ground Reaction Force

And Ground Reaction Force Vector In Five Common

Clinical Tests. J Musculoskelet Neuronal Interact, 12,

219-223.

Veilleux, L. N., Cheung, M., Ben Amor, M. & Rauch, F.

2012b. Abnormalities In Muscle Density And Muscle

Function In Hypophosphatemic Rickets. J Clin

Endocrinol Metab, 97, E1492-8. Doi:

10.1210/Jc.2012-1336.

Vuillerme, N. & Boisgontier, M. 2010. Changes In The

Relative Contribution Of Each Leg To The Control Of

Quiet Two-Legged Stance Following Unilateral

MeasurementofHeel-RiseTestResultsusingaMobileDevice

Plantar-Flexor Muscles Fatigue. Eur J Appl Physiol,

110, 207-13. Doi: 10.1007/S00421-010-1449-Z.

Yocum, A., Mccoy, S. W., Bjornson, K. F., Mullens, P. &

Burton, G. N. 2010. Reliability And Validity Of The

Standing Heel-Rise Test. Phys Occup Ther Pediatr,

30, 190-204. Doi: 10.3109/01942631003761380.

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