Download Physiological Monitor for the Prevention of Heat-Related Injuries

Physiological Monitor for the Prevention of Heat-Related Injuries
Wes Bellman, Biomedical Engineering Student, Lawrence Technological University
Matt West, Biomedical Engineering Student, Lawrence Technological University
Lorraine Novak, Biomedical Engineering Student, Lawrence Technological University
Kevin Mason, Electrical & Biomedical Engineering Student, Lawrence Technological
University
Abstract- The scope of this project is to monitor
physiological indicators in order to prevent heatrelated injury or death during physical activity.
These physiological indicators are: core body
temperature, heart rate and sodium
concentration of sweat. Together these
parameter measurements will indicate if the
athlete is approaching threshold values that are
dangerous for the athlete’s well-being.
1. Introduction
Heat stroke due to exertion is the third
leading cause of on-the-field sudden death in
athletes [1]. According to a USA today article,
deaths from heat related injuries for athletes has
more than doubled since 1975 [2]. The
combination of hot weather and intense exercise
makes athletes vulnerable to injuries such as
heat stroke and dehydration. Some of the
physiological conditions in question are rapid
heart rate, high body temperature, and
dehydration [3, 4]. Some symptoms of heat
stroke are nausea and vomiting, lack of
sweating, headache, confusion and loss of
consciousness. A body temperature of 104 F is
the first sign. This combined with an elevated
heart rate and dehydration puts the athlete at a
greater risk for heat-related injuries. Analyzing a
parameter such as sodium concentration in
sweat can offer a way to monitor dehydration as
shown in two studies by Dublin City University [5,
6]. Monitoring cored body temperature, heart
rate and sodium concentration in sweat with a
wearable device will allow medical staff and
athletes to take appropriate action and prevent
serious heat related injuries.
The project will have two aspects that
will be completed concurrently, the first being
the development of the physiological sensors.
Research has been completed on heat stroke
and other heat related illnesses to indicate which
physiological parameters will be monitored [3, 4].
An initial core body temperature threshold has
been found to be around 104oF [4]. Multiple
sources have been looked into for finding a
heart rate threshold [9, 10]. These sources have
not provided a specific range other than taking
your age and subtracting it from 220 for a max
heart rate of a healthy person. Previous
research [5, 6] has specified that monitoring
sodium concentration in sweat can be an
effective way in monitoring the hydration levels
of a subject. However, any specific correlation
between the sodium concentration and hydration
levels is still under investigation in the scientific
community. As a starting point we will use the
information that sweat contains between 2.253.4 grams of sodium [8]. Hypernatremia is a
condition in which the level of sodium in the
body is too low and this can result to a range of
problems from headaches and nausea to
serious health problems such as seizures and
respiratory arrest [7, 14]. For the monitoring of
the core body temperature, the principal of zeroheat flow has been determined as the best
method [11]. After the physical sensors have
been studied and developed, the microcontroller
and software system will be developed to read
the outputs of the sensors and make decisions
according to those readings. The research and
development of the microcontroller and software
system will be the second aspect of the project.
2. Heart Rate Sensor
Electrocardiography (ECG) uses
electrodes to measure the electrical activity of
the heart. During a heartbeat, the muscle cells
in the heart depolarize due to a net positive
change in the cell membrane potential [12,13].
In a healthy heart, depolarization occurs in an
orderly progression and is measured as a
change in voltage between pairs of electrodes
which graphically produces the PQRST
waveform; where each interval corresponds to a
specific stage of a heartbeat [12, 13]. An ECG
waveform can be analyzed to find irregularities
in the heartbeat and identify cardiovascular
issues or to calculate the heart rate in beats per
minute (BPM). Rapid heart rate is a symptom of
both heat stroke and dehydration; a maximum
heart rate (above being dangerous) is
approximated by subtracting the age of the
subject from 220 (BPM) [3, 4]. Calculating heart
rate from an ECG signal is a common practice
and wearable heart rate monitors can be
purchased for use in sports training and
personal fitness. We will develop a heart rate
monitor which measures the ECG signal from a
subject and from this calculate the heart rate. To
develop the initial ECG circuit, we are referring
to the ECG design and component values that
we used previously in a Bioinstrumentation Lab
curse [15]. The block diagram for the heart rate
monitor is shown in Figure 1.
Fig.2. Electrode Placement Diagram
Fig.1. Heart Rate Sensor Block Diagram
The input signal received from the electrodes
will be amplified using an instrumentation
amplifier, which amplifies the difference between
two input signals while rejecting signals common
to both [15]. A high pass filter is used to
eliminate DC offsets and the signal is amplified
again with the positive gain block [15]. The
purpose of the low pass filter and notch filter is
to limit the bandwidth to the frequencies of
interest and reduce 60 Hz noise [15]. The ECG
output will be sent to the microcontroller for
analog to digital conversion. This data will be
sent from the microcontroller to a laptop via
wired USB connection where the heart rate will
be measured using Matlab software. The
Matlab software is complete and will record 10
seconds of ECG output, measure the number of
QRS peaks within this time interval, and multiply
this by 6 to approximate the number of peaks
per minute, or (BPM). The circuit diagram for
the ECG is shown in the Appendix. The ECG
circuit will be built on a breadboard. To obtain
the ECG signal, we will use a 3 electrode setup.
An electrode will be placed in the fourth
intercostal space just lateral of the sternum on
both sides of the sternum. The diagram in
Figure 2 shows the approximate electrode
placement [18].
The final electrode is a ground which will be
placed on the subject’s arm.
Calibrating the heart rate monitor will
require ECG setup and measurement on a
subject. Before we can begin this we will have
to seek IRB approval. Due to the general
familiarity and low risk associated with ECG we
expect no issue obtaining IRB approval. During
testing, heart rate measurements will be taken
from a subject resting and exercising using our
sensor. An on-the-market wearable heart rate
monitor, which we already own, will be used to
measure the heart rate under the same
conditions in order to assess the accuracy of our
sensor. The primary challenges expected
during calibration are ensuring proper electrode
placement and determining the effects of
movement on the ECG.
3. Core Body Temperature Sensor
One of the symptoms of heat exhaustion
and heat stroke is a high body temperature [4].
The dangerous level for the body temperature is
104 F [4, 17] and at these temperatures the
nervous system is affected, the heart is
extremely stressed and organs begin to fail [17].
This body temperature range and monitoring
refers to core body temperature rather than then
surface temperature at the skin. Currently the
methods for monitoring core body temperature
involve the principle of zero-heat flow and
heating the skin to prevent any outflow heat loss.
To heat the skin surface a large electronic
heater and power source are needed. These
methods are most commonly used in operating
rooms and intensive care units in hospitals [11].
In order to monitor an athlete’s core body
temperature during movement and noninvasive
and portable sensor is needed. A research
group in Japan [11] developed and studied a
new method for measuring deep body
temperatures without a need for the heater
aspect. Their method made use of the setup
show in Figure 3.
Fig. 4. Temperature Sensor Block Diagram
Fig. 3. Diagram of Heat Flow Channels
Using this set up the following equation can be
used for determining the deep body temperature:
(1)
The variable, K, will be determined
experimentally and this will be discussed in the
sensor calibration section. To acquire the four
temperature readings needed to calculate the
core temperature, the use of temperature
transducers are required. The Analog Devices
AD22100 was selected for use as the
temperature transducers in this sensor. The
datasheet for this product is contained in the
Appendix. This transducer was chosen because
of the simplicity of interfacing it with an analog to
digital converter. The transducer has built in
signal conditioning and as a result only a simple
low pass filter is needed on the output to reduce
noise and maintain accuracy. The block diagram
showing the overall system for the core body
temperature sensor is shown in Figure 4.
The analog to digital converter is contained in
the microcontroller and the indicated 10 bit
resolution enables enough precision for the
readings from the AD22100. ccording to the
manufacturer’s datasheet an bit analog to
digital con erter gi es a resolution of .
using the 10 bit resolution only adds to the
precision and accuracy of the sensor.
The sensor will be made up of two parts
the shell and the temperature transducers. The
transducers are attached to the shell. The shell
has two parts: a thermal conductor and a
thermal insulator. The thermal conductor will be
copper and will be used for the fabrication of the
outer shell. The thermal insulator will be a
rubber material that can be injected into the
copper shell. This insulator creates the two
needed heat-flow channels; the first one is
between IC1 and IC3 and the second one is
between IC2 and IC4. The diagram showing the
outline of this sensor is shown in Figure 5
Fig. 5. Sensor Shell CAD Rendering
The calibration of the core body
temperature sensor has two portions. The first
portion is the determination of the K variable
used in the equation for determining core body
temperature. This will be done by using a water
bath of a known temperature to simulate a core
body temperature. The sensor will be placed on
this water bath and readings will be taken from
each of the transducers and with these readings
the K variable can be calculated. A possible
method for testing the accuracy of our sensor
and determined K variable is to alter the
temperature of the water and taking readings
from each transducer and calculate the
temperature of the water and compare it to the
known alue for the water’s temperature. The
second portion will involve comparison
calibrations to an ear thermometer. For the
scope of this project it is unrealistic to acquire or
use the instrumentation used in hospitals to
monitor the deep body temperature. As a result
the ear thermometer readings will give the
closest actual readings among the alternative
methods. The experiment set up will include a
subject doing a form of physical exercise and
wearing the core body temperature sensor and
readings will be taken every five to ten minutes.
These readings will then be compared to the ear
thermometer readings and the accuracy of the
developed sensor can be determined.
4. Dehydration Sensor
4,1 Conductance Sensor
The body’s electrolyte excretion through
sweat can also be used as an indicator for
dehydration. Sodium concentration of sweat has
been discovered to range between 30 and 65
mMol/L [6]. As someone exerts energy through
exercise or sport, sodium ions continue to be
released into the sweat, causing the
concentration to increase. Monitoring this
relationship will allow a quantitative analysis as
to the dehydration level of the subject. Sodium
concentration will not be measured directly,
however. Another relationship exists that allows
for electrical analysis of a sweat sample.
onducti ity is the measure of a solution’s ability
to carry current. This capability to carry current
is determined by the presence of ions within the
solution. Therefore as the sweat’s sodium ion
concentration increases, its conductivity does as
well. Furthermore, conductivity possesses a
relationship with resistivity, simply being the
inverse. Understanding these relationships
allows the application of a stimulus with known
current to a sample of sweat, measuring the
voltage drop across the sample, calculating
resistivity of the sample, and inverting that value
in order to obtain conductivity of the sample.
The relationships between sodium
concentration, resistivity, and conductivity will be
applied to our sensor through the design of
circuitry that will be fed into our microcontroller.
Figure 4 presents a block diagram of this design.
Fig. 6. Conductivity sensor block diagram.
The voltage to current converter allows us to
apply a stimulus of known current to our
electrode setup. A four-electrode configuration
will be used where the stimulus is applied to
electrodes 1 and 4 (as labeled in Figure 6), and
the voltage drop will be measured across
electrodes 2 and 3. The dotted red rectangle
refers to the microfluidic device that will be
delivering the sweat samples to the electrodes.
The measured voltage drop will be sent through
an instrumentation amplifier in order to amplify
the voltage difference between electrodes 2 and
3. A low pass filter will be used to eliminate
noise and a gain amplifier will then be used to
further amplify the signal. This signal will then be
sent to our microcontroller where the
calculations will be done to obtain resistivity and,
subsequently, conductivity.
In order to calibrate and test our system,
artificial sweat will be created. A formula
approved by the International Organization for
Standardization (ISO) will be used and solutions
of different sodium concentrations will be made
[16]. These solutions will be applied to our
electrodes and measurements will be taken in
order to confirm the proper function of our
sensor and to calibrate it based on the known
concentrations of the solutions. Our conductivity
sensor, along with the microfluidic device, will be
fitted to a human volunteer subject. The subject
will then run on a treadmill long enough for
sweat to be collected through the microfluidic
device and measurements to be taken with the
conductivity sensor. These measurements will
be compared with our calibrations and threshold
values will be determined that signify dangerous
conditions for the subject wearing the sensor.
4.2 Microfluidics Device
proper channel dimensions, an important
equation must be considered:
(2)
Fig. 7. Overview of PDMS/Platinum Electrode
The conductivity sensor requires a continuous
flow of sweat across the electrodes. This will
consist of a molded silicon channel which will
flow over the electrodes as shown in Figure 7.
This can be accomplished without the need for
pumps or other complicated hardware.
Microfluidics was chosen because of its passive
nature of capillary action. Capillary action is
defined as the ability to move a substance
without the use of another force such as
pressure or gravity. There are two forces
associated with this phenomenon. [4] The first is
surface tension and the second is adhesion.
Liquid forms a meniscus in a capillary which has
a concave shape. The meniscus attempts to
keep its concave shape while the liquid adheres
to the sides of the capillary and pulls the
meniscus with it. In this way, a liquid is able to
move by itself.
Where: Pc is capillary pressure.θB, θT, θL and θR
are the angle with respect to the bottom, top, left
and right of the channel, respectively. The depth
(d) and width (w) are the critical dimensions that
must be experimentally determined. You see ɣ
is the surface tension of the liquid which will first
be characterized using NaCl in solution.
However, as one sweats, their sodium
concentration increases which correlates to
dehydration. This complicates the equation
because as shown in the following figure, the
surface tension increases as sodium
concentration increases.
Fig. 9. Surface Tension of NaCL vs. Surface Tension
Upon further investigation, it was discovered that
once the microfluidic channel is saturated, the
liquid inside will no longer flow. Therefore, our
design includes an absorbent material at the end
of the channels to keep the fluid moving.
Fig. 8. Meniscus of a Liquid
The microfluidic device should have channels
large enough to allow sodium ions to pass
through without clogging, yet small enough so
that capillary action will still occur. This is
because as the diameter decreases, the surface
tension increases and thereby the capillary
pressure increases. This change in pressure is
how we refer to fluid flow. In order to design the
The material that will be used is called PDMS or
Polydimethylsiloxane which is a two part silicon
elastomer which is readily available. It is quick
and inexpensive to fabricate microfluidic
channels which satisfy our constraints of being
disposable. We chose to use PDMS to satisfy
our design constraint in that it must be
disposable. The reason it must be disposable is
because it will end up saturating with sodium
ions from previous testing.
The first step in overcoming this challenge will
be approaching Dr. Selin Arslan of the
department of Mechanical Engineering at
Lawrence Technological University for
assistance with mathematical modeling of the
microfluidics. The next steps include making
various samples of PDMS channels and testing
them with the provided conductance sensor.
Other research includes deciding which material
to use for the microfluidic device.
5. Microcontroller and Software Development
The outputs of the three individual
sensors need to be able be processed and able
to report the condition of the athlete. To
accomplish this, the use of a microcontroller is
needed. The microcontroller chosen for this
project is the ATmega32U4 contained on the
Arduino Leonardo. The Arduino family of
development board was chosen because of the
established protocols and helpful resources to
develop the software needed for this project. To
choose to microcontroller on the Arduino three
main specifications were looked at. The first was
the resolution of the analog to digital converter.
The ATmega32U4 has a 10 bit analog to digital
converter. This gives us a resolution of five
millivolts which allows the device to maintain
adequate accuracy. The second specification
looked at was USB interfacing. The Arduino
Leonardo has USB controllers built into the
board so no software development has to be
done to send the data via a USB connection.
This saves time and allows more focus to be
spent on development of other aspects of the
project. The last specification was the power
supply. A power supply of five volts was needed
because the analog to digital converter and
other sensors use five volts as power supplies
and reference voltages. The manufacturer data
sheet pages are included in the Appendix.
The software for the microcontroller has
two elements. The first are the sub routines.
Each subroutine calls to the sensor and
retrieves the output reading. Based on this
reading, the sub routine then makes a decision
about whether the reading is in the danger
region. Based on the decision the sub routine
gives an output to the main routine. This main
routine takes the output from each subroutine
and makes a decision about the athlete’s
condition. The main routine keeps looping and
asking the sub routines for the state of each
sensor. Once the athlete crosses into the
dangerous physiological state the main routine
then gives an output letting the athlete know
they may be at risk. The flow chart structures on
these routines are shown in Figure 10.
determining the threshold values in order to
effectively monitor the athlete. Other possible
challenges include how the sensors and
integrated circuit devices behave in different
climate conditions such as changing ambient
temperature and humidity. The evaporation of
sweat will be another design parameter that may
possibly have to be addressed. Also the effects
of interference because of the proximity of the
sensors to each other may have to be
considered when designing and testing the
device. A desirable feature of the device is of a
compact size that can be easily incorporated
into the athlete’s gear and not hinder any
performance.
7. Anticipated Costs
The anticipated costs for the project are
shown in Figure 11. The costs were presented
as a range to account for components for which
the exact price is not certain, and for expenses
which we might not need such as the clean
room. The costs range also allows us some
room for error in case of damages or
unexpected costs. However, we expect that our
costs should not reach the top limit of the range.
We received $1,200 funding from LESA Awards
which should completely cover the costs of the
project.
Fig. 10. Main and Sub Routine Flowcharts
Eventually a graphical user interface (GUI) will
be developed for a laptop to view the sensor
readings and the athlete’s condition.
Fig.11. Anticipated Costs Table
6. Anticipated Problems/Challenges
8. Project Timeline & Team Responsibilities
Three areas have been identified as
main challenges. The first is maintaining the
accuracy while simultaneously monitoring three
parameters and checking them against our
predetermined threshold values. The second
challenge will be developing the microcontroller
and software system to control and monitor the
device. The third challenge will be correctly
The project timeline is shown in Figure
13 in the Appendix. Our next step will be to
order the microcontroller and begin initial code
development. Even though the microcontroller
will be used at a later stage in the project, it is
important to become familiar with its use and
software earlier in order to spot potential
problems. Calibrating each individual sensor will
be our next responsibility. To do this, we will
need to purchase all of the necessary
components to build and test the sensors. Heart
rate sensor and electrode circuit calibration are
expected to be the first sensors completed due
to the simplicity of the circuit and components.
The temperature sensor and microfluidic device
have fabrication times which have been
accounted for. The last stages of the project are
final code development of the microcontroller
and user interface, sensor integration, and
calibration of the overall system. This tentative
schedule shows us finishing the project in midApril, giving us a few extra weeks of time to work
with should any problems or delays arise.
Each team member has specific
sections of the project for which they are
responsible for the design, building, and testing.
Wes Bellman is responsible for heart rate
monitor and software, Kevin Mason for the core
body temperature sensor, microcontroller, and
software, Lorraine Novak for the microfluidic
device, microcontroller, and software, and Matt
West for the conductance sensor and
microfluidic device. The division of labor is
intended to increase efficiency by allowing team
members to accomplish individual tasks during
the course of the project. However, much of the
design has been completed by group
collaboration, and team work will be common in
the future stages of the project. Group meetings
are frequent and each team member is expected
to give progress reports and inform of any
individual work completed. In addition, we are
also responsible for reporting progress to our
faculty Dr. Mansoor Nasir and technical advisor
Professor Ken Cook.
9. Future Directions & Implications/Impact
Future development of this device
includes development of a wireless
communication network that would monitor
many athletes from a central monitoring node.
This central node would enable medical teams
to monitor the readings of athletes’ physiological
parameters in real time and assess whether a
specific player needs preventive medical
attention or rest. This monitoring device,
wireless communication network and central
monitoring node could also be used in other
applications, such as firefighters who may also
experience extreme conditions under certain
circumstances. Another application could be in
monitoring construction workers, especially
those working on roads or roofing, who are
exposed to hot weather conditions when working
long shifts in the summer.
With the development of this
physiological monitoring device, athletes can be
monitored and these readings when provided to
the athlete or medical staff can help prevent
heat-related injuries. With a successful
demonstration of a working prototype, future
development into a working device could be
done. This future development is discussed in
the next section.
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Appendix
Fig.12. ECG Schematic
Fig. 13. Project Timeline
Microcontroller Manufacturer Datasheet
Temperature Transducer Manufacturer Datasheet