Honors Thesis Paper

Predicting Fatigue in Military Personnel Using Wearable Technology 1
Table of Contents
Table of Figures…………………………………………………………………………………..2
Introduction………………………………………………………………………………………3
Thesis Statement...……………………………………………………...…………………3
Abstract……………………………………………….………………...…………………3
Overview…………………………………………...…………………...…………………4
The Issue…………………………………………...…………………...…………………7
History Behind the Issue…………………………………………...……...………7
Challenge posed by issue………..………………………………………...9
Impacts…………………………………………………………………...10
Methods………………………………………………………………………………………….10
Results...........................................................................................................................................14
Demographics……………………………………………………………………………14
Treadmill – Heart Rate…………………………………………………………………..14
Leg Extension – Heart Rate……………………………………………………………...16
Treadmill – Breathing Rate………………………………………………………………17
Leg Extension – Breathing Rate…………………………………………………………18
Maximum Volume of Oxygen Consumption…………………………………………....19
Maximum Energy Expenditure………………………………………………………….21
Discussion……………………………………………………………………………………….23
Conclusion………………………………………………………………………...…………….28
References……………………………………………………………………………….……...30
Appendices………………………………………………………………………………….…..32

Table of Figures
1 – Ralph Lauren PoloTech Shirt……………………………………………………………….…6
2 – Sensors inside of shirt………………………………………………………………………....6
3 – PoloTech application screenshot………………………………………………………………7
4 – Joint Position Experiment by Farshid…………………………………………………………8
5 – Table of Participant Information…..........................................................................................14
6 – Treadmill Heart Rate Graph………………………………………………………………….15
7 – Leg Extension Heart Rate Graph…………………………………………………………….16
8 – Treadmill Breathing Rate Graph.…………………………………………………………….17
9 – Leg Extension Breathing Rate Graph.……………………………………………………….18
10 – VO2 Calculations Table…………………………………………………………………….20
11 – Treadmill VO2max Graph…………………………………………………………………...20
12 – Energy Expenditure Calculations…………………………………………………………..21

Introduction
Thesis statement
Fatigue can be tracked and predicted in the military through wearable technology using
athletic clothing with activity sensors, preventing further injury to soldiers and enabling peak
performance output at all times.
Abstract
Military personnel are affected by muscle fatigue during the various missions and
training regimens for their work. Muscle fatigue is caused by the overuse and lack of nutrients to
muscles. When a soldier is fatigued, they are unable to perform at their maximum potential and
are also more susceptible to injury. For military personnel to save time and money as well as
become more efficient within the missions they deploy soldiers, muscle fatigue should be
predicted. Predicting fatigue will allow for a reduced rate of negative exercise-related impacts.
This means that soldiers will be able to avoid potential life threatening situations they encounter
due to the muscle fatigue. The newest technology in wearable devices includes clothing that
incorporates heart rate monitors, breathing rate and breathing depth sensors, and a database that
converts this information into the amount of calories burned during a workout. Fatigue can be
tracked and predicted in the military using wearable clothing with activity sensors, preventing
further injury to the soldiers and optimizing performance output at all times. For military
personnel, the ability to predict fatigue using this technology would be beneficial to the soldiers
and the military as a whole.

Overview
The military face many problems during every mission in which military personnel and
field operatives are enduring long, strenuous activities or operations. These personnel become
fatigued and are not able to perform at their maximum potential, making the missions longer and
increasing the risk for mistakes occurring. Determining the point when fatigue occurs is
important because “at an operational level, fatigue has an insidious effect on the interaction
between teams” (Johnson, Chris). There has not previously been an accurate, accessible way for
fatigue to be predicted, which means that soldiers have a higher risk of becoming injured. This
can result in field operatives being absent from the field for extended periods of time because
they are forced to recover before working again. The ability to detect fatigue has the capability of
saving soldiers from life threatening situations in which they are not able to perform at their peak
performance. Allen et al. determined fatigue as being a “progressive decline of performance”
that puts soldiers in situations where the lack of total ability could be detrimental to their safety.
“Unless greater attention is paid to the more complex, systemic aspects of fatigue then there
seems little prospect that we will be able to reduce the growing numbers to accidents that have
been experienced by many military organizations” (Johnson, Chris).
According to Allen et al. (2008), there are two types of muscle fatigue, one is central
fatigue and the other is peripheral fatigue. Central fatigue is more present after aerobic based
exercises. This means that activities similar to running and full body exercises will contribute to
the body becoming centrally fatigued. The amount of mitochondria and its ability to use the
oxygen being consumed is the biggest factor contributing to muscle fatigue. This is difficult to
detect in an exact figure without using high technology devices, but it can be estimated using a
calculation that includes heart rate. The mitochondria’s efficiency in utilizing the oxygen

consumed affects how well the body can recover from energy expenditure as well as reduce the
risk of becoming fatigued. Peripheral fatigue is seen more in the extremities of the body and can
affect the smaller muscles. For example, “fatigue in a small muscle of the hand could be entirely
peripheral” (Allen et al., 2008). This peripheral fatigue does not have as much of an effect on the
body as the central fatigue, because there are fewer muscles involved. Central fatigue consists of
an overall tiredness of the body, and peripheral tends to affect just one portion where the specific
muscles were being used.
This research study will address the fact that wearable technology, such as the Ralph
Lauren PoloTech shirt used, can be a tool for detecting the onset of fatigue in military personnel
who are completing rigorous field tasks. The shirt can be seen in Figure 1 below, and the inside
of the shirt is displayed in Figure 2. The sensor marked “A” is the contact point for a strain gauge
that wraps around the trunk of the subject. This band is meant to measure the breathing rate and
breathing depth while exercising. As the band expands and contracts, it is measured as one
breath, and the amount of expansion in the band during that breath is the breathing depth. The
sensor marked “B” in Figure 2 is the pressure sensor that picks up the heart rate for the subject.
The position of this sensor is located immediately over the heart, in order to get the most
accurate readings. The readings are output to a live feedback system that syncs with a smart
phone. This data is able to be constantly monitored, and is displayed in Figure 3 below.

Figure 1: This is the Ralph Lauren PoloTech shirt that was used in this study. (Torres, 2015).
Figure 2: This is the inside of the shirt, showing the sensors that are in contact with the skin.

Figure 3: The PoloTech application which shows live biometric data.
The Issue
History Behind the Issue
The idea of predicting fatigue using wearable technology with activity monitoring
sensors has not been thoroughly tested. The wearable technology field is relatively new and has
yet to be explored. It is a growing area of interest that will continue to become more useful in
many different aspects of everyday life. Previous studies that observed fatigue needed to use
high-technology machines that are really expensive, rather large, or invasive to the body.
One of the ways that muscle fatigue is currently tested in field operatives is by having
these individuals assume a position with their leg, and if they are not able to get to that desired
position, they are fatigued. This particular instance was done in a pilot study in the Journal of
Athletic Training. (Mohammadi et al., 2013) An image of this can be seen below in Figure 2.

This information means that since the patient was considered fatigued, they were more
susceptible to injuries in their legs. The ability to sense the positions of the joints were greatly
reduced in a fatigued patient, which is the problem in this case. The soldier is not able to
accurately feel exactly where they are walking and at what angle their knees or ankles are at
during impact with the ground. This can lead to potentially serious injuries.
Figure 4: Image of the joint position for a subject in the Farshid study. (Mohammadi et al.,
2013).
Another study conducted on fatigue involved detecting whether the muscle fatigue was
completely central or if there was peripheral fatigue present. This was done by Vollestad (1997)
where she used twitch interpolation to directly measure the central fatigue. This twitch

interpolation involves an electrical stimulation that measures the forces through a nerve. The
measurement of twitch interpolation is the difference between the force that the muscle can
produce when it is at its maximum voluntary contraction and the stimulation forces. This
difference shows how much the muscle has decreased from its maximum performance levels.
The study found that central fatigue was reliably detected using the twitch interpolation, but
peripheral fatigue was more difficult to detect, and other testing methods are not as accurate as
the electrical stimulation.
Challenge posed by issue
Fatigue is difficult to predict because it is different in every person. Fatigue is a common
occurrence in the field as demonstrated by a 2010 study of 90,000 veterans that observed “over
75 percent reporting fatigue” (Russell, 2012). This study was significant because the veterans
were surveyed about the different conditions that they had during war. With over 75% of the
90,000 veterans reported being fatigued during at least one point while in the military, it is
apparent that muscle fatigue is a large issue. The main factor that will affect when fatigue begins
to hinder a subject’s performance is how physically fit they are. The overall physical fitness level
of the subject can impact the body’s ability to adapt to changes in activity, which can be very
quickly if they are fit, or slowly if the subject is unfit and out of shape.
Though currently there is not an accurate way to predict when fatigue is going to set in,
there are steps that field operatives can take in order to prevent fatigue. The best way to prevent
fatigue is to make sure that the training program is as close to what the real missions and tasks
will be. This ensures that the body is preparing at the same activity level while using the same
muscles. During training, the field operative should stay as hydrated and as cool as possible.
Maintaining a cool core allows for the soldier to keep pushing and performing at their best

instead of over-heating and tiring out the muscles. The last key measure that the soldier should
take in training is to pace themselves through the entire workout. When being forced to perform
tasks that are so strenuous and timely, the soldier must make sure that the energy levels in the
body are not being wasted at the beginning of the task (Smith). Even though the subject is able to
prepare for certain types of exercise, there are some variables in an activity that are out of the
subject’s control, which can include wind conditions or varying loads of materials being
transferred in the case of an emergency.
Impacts
Before conducting research, the only way to predict fatigue involved having the subject
hooked up to several different sensors and systems. One of the methods to determine the
intensity of muscle fatigue is electromyography (EMG). According to Allen (2008), this is done
by measuring the muscle force of a patient before and after exercise and observing the change in
force exerted. The magic number considered to be fatigue by most researchers is 60%. This
means that if muscles with an area of 20 cm2
can lift a maximum of 100 kg, which is 1000
Newton’s, the calculation would be the total Newton force lifted over the area of the muscles,
equaling 50 N/cm2
(Science Learning Hub, 2007).
This lack of testing mobility causes several constraints. For example, the real time and
place information in the field is unable to be tracked. There are also no individualized fatigue
parameters that could be utilized for prediction or intervention.
If fatigue can be predicted and prevented using smart shirts that track basic biometric
readings, people will be able to prevent the risk of injury and make sure that they are optimizing
their body’s capabilities before continuing to work. This would be extremely useful to the
military because soldiers and field operatives would always be performing at their maximum,

and would be less likely to be injured in the field due to fatigue from strenuous exercise and
activities.
Methods
Before the study started, an IRB was created, submitted and approved through the
Arizona State University Institutional Review Board. This process involved reporting the
purpose of the study, the types of subjects being recruited, equipment and protocols being used,
and several other details that are necessary for any research study.
The Sun Devil Fitness Center was used as the site for research and given a permission
form. It was signed and completed before any research could be done as shown in Appendix A.
The participants also filled out a consent form, which informed them on the study details as well
as their rights as a participant. This form restated the information from the IRB, so that the
subject was well aware that the study offered no direct benefits to them. This form can be seen in
Appendix B.
For testing, the participant was instructed to come to the gym in running shoes, gym
shorts, and a t-shirt. The t-shirt was replaced by the Ralph Lauren PoloTech smart shirt. Before
any measurements were performed, the subjects took the Physical Activity Readiness
Questionnaire (PAR-Q) performance test (Appendix C) to ensure that they are deemed
physically fit to complete these exercises. This test is necessary for determining the physical
fitness of a subject and their readiness for the study. The participants were then weighed and
height was taken after they changed and were ready to be tested.
The shirt is meant to track basic biometric measurements of heart rate, breathing rate, and
breathing depth. These measurements will be used to observe central fatigue, which reacts to
long cardiovascular activities like running, and peripheral fatigue, which occurs in limb muscles
during strength-based activities. This research will look at how peripheral fatigue is related to the

central fatigue that is being tracked by the PoloTech shirt. This will be done by first having the
participant perform a central fatigue test, using a treadmill to induce fatigue through aerobic
exercise and observing the changes in the basic biometric readings. These values will be used to
determine the maximum volume of oxygen that the subject can use. The subject will then be
peripherally fatigued by performing a series of leg extensions with heavy weights and then the
same basic biometrics will be tracked. The maximum volume of oxygen for the subject will
again be observed and compared to the biometrics measured in the central fatigue testing for any
correlation.
A central fatigue treadmill test was conducted first. This was accomplished by having the
participant walk on a treadmill at 3 miles per hour for 3 minutes, then increased to 4 miles per
hour for 3 minutes, then 5 miles per hour for 3 minutes, and finally 6 miles per hour for 3
minutes. During this entire 12 minute period, the heart rate, the breathing rate, and the breathing
depth was recorded every 10 seconds from the iPhone PoloTech application into a Microsoft
Excel document.
The second phase of the study was the peripheral fatigue leg extension test. The
participant started with lifting leg extensions at 36 pounds and perform 10 repetitions, then
moved up to 48 pounds for 10 repetitions immediately after the previous set, and finished with
60 pounds for 10 more repetitions. These 30 repetitions were performed without the participant
taking a break. The heart rate, breathing rate, and breathing depth were all monitored from the
iPhone PoloTech applications and recorded into an Excel spreadsheet on the laptop.
For data analysis, the heart rate graphs from the study were examined. The slope of the
heart rate during the workout was used in order to find the point of the maximum heart rate. This
maximum heart rate was used to determine what the maximum volume of oxygen consumed

(VO2max) was for each of the participants. This is important because the VO2max is the absolute
maximum amount of oxygen that can be used for each subject. The value is able to tell us what
the aerobic threshold is, which corresponds to the energy expenditure. The heart rate along with
the VO2max shows what category of workload the participant is facing and what their total energy
expenditure is. With all of this information, it can be determined whether wearable technology in
the field is a sufficient way to predict fatigue for military personnel.

Results
Demographics:
Participants
Age (Years) Height Weight (pounds)
Participant 1 22 5’ 10” 150
Average 21.66 6’ ½” 158.33
Figure 5: Table showing age, height, and weight of participants in the study
Treadmill – Heart Rate
The heart rate information graphed during the treadmill exercise is seen in Figure 6 below. This
data was recorded every 10 seconds for each of the participants. The graph is split into the four
different sections for how fast the participants were running during that 180-second time period.

Figure 6: This image displays the heart rates for each participant of the study during the treadmill
exercise.
This graph shows the relation of the heart rate to the time intervals at each increase in
speed on the treadmill. After 180 seconds, the speed of the treadmill was increased from 3 miles
per hour to 4 miles per hour. After 360 seconds, the speed was once again increased to 5 miles
per hour. For the last 180 seconds, the speed was set to 6 miles per hour.
As seen in the graph, there was a gradual increase in heart rate throughout the entire
workout. The average starting heart rate was 75.66 beats per minute, while the average final
heart rate was 161.66 beats per minute, making a difference of 86 beats per minute from start to
finish. This is expected because the heart rate increases with an increase in activity. The average
increase for the 180 second period at 3 miles per hour was found to be 27 beats per minute. The
average increase for the 180 second period at 4 miles per hour was found to be 13.33 beats per
minute. The average increase for the 180 second period at 5 miles per hour was found to be
27.66 beats per minute. The average increase for the 180 second period at 6 miles per hour was

found to be 13.66 beats per minute. The average heart rate increase in the first 10 seconds of the
increase in speed was 5.66 beats per minute and the increase in speed in the first 20 seconds was
8.75 beats per minute.
Leg Extension – Heart Rate
Figure 7: This image displays the heart rates for each participant of the study during the treadmill
exercise.
The results of the leg extension showed an average increase from start to finish of 31.33
beats per minute. It was seen that the heart rates did not vary as much during the leg extension
exercise as they did during the treadmill exercise. This was due to the starting heart rate being
higher in the leg extension exercise since it was completed after the treadmill exercise. It was set
up this way in order to better simulate real life conditions, since the peripheral activities in the
field are often completed during or after central activities such as running and carrying
equipment.

Treadmill – Breathing Rate
Figure 8: This figure shows the breathing rate for each participant on the treadmill.
The breathing rate data had much more variability than the heart rate data. This most
likely occurred because of the band in the shirt was not completely tight against the skin and was
not able to produce completely accurate results. The breathing rate data was collected in order to
observe trends. The shirt collects breathing rate data, and as expected, the breathing rate steadily
increased with an increase in treadmill speed just as the heart rate did.

Leg Extension – Breathing Rate
Figure 9: This graph shows the breathing rates for the participants during leg extensions.
The breathing rate for the leg extension exercise was more steady and gradual than the
data with the treadmill. This shows that the shirt can detect breathing rates better while the
subject is sitting somewhat still and that the data should be validated for both while the subject is
sitting and also while they are running. The average difference in breathing rate from start to
finish in the leg extension exercise was an increase of 5.33 breaths per minute. This is much less
than the 22.33 breaths per minute increase during the treadmill exercise. This most likely
occurred because of the central fatigue affecting more of the body and giving an overall tiredness
versus one sections of muscles being used.
The main purpose for taking this breathing rate data was to look at the trends and test to
see if there were any significant issues with collecting breathing rate information while
performing the two types of exercises. The heart rate information is the key in calculations for
the VO2max values and also the energy expenditures values. But, using as many different sensors

and collecting as much data as possible while wearing a smart shirt is only a positive to the
study.
Maximum Volume of Oxygen Consumption
Using the chart for maximum volume of oxygen in Appendix E, it is seen that the trend
line for all the heart rates compared to the VO2 is the same. This was tested using all three
participants, and the VO2max was determined to be y = 0.02x + (-1.0), which was found using the
trend line in the Appendix E data and proven in the Figure 11 data. The y-value is the VO2max
value that is being determined. The x-value in this equation is the maximum calculated heart
rate. The common calculation for maximum heart rate is HRmax = (220) – (age). The VO2max
values for each of the participants can be seen in Figure 10. A sample calculation for this can be
seen below:
Participant 1:
Maximum heart rate:
HRmax = (220) – (age)
HRmax = (220) – (22)
HRmax = 198 Beats/Minute
Maximum VO2:
VO2max = 0.02(HRmax) + (-1.0)
VO2max = 0.02(198) + (-1.0)
VO2max = 2.96 Liters/Minute

VO2 Calculations
Participants VO2reached (Liters/Minute) VO2max (Liters/Minute)
Participant 1 2.44 2.96
Figure 10: This is a table of the VO2reached and VO2max values that were calculated.
Figure 11: Sample graph of VO2max calculation for Participant 1 showing common trend line.
This VO2max graph was made using three heart rate values taken from the results of
Participant 1 on the treadmill. The heart rates were selected based on that they were values which
occurred after the plateau during each speed section of the workout. These heart rates were then
compared to Classification of Physical Work in Appendix E. The oxygen consumption values
were found by interpolation with each of the heart rate values. After these were graphed, the line
of best fit was used by plugging in the maximum heart rate to find the maximum volume of
oxygen consumption.

Maximum Energy Expenditure
Using these VO2max values, the maximum energy expenditure is calculated. This was
completed again using the table from Appendix E where the heart rate can be used to find the
estimated oxygen consumption and the estimated energy expenditure.
The formula, found using the Appendix E table is y = 0.1x + (-5.0). This shows that the
x-value is the heart rate, which is taken from the maximum heart rate calculations, which were
already performed, so that the energy expenditure could be found. The information is seen in the
following Figure 12 and a sample calculation can be seen below.
Participant 1:
Maximum heart rate:
HRmax = 198 Beats/Minute
Maximum Energy Expenditure:
EnergyExpendituremax = 0.1(HRmax) + (-5.0)
EnergyExpendituremax = 0.1(198) + (-5.0)
EnergyExpendituremax = 14.8 kcal/min
Energy Expenditure Calculations
Participants Reached Energy Expenditure
(kcal/min)
Max. Energy Expenditure
(kcal/min)
Figure 12: This is a table of the reached and maximum energy expenditures for each participant.

The maximum energy expenditures were virtually the same for all three of the
participants. This was due to the fact that the energy expenditure calculation was based on the
maximum heart rate value, which was based on the age of the participant. Given that two of the
participants were 22 years old and the third was 21 years old, this means that the calculated
maximum energy expenditure values will all be very similar. The 14.8-14.9 kcal/min means that
the participants fall into the extremely high (sport) category based upon the information given in
Appendix E.

Discussion
There were many strengths to this study, which allowed for it to run smoothly and for the
collection of data to be used successfully for data analysis. The greatest strength of this study
was that it was innovative. There has not been much done in regards to predicting fatigue using
simple biometrics in a live feedback system. This study allowed exploration into something that
has not been documented before, making each discovery in the study an important one. Putting
the participant through aerobic exercise then isometric exercise enabled for me to see the
cumulative effect of multiple types of exercise on the body. Also, the respiratory rate began at a
higher level during the leg extensions as the body has already undergone a prolonged period of
exercise. Even though there was a resting period to get the heart rate back down, the treadmill
exercise was performed first because this mimics real-life conditions. Mimicking real life
conditions meant that there is not much seen in the way of increased heart rate.
This is important in active field conditions as soldiers utilize their whole body in different ways
throughout their activities. It is very interesting that this new wearable technology is used for a
much different application than it was meant for, which was recreational use for regular people
to observe basic biometrics during workouts. The opportunity for research and more innovative
products that can be derived from this study have the capability of saving many lives and helping
the military complete missions without having fatigued field operatives. There are many uses for
this wearable shirt with sensors, and attempting to find a way for fatigue to be predicted is an
innovative use of this product. Another strength of this study was the fact that the research was
conducted in a controlled and stable environment. Although there were only three participants
and just myself conducting the research, this allowed for all of the testing to be done in the same
exact manner. This means that all data was consistent and there were no unexpected variables in

the study that commonly occurs in large studies with multiple personnel and participants. The
study was also controlled and very safe because of the approval of an IRB for the study. This
allowed for the use of human participants to aid in the collection of data that can be published.
The stable control was also present in the participants which were tested. Even though the shirts
limited the body types that were able to test the shirt, it was a benefit because all data was
collected from very similar participants. These participants were all similar sizes with similar
athletic abilities, giving the research study a strong base for future research and development.
The subjects being all males, having similar fitness abilities, and the same body size amounted to
the study having controlled variables where the data could be generalized for this specific body
type. For this pilot study, the control is a good feature to have, but future work will need to
consist of a wider variety of body types.
There were some weaknesses in this study though. The biggest weakness was the process
in which the participants were fatigued. They were fatigued in a way that might not replicate the
way that soldiers will be facing muscle fatigue in the field. This is because the participants were
running on a treadmill in order to reach a central fatigue, and the soldiers in the field will be
carrying several pounds in equipment. The participants could have been given weighted
backpacks in order for more accuracy in stimulating the central fatigue that soldiers face in the
field. The study could have also been set up differently regarding the peripheral fatigue
exercises. Although the soldiers are using their legs to pick up large objects during trainings and
missions, they are not performing leg extensions on a machine. One way to fix this would be to
put the participants through a full body workout that soldiers use for training. This would better
resemble the actual muscle activity and peripheral muscle fatigue that field operatives face.
Another setback in this study was that there were only two shirts, a small and a medium, which

made it difficult for finding participants that would fit the shirts. When participants were
selected, but did not fit in either of the shirts, the data was not accurate because the sensors were
not making optimal contact with the body and the readings would not register. This could have
been prevented by finding more sizes of shirts and a selection larger of sizes for the participants.
The participants used in the study are considered human subjects and were being tested, so it was
necessary to request an IRB be approved for this research. The IRB was submitted quickly after
it was determined that one must be approved, but there was a setback in the approval process that
took many weeks of revisions. This set the study back almost a month and did not allow for
adequate time in collecting data. The PoloTech phone application was another downfall in this
research. This was because the application only allowed for one participant performing in the
study at a time. It would have been more convenient for two participants to participate in the
study during the same session since there were required resting periods and the down time
became quite excessive. The application also did not always work, which became a large
problem. On the day that the third and fourth subjects were scheduled to participate, the shirt
synced when it was tested in the morning, but by the time of the fourth appointment it would not
sync at all. This could have been due to many reasons, including the shirt being too large for the
subject and the sensors not making solid contact, or the Bluetooth signal not being strong enough
in the gym. This meant that the study would include a total of three participants instead of the
anticipated four participants. The narrow population that the shirt would fit made this a
complicated process in recruiting participants that would fit in the shirt.
Overall, this study was successful. There were three subjects that participated. Solid data
was acquired for each of those three participants, and trends were seen in the data which proved
that the predicting of fatigue is possible if more testing is done. The heart rate and breathing rate

in the shirt can be used in order to find an algorithm that would correlate to the point at which
fatigue is expected to set in. The next step would include testing more subjects and validating the
calculated volume of oxygen consumption using a mask system which examines the oxygen
levels during each inhalation compared to the oxygen levels during each exhale.
Before the shirt can be tested on more participants, it should be modified so that it is
available to a larger selection of people. The shirt should come in more sizes, and be made of a
different material that is lighter and more elastic that can fit more body types. Sensors in the shirt
will also need to be relocated. The heart rate sensors is currently right over the heart, as it should
be, but this limits the shirt to only be accurate on men. The sensor should be located over the
heart, but another sensor should be placed below the bust line where it can still pick up a
heartbeat while still allowing men and women to both wear the shirt. The strain gauge which
wraps around the trunk of the shirt should also be placed lower than its current position. It is
even too high on men wearing the shirt, which causes for data to not record all of the time. If this
sensor was moved down, it would allow the breathing rate data to be recorded while women
wear the shirt as well.
Once the shirt calculations are validated, the next step would be to perform the study
using a wider variety of exercises, and eventually moving to military subjects while they are
performing their training workouts. This would allow for the shirt to be tested on subjects that
have similar body shapes and are performing similar activities to field operatives.
The information found in this study can be used by those in the field studying fatigue and
the affects that cause a subject to lose maximum performance during an exercise or activity. This
is valuable information because of the areas in which it can be applied. Professional athletes,
firefighters, military soldiers, or any other career which depends on the body to constantly be in

the upmost shape and condition can use this to make sure they are not over-exerting their body.
Recommendations for future work would include refining the formulas that are used for
calculating the maximum volume of oxygen consumed, and grasping a better understanding of
what it would take to turn that into predicting when fatigue might set in. This has not been
accurately accomplished, and the process would be very valuable if this information was
discovered.

Conclusions
This research is very important because there is no other way that muscle fatigue can be
predicted in the field. This is a concept that has been researched for several years, and the fact
that wearable technology might be a breakthrough in the industry is very interesting. Because of
the data that was gathered, and the patterns that were able to be recognized, the shirt could
eventually be a viable option for predicting fatigue. The aim of this research was to determine if
fatigue can be tracked and predicted in the military using wearable clothing with activity sensors.
From the results, the aim of the research study has been supported. The data collected in this
study indicated that the shirt would be a viable option for recording biometrics that can be used
in predicting muscle fatigue. There would have to be further research done in order to support
the claim that fatigue can be predicted though.
Information found in the literature was very telling about what ways the shirt could be
used in predicting fatigue. Based on information that is currently available, a shirt with heart rate
and breathing rate sensors cannot directly predict fatigue. However, it would be able to use the
heart rate and breathing rate to calculate information such as the maximum volume of oxygen
consumed over a period of time in order to predict when the muscles become fatigued.
Many things can be learned from the research. The most telling thing that can be learned
from the research is that the shirt might eventually be able to predict fatigue in military
personnel. There would be more programming and algorithm identification that needs to be done
in order for the shirt to actually work in predicting fatigue. The shirt would also need to be
modified in the form of sensor placement so that all body types are able to wear it. This
information will be useful to all branches of the military or other careers that rely on people

being in great physical shape. This is because they will be able to use the information that the
shirt gives their personnel to detect when they will become fatigued.
The next steps to the study are the most important parts of the process. The information
that was gained from this initial study were important because there has not been much research
done that is similar to predicting fatigue using wearable technology. The first step that will need
to be done is including more participants and more exercises for identifying true muscle fatigue.
This next study would include members of the military. ROTC members on campus would be
optimal participants to be studied while doing their military training workouts.
The steps that were taken during this study worked because of the simplicity and
consistency for which they were set up. There was a protocol, which was followed exactly,
making all data collected as accurate as possible. This made it so that the results were telling of
what the shirt can detect, and how it can be improved. Overall, this study was very successfully
and offers much promise for future research.

References
Allen, D. G., Lamb, G. D., & Westerblad, H. (2008). Skeletal Muscle Fatigue: Cellular
Mechanisms. Physiological Review, 88, 1287-1332. Retrieved from
http://physrev.physiology.org/content/88/1/287
Enoka, R. M., & Duchateau, J. (2008). Muscle Fatigue: what, why, and how it influences muscle
function. The Journal of Physiology, 586(1), 11-23. Retrieved from
http://onlinelibrary.wiley.com/doi/10.1113/jphysiol.2007.139477/full
Johnson, C. (2007). The Systemic Effects of Fatigue on Military Operations. Institute of
Electrical and Electronics Engineers. Retrieved from
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.63.890&rep=rep1&type=pdf
Lockhart, T. (2012). Lecture 7 Work Physiology. Human Factors and Ergonomics Systems
Design.
Mohammadi, F., Azma, K., Naseh, I., Emadifard, R., & Etemadi, Y. (2013). Military Exercises,
Knee and Ankle Joint Position Sense, and Injury in Male Conscripts: A Pilot
Study. Journal of Athletic Training, 48(6), 790–796. Retrieved from
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3867090/
Quinn, E (2015, December 30). 7 Questions To Ask Before Starting A New Workout This Year.
About Health. Retrieved from
http://sportsmedicine.about.com/od/fitnessevalandassessment/qt/PAR-Q.htm
Russell, M. (2012, April 6). Underestimating the True Prevalence of War Stress Injury in the
Military. Huffington Post Healthy Living. Retrieved from
http://www.huffingtonpost.com/mark-c-russell-phd-abpp/ptsd-military-_b_1250227.html

Science Learning Hub (2007, June 21). Muscle Performance. Retrieved from
http://sciencelearn.org.nz/Contexts/Sporting-Edge/Science-Ideas-and-Concepts/Muscle-
performance
Smith, S. Prevent Fatigue During PFT. Military.com. Retrieved from
http://www.military.com/military-fitness/fitness-test-prep/prevent-fatigue-during-pft
Torres, T. (26 October 2015). Ralph Lauren PoloTech Shirt. Retrieved from
http://www.pcmag.com/article2/0,2817,2493326,00.asp
Vollestad, N. (1997). Measurement of human muscle fatigue. Journal of Neuroscience Methods,
74(2), 219-227. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/9219890
Young, S. N. (1986). The clinical psychopharmacology of tryptophan. In: Nutrition and the
Brain, 7, 49–88.
Zadpoor, A. A., & Nikooyan A. A. (2012). The effects of lower extremity muscle fatigue on the
vertical ground reaction force: A meta-analysis. Journal of Engineering in Medicine, 226(8),
579-588. Retrieved from
https://www.researchgate.net/publication/230820140_The_effects_of_lower-
extremity_muscle_fatigue_on_the_vertical_ground_reaction_force_A_meta-analysis

Appendices
Appendix A
SDFC Note of Site Approval

Appendix B
Consent Form
Title of research study: Predicting Fatigue in Military Personnel Using
Wearable Technology
Investigator: Brady Falk
Why am I being invited to take part in a research study?
We invite you to take part in a research study because you are self-identified as a healthy, fit
individual who is male between the ages of 18 and 25 who regularly exercises. You will be asked
to perform two simple workouts in which your heart rate, breathing rate, and breathing depth will
be monitored, and you will become aware of the range of these biometrics while performing mild
to vigorous activities.
Why is this research being done?
This research is being done in order to help identify an easier way to determine, predict, and
prevent muscle fatigue in individuals who are being physically active. This data will be able to
be used by several different fields, particularly the military and their soldiers in the field.
Knowing when fatigue is going to occur can make many injuries avoidable, and also allow for
the military to have all soldiers performing at their maximum potential.
How long will the research last?
We expect that individuals will spend 1 hour in the gym participating in the proposed activities.
How many people will be studied?
We anticipate studying 5 men between the ages of 18 and 25.
What happens if I say yes, I want to be in this research?
It is up to you to decide whether or not to participate. You will come to the gym in running
shoes, gym shorts, and a t-shirt that can be exchanged for the PoloTech shirt that will be
provided by the researchers. A Physical Activity Readiness Questionnaire (PAR-Q) will be filled
out before the testing begins and if all questions are answered “no” then the study will continue.
You will spend about 5 minutes being measured for height and weight, followed by a total of 12
minutes on the treadmill at 4 different speeds: 3 minutes each at 3 mph, 4 mph, 5 mph and 6
mph. You will then spend roughly 10 minutes on the leg extension machine performing 15
repetitions on each of the 3 predetermined weights (100 lbs, 120 lbs, and 140 lbs). Mr. Falk will
be recording all data during height and weight measurements, during the treadmill exercise, and
during the leg extension exercise. Once you are completed with the two exercises, there will be
no follow-up required.
What happens if I say yes, but I change my mind later?
You can leave the research at any time it will not be held against you.

If you decide to leave the research, the data will not be used in the study. If you decide to leave
the research, you may contact the lead investigator as well as Mr. Falk to have them discard the
data, if there has been any collected. The data will be deleted and not used in the study.
Is there any way being in this study could be bad for me?
The only risk to this study is that you may become tired from the exercises (or possibly sore the
next day), but will be able to terminate your participation at any time.
Will being in this study help me any way?
We cannot promise any benefits to you or others from your taking part in this research. However,
possible benefits include being aware of your heart rate, breathing rate, and breathing depth
during exercise.
What happens to the information collected for the research?
Efforts will be made to limit the use and disclosure of your personal information, including
research study records, to people who have a need to review this information. We cannot
promise complete secrecy. Organizations that may inspect and copy your information include the
IRB and other representatives of this organization.
Who can I talk to?
If you have questions, concerns, or complaints, or think the research has hurt you, contact the
research team at Deborah.L.Williams@asu.edu or at 480-727-7579.
This research has been reviewed and approved by the Bioscience IRB (“IRB”). You may talk to
them at (480) 965-6788 or research.integrity@asu.edu if:
● Your questions, concerns, or complaints are not being answered by the research team.
● You cannot reach the research team.
● You want to talk to someone besides the research team.
● You have questions about your rights as a research participant.
● You want to get information or provide input about this research.
Signature Block for Capable Adult
Your signature documents your permission to take part in this research.
Signature of participant Date
Printed name of participant
Signature of person obtaining consent Date
Printed name of person obtaining consent

Appendix C
Physical Activity Readiness Questionnaire
Name: ____________________________
Answer yes or no to the following questions:
1. Has your doctor ever said that you have a heart condition and that you should only do physical
activity recommended by a doctor?
2. Do you feel pain in your chest when you do physical activity?
3. In the past month, have you had chest pain when you were not doing physical activity?
4. Do you lose your balance because of dizziness or do you ever lose consciousness?
5. Do you have a bone or joint problem that could be made worse by a change in your physical
activity?
6. Is your doctor currently prescribing drugs (for example, water pills) for your blood pressure or
heart condition?
7. Do you know of any other reason why you should not do physical activity?
If you answered yes:
If you answered yes to one or more questions, are older than age 40 and have been inactive or are
concerned about your health, consult a physician before taking a fitness test or substantially
increasing your physical activity. You should ask for a medical clearance along with information
about specific exercise limitations you may have.
In most cases, you will still be able to do any type of activity you want as long as you adhere to
some guidelines.
If you answered no:
If you answered no to all the PAR-Q questions, you can be reasonably sure that you can exercise
safely and have low risk of having any medical complications from exercise. It is still important
to start slowing and increase gradually. It may also be helpful to have a fitness assessment with a
personal trainer or coach in order to determine where to begin.

Appendix D
Recruitment Paragraph
As you have been informed, I am using the Ralph Lauren PoloTech smart shirt to track heart
rate, breathing rate, and breathing depth. These biometric readings will be observed and recorded
during two separate exercises in order to decide the relationship between peripheral fatigue and
central fatigue. The data from running on the treadmill and the data from performing a series of
leg extensions will compared in order to determine if a wearable smart shirt can be an
appropriate device for predicting fatigue in military personnel. I would like you to participate in
this study by April 1st
, 2016. The participation in this study will take approximately one hour,
and there will be no follow up procedures.

Appendix E

Honors Thesis Paper

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