IoT Project Report.Group 7

ECE:5995 Spring 2016
The Internet of Things
Project Report
Group 7
Benjamin M. Reynolds
Chao Geng
Joseph D. Carr
Yichao Wang

ECE:5995 SPRING 2016, THE INTERNET OF THINGS
COLLEGE OF ENGINEERING, UNIVERSITY OF IOWA
This ﬁnal project was done by group 7, under the supervision of Prof. Jon Kuhl and Prof.
Erwei Bai, within a total of 3 weeks, from Apr. 18th to May 6th of 2016. This project report
was co-authored by group 7 members from Apr. 18th to May 11th of 2016.
Submission, May 11, 2016

Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1 Motivation 5
1.2 Why IoT 6
2 Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 LiveLongTM 7
2.2 Hardware & Software 8
2.3 Security 10
2.4 Analytics 11
2.5 Other Consideration 15
2.5.1 Error from Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5.2 Error Propagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5.3 Conﬁdence Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.5.4 Hidden Pattern and Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.6 Cost 16
2.7 Feasibility/Limitations 17
3 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1 Future & Business Model 19
3.2 Conclusions 20
3.3 Acknowledgements 20

4
4 Responsibilities and Contributions List . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1 Responsibilities and Contributions List 21
5 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

1. Introduction
1.1 Motivation
Our team, group 7 adopt the default project idea. We use Arduino with Bluetooth LE, Raspi,
BME 380 sensor and a heart rate sensor. We need to provide alerts of impending health
crises such as heat exhaustion, heat stroke, and other cardiac-related problems. We need
also provide alerts of impending health crises such as heat exhaustion, heat stroke, and
other cardiac-related problems.
We collect two types of data from the 2 sensors. The heart rate sensor measures human
heart rate, which is an internal human body variable. The bme 380 sensor measures am-
bient temperature, humidity, barometric pressure and altitude, which are external natural
environment variables. Studies show that external natural environment variables, are known
important long-term risk important factors of cardiac disease, and trigger acute cardiovas-
cular events [1–5]. Studies reported positive associations between ambient temperature
and the incident of stroke [1, 6–12].
Barometric pressure variations were found to modulate the occurrence of vascular
events, e.g., oxygen saturation, which is early sign of heart obstructive disease and con-
gestive heart failure [13], acute myocardial infarction [14], ischemic stroke [9], non-lacunar
stroke [15] and hemorrhagic stroke[16].
According to Ref. [3], ambient temperature, humidity, barometric pressure and altitude
can also predict on worker absenteeism [3]. They show severe external weather conditions
have a strong correlation on worker absenteeism. Ref. [3] also argue that the study can
also affect absenteeism at the plant level of analysis. If there are collaboration of different
organizations, research groups on similar data collection and analysis, then a key advantage
of this potential collaboration is that people can predictive forecasting, thereby opening
the possibility of practical, forecasting applications. So cloud based computing might be a
good candidate to do such analytic and forecasts. Also according to Department of Labor,
Occupational Safety and Health Administration, ambient temperature above 40 ◦C may
lead to heat exhaustion, heart stroke and other heat related problems.

6 Chapter 1. Introduction
In daily events, ambient temperature is found to be the most potent predictor for the
the attendance of children at day-care centers [17], have a low inverse correlation with
patients attendance at emergency room for heat exhaustion, heat stroke, and other cardiac-
related problems [18–20]. Studies also suggest ambient temperature, barometric pressure
have important impacts on human behavior [21–25]. Moreover, the relationship of low
barometric pressure with an increase in hospital admissions for depression is one of only
two links between weather and emotional states that has been consistently replicated
[26]. In an industrial setting, work-related accidents were higher during periods of low
barometric pressure [24].
The data provided on ICON for our project is for a person of 50 years old. However,
the heat index is not ambient temperature, actually, according to Ref. [27], is a nonlinear
function of internal human body variables , external natural environment variables and
external human variables (activity, convection from the surface of the skin, et al.). Under the
trivial assumptions of Ref. [27], heat index can be use multiple nonlinear regression analysis
to be only function of ambient temperature and relatively humidity. If we use the heat
index equation in [27], then the data provided on ICON can extend to temperature range
for simulation of white collar office conditions 64◦F → 79◦F in available study [28, 29].
Also, as the extreme high temperature range (e.g. above 95 ◦F) may cause additional
stress for workers, especially those doing manual labor [30–32] and there is a curvilinear
nonlinear relation between temperature and labor performance with hot days showing
the lowest efficiency [22]. Higher temperatures are associated with greater physiological
and psychological distress [3]. Such data has potentially to monitor the health condition of
different occupations too.
1.2 Why IoT
We learned from the lecture that smart healthcare system is a good example of the large-
scale Internet of Things (IoT) applications. Smart healthcare system contains wearable
devices, home monitoring, cloud-based personal analytics, EHR/EMR and aggregated
analytics (big data). As external natural environment variables are known important long-term
risk important factors of cardiac disease, incident of stroke and trigger acute cardiovascular
events [1–12]. Ref. [2] investigates the heart rate and heart rate variability of a clinical
healthy 48 year old man in Kiev with external natural environment variables of a 50 days
long span [2]. Ref. [33] proposed the long term tracking of a patient’s heart rate during
sleep, and they conclude that the long term tracking can be used to detect early changes in
a patient’s health condition and understand the effects of season transitions on the patient’s
health condition. Several studies show that home monitoring reduce the number of hospital
readmissions in congestive heart failure significantly [4]. Long-term monitoring allow a
better treatment and management of patients as well as clinical applications [2, 33]. Thus,
one of the potential use of this project related to smart heathcare system is the long term
monitoring, analytics and warning system for cardiac signal. Due to the complexity of such
problem, the IoT turns out to be a promising candidate in solving the problem.

2. Report
2.1 LiveLongTM
This device will promote independence and peace of mind for both the user and their
loved ones by analyzing your resting heart rate while you sleep providing a warning LED
locally as well as a ﬂag to determine a client at risk. Our system will anticipate and remedy
health complications of the heart by collecting internal and external data enabling the
physicians to properly diagnose the issues prior to the complications reaching a critical
point. LiveLongTM will transmit your data from your device to both android, IoS, and our
web-based platform. Just remember, LiveLongTM and prosper.
Figure 2.1: Get from Ref. [33, Fig 1], this is one of the setup of the current project.
We learned through the semester that the Arduino, Raspi should be a good thing for
long term tracking system. It can make the ﬁnal product small compared to the schematics
showed in Fig. 2.1. The sensor may be wireless too, such as a ring, using solar cell or
temperature difference as a way to generate power. The Bluetooth LE will save power, only
transmitted when required.

8 Chapter 2. Report
2.2 Hardware & Software
Arduino Uno Specs are listed in Table 2.1.
The Arduino Uno works at a specified baud rate 115200. During our development on
the Arduino Uno, we use Serial Monitor to track the output. Arruino Uno also has one
UART hardware port, and we can use that to exchange information with the PC. The specs
of Arduino Uno are listed in Table 2.1.
Type Value
Microcontroller ATmega328P
Operating Voltage 5 V
Input Voltage (recommended) 7 − 12V
Input Voltage (limit) 6 − 20V
Digital I/O Pins 14 (of which 6 provide PWM output)
PWM Digital I/O Pins 6
Analog Input Pins 6
DC Current per I/O Pin 20 mA
DC Current for 3.3 V Pin 50 mA
Flash Memory 32 KB (ATmega328P) of which 0.5 KB used by bootloader
SRAM 2 KB (ATmega328P)
EEPROM 1 KB (ATmega328P)
Clock Speed 16 MHz
Length 68.6 mm
Width 53.4 mm
Weight 25 g
Table 2.1: Arduino Uno Specs, https://www.arduino.cc/en/main/arduinoBoardUno
From the lecture, we know a better definition of the IOT is
Definition. The IOT refers to a virtual representation of a broad variety of objects on the
Internet and their integration into Internet or Web based systems and services.
The Arduino alllows us to interface with the cyberphysical world using a phletoria of
sensors. Though Arduino Uno is very useful debugging for the data collected from sensor,
due to the rather limited processor- and memory-compartment of Arduino Uno, we need to
integrate Arduino into the Internet. One promising candidate is the Linux-based Rasberry
Pi (Raspi). Arduino+Raspi might be the best combination to connect things. Raspi is only of
credit card size, the entire Linux and other open source packages are available, is very easy

2.2 Hardware & Software 9
to connect to the Internet and can be developed using different programming languages.
But Raspi has limitations (http://hardwarefun.com) too.
• No built-in Analog to Digital support
• Can’t run Inductive load (motors)
• Is not real-time (CPU might be busy)
• No "safe circuits" present
• Operators at 3.3 V and is not directly compatible with Arduino default voltage
• Hardware design is not open source
Comparisons of Raspi and Arduino Uno if used alone are shown in Table 2.2.The solution
to exploit the strengths and overcome weakness of both Arduino Uno and Raspi for IOT
applications is to use both together. Fig. 2.2 shows our hardware of our project.
Feature Raspi Arduino Uno
Processor Speed 700 MHz 16 MHz
Programming Language No limit Arduino, C/C++
Real-time Hardware Yes No
A/D Convertor No Yes
Hardware Design Closed sourse Open source
Internet Connection Very easy Not easy
Table 2.2: Raspi vs Arduino Uno for IOT
Figure 2.2: Hardware Setup of the Project
For the hardware setup for our project, we used everything that we learned from the
previous IOT lab, such as how to use Bluetooth LE and how to implementing Firebase

10 Chapter 2. Report
on Raspi. So we use the BME280 and the pulse sensor amped to collect the data we need
to use for the analysis part. Then we connect these two sensors connect to Arduino, we
implement the libraries class from the Adafriut website to collect the data and save them
on the Arduino. Since we want to build a wearable device, so we need a wireless device
that could separate from our Arduino board. Bluetooth LE is really ﬁt for our project so we
use it with a Raspi to communicate with our Arduino(sensors). The basic structure is pretty
straightforward and all we need to do is program the code for connection of Arduino and
Raspi.
For the software setup for our project, we elected to create a website application pro-
grammed in PHP/JS/HTML/CSS that can retrieve Firebase stored values and plot them
on a graph that updates every time a new value is stored. The graphs showing heart rate
and temperature values are created using the Plot.ly API. To access these graphs, a user
must login through his/her Google+ account via Oauth2.0 authorization protocol. We had
to implement Oauth2.0 which was provided by the Google+ API. Oauth2.0 authorization
protocol allows a third-party website app (e.g. LiveLong TM) to access user data without
the user needing to share login credentials.
Figure 2.3: Hardware & Software
2.3 Security
Implemented parts include the Firebase HTTPS SSL encryption (transit) as well as the
Google+ (OAuth 2.0).
Facets of our project that still need implementation are at rest AES Hardware for the
Arduino, AES Bluetooth LE encryption for Arduino to raspberry pi as well as the many
REST API AES packages for iOS, Android, and web based platforms provided through
Firebase. Provided below are graphs displaying what is possible utilizing a ATMEGA2560

2.4 Analytics 11
but due to our limitation with the ATMEGa328p-pu (See Tables 2.2 and 2.3), we were not
able to implement AES Arduino HW given the insufficient SRAM. Our Adafruit Bluetooth
LE shield is not capable of utilizing the AVR-Cryto Library and is only usable by Bluetooth
(4.0 and higher). A possible candidate for a replacement would be the nRF8001 for Bluetooth
transit encryption. Lastly, Firebase provides REST API for iOS, Android, and Web-based
platforms but given our limited time and and inexperience in app development we were
not able to complete the task.
• Arduino AES HW: https://github.com/DavyLandman/AESLib
• nRF8001 AES: https://github.com/cantora/avr-crypto-lib
• Firebase AES: https://www.firebase.com/docs/rest/api
Summary of space available and space used on different type of memories.
Type of Memories Total space available Space used Space Remaining
Flash 256 KB 8.79 KB 247.2 KB
EEPROM 4 KB 0.015 KB 3.98 KB
SRAM 8 KB 1.4 KB 6.8 KB
Table 2.3: Amount of flash, EEPROM and SRAM available/used/remaining. From [34,
Table I]
Speed/time information of AES encryption and decryption on Arduino
Phase Time (ms)
Key setup 0.37
Encryption 0.58 (27.5 kB/s)
Decryption 0.77 (20.5 kB/s)
(a) Block Length 128 bit
Phase Time (ms)
Key setup 0.52
Encryption 0.82 (19.5 kB/s)
Decryption 1.09 (14.5 kB/s)
(b) Block Length 256 bit
Table 2.4: Speed/time information of AES encryption and decryption on Arduino. From
[34, Sec. 3]
2.4 Analytics
We use the conventional heat index (HI) equation given in Ref. [27]
HI(T,R) = − 42.379 + 2.04901523T + 10.14333127R − 0.22475541TR − 6.83783 × 10−3
T2
− 5.481717 × 10−2
R2
+ 1.22874 × 10−3
T2
R + 8.5282 × 10−4
TR2
− 1.99 × 10−6
T2
R2
(2.1)
where T (◦F) the ambient temperature (◦F), and R the relative humidity (%).
We use SVM to find the separation line of the UNSAFE and SAFE groups in the available
data sets. We think this separation line (black line in Fig. 2.4a) doesn’t specify the physical

activity level. As human activity level is a factor affect the heat index and pulse rate [27],
using SVM method again, We group the given data on ICON to more subtle sub groups
(red dot dashed and blue dashed line in Fig. 2.4a), and we believe that these two additional
separation lines describes the hidden variable of different human activity level.
Figure 2.4: (a)The black line is the the separating line required in the ProjectIdea on ICON.
The red dashed line and blue dash dotted line are our interpretation of the sub group
related to heart rate.(b) expression for black line(c) expression for red dot dashed line (c)
expression for blue dashed line
The expressions for the 3 separation lines are in Fig. 2.4a are respectively,
y1 = 666.28 − 6.18x (2.2a)
y2 = 39.09 + 0.96x (2.2b)
y3 = 57.74 + 0.32x (2.2c)
where yi is the heart rate (bpm) and x the heat index calculated from Eq. (2.1).
These linear expressions can be used to identify the sub groups in Fig. 2.4a. As in reality,
if people are at rest or light activity, it should be under the blue dash dotted line in Fig. 2.4a.
If people are doing moderate exercise, under big working pressure, or in emotional mood,

2.4 Analytics 13
then we expect people are between blue dash dotted line and red dashed line Fig. 2.4a. If
people are over exercising, then we expect people are above red dashed line in Fig. 2.4a. As
our primary goal is to build a long time home monitor, especially during sleep. Under such
scenario, we are able to ﬁnd evidence of one hidden variable in the heat index, the human
activity level. We make the assumptions that the normal heart beat of a patient should be
under the red dot dashed line. So for our model, the safe region for a home monitor for the
purpose of long time tracking during sleep should be the intersection of SAFE region and
the activity level 0 region. The interpretation of Fig. 2.4a actually base on location, what
kind of activities, people’s emotional feeling and other factors. So at least, if we’re able to
collect location information based on user’s consent, then this project can be use to monitor
a senior’s physical exercising, working conditions, and even help policy making process.
(a) (b) (c)
(d) (e) (f)
Figure 2.5: density function analytic of the nonlinear relation of heat index, ambient
temperature, relative humidity, heart rate. (a) density plot of heat index as a function of
ambient temperature, relative humidity; (b) Nonlinear surface of the heat index vs ambient
temperature and relative humidity; (c)-(f) Density plot of Unsafe region, activity level 0 to 3
as deﬁned in Fig. 2.4a, with horizontal axis the ambient temperature (◦F), longitudinal axis
the relative humidity (%) and vertical axis the heart rate (bpm).
As heat index HI is a nonlinear function of ambient temperature and relative humidity,
in Fig. 2.5, we plot the density plot of the HI(T,R) and the three separation lines in a 3D
space. Figs. 2.5a and 2.5b is in consistent with the daily fact that high ambient temperature,

combined with high relatively humidity are unsafe, as shown in Fig. 2.5c. Similarly, for
the same temperature and humidity, with the increase of human activity level, heart beat
generally increases as shown in Figs. 2.5d to 2.5f.
(a) (b) (c)
(d) (e) (f)
Figure 2.6: Region analytic of the nonlinear relation of ambient temperature, relative
humidity and heart rate. (a)-(f) Sub Regions defined in Fig. 2.4a, with horizontal axis the
ambient temperature (◦F), longitudinal axis the relative humidity (%) and vertical axis the
heart rate (bpm).
Fig. 2.6 maps the 6 sub-regions defined in Fig. 2.4a from the data analytic variables
domain to the sensor data variables domain. The regions in Fig. 2.6 helps us to hard coded
some initial evaluation and warning criteria in our Arduino Uno & Raspi side. We give
a flag −1 for not collecting heart rate, flags 0 − 5 for the 6 regions shown in Fig. 2.6. We
introduce the local analytics with different values of flags for the long term sleep monitor
system. We consider data collected from the hardware part (heart rate, ambient temperature,
relative humidity) in region of Fig. 2.6a be safe (flag value 0), with a local green led status.
Any conditions beyond Fig. 2.6a is considered unnormal (flags values 1-5) and returns
a local red led warning. We calculate the running average locally and send the running
average to the cloud (e.g.Firebase) with a predefined time interval ∆tcloud.

2.5 Other Consideration 15
2.5 Other Consideration
There’re several issues with the IOT platform. One of the most important questions to be
asked is "How reliable are the data collected ?". There’re several factors that needs to take
consideration.
In our project, we calculate the running average on the hardware side to reduce the
error. However, we need to evaluate the data collected from the sensors to test some
hypothesis because the data have random fluctuations due to lack of complete control over
the measurement conditions in our future development of the prototype of our project. We
attempt to use the overall statistics, probability theory and signal processing perspective
to estimate the mean value and try the qualitatively and semi-quantitatively description
of the and variance of the distributions from which the data collected, and to generalize
properties valid for a data to the rest of the measurement events at a prescribed confidence
level. Any assumption about an unknown probability distribution is called a statistical
hypothesis. The concepts of tests and confidence intervals are among the most important
developments of statistics [35].
2.5.1 Error from Sensor
We make the statistical hypothesis that every data collected from each sensor can be treated
as an independent measurement. While we test our project, we notice that the heart rate
sensor returns value ranging from 35 → 200 if we don’t use the sensor at all. That’s a big
issue. Possible reason caused this might be that we use I2C wiring scheme, and the I2C
wiring wasn’t set up properly to minimize the error. The pulse sensor was connected to
the analog input port 1 in Arduino. We used the open source Adafruit_Sensor.h to read
heart rate sensor. We just connect one pin of the sensor to the GNR pin of Arduino, but
we don’t know if Arduino GND is truly grounded when connecting to the PC via a USB
cable. Also there might be additional considerations from the signal processing as well.
The heart rate data collected should be regarded as analog signal. But actually with the
baud rate, we actually sampling the analog signal to generate digital input. According to
Ref. [33], signal processing of the raw data collected from sensor are important. By doing
this, one can reconstruct the pulse waveform, improve SNR, identify hidden patterns. Such
discussion is out of the scope of the class, and we don’t delve into more detailed analysis.
2.5.2 Error Propagation
The heat index equation we used in Eq. (2.1) actually has an error of ±1.3◦F and best fitting
range [27]. From Ref. [35], the error propagation for a f (x,y) is
f ¯x ± σx, ¯y ± σy = f ( ¯x, ¯y) ± ∂x f |¯x, ¯y
2
σ2
x + ∂y f |¯x, ¯y
2
σ2
y
1/2
(2.3)
The heat index value is actually estimated of a known function Eq. (2.1), and it already
has an error distribution. We cannot ignore other external environmental variables when
study the heart rate. There are hidden variables that could exists in the data collected and
those hidden variables are nonlinear functions of the distribution and sensitive to noises
[2, 33].From ??, we already come up with the hidden variable, the human activity level
using SVM method. We suspect under other situations, altitude, barometric pressure and
other unknown variables (e.g.urban environment pollution, the sedentary city life with less

exercise, increasing mental stress and poor diet from [33]) may affect the nonlinear heat
index equations. If combined with the error propagation from the external environmental
variables and other potentially hidden variables, then developers and researchers need to
take a more serious way when use IOT for their solutions.
2.5.3 Confidence Interval
If we make the assumption every data collected from each sensor can be treated as an
independent measurement. If we are given a distribution function P(∆xn = xn − xn−1), with
range [0,1], where xn the n-th measurement. Then the probability qm that m consecutive
measures are not reliable can be expressed as the following:
qm =
m
∏
n=1
[1 − p(∆xn)] = [1 − p(∆x1)][1 − p(∆x2)][1 − p(∆x3)]···[1 − p(∆xm)] (2.4)
from t0 → t0 + m∆t. The cloud may find a threshold value from big data analytics, if qm is
below the threshold, the cloud considers the data not reliable. The procedure discussed
above might be used to model the confidence interval of the data collected from the sensors.
2.5.4 Hidden Pattern and Cloud Computing
If we want to introduce cloud based analytics and warning system, sending alert to service
providers, the cloud must evaluate the validity of the data reported from the hardware. If
the amount of the data is adequate, then the cloud computing might identify and model the
hidden patterns that is crucial to the assessment of the data. If without further assessment
of the data and just barely send request to professional and 3rd party the information, that
may result causing fate alert, waste of resource and other potential issues. In the interest of
brevity, we won’t discuss how cloud Computing may solve these problem as such topic is
out of the scope of the class.
2.6 Cost
Below is the table that listed all the components we used for our project, the total cost is
88.28 for our single device. To be honestly, it’s pretty expensive compare some existing
wearable devices like Fitbit, Jwabone, Nike Fuelband with more advanced features on them.
But if we can start to manufacture our project as a realistic product, we believe it’s relatively
easy to reduce the total price for thousand order from some foundry

2.7 Feasibility/Limitations 17
Parts Price (USD)
ATmega88-20 AUR 3.00
Pulse Sensor Amped 25.00
Adafruit BME280 19.95
PCB ∼ 20.00
nRF8001 (BLE 4.0) 19.95
Mold (PP/ABS) ∼ 50kg
Total Cost 88.28
Table 2.5: Estimated cost of the hardware of the project. However, price of the PCB is
inflated; price of mold is neglected; price of data analysis is omitted; Cost of alert, external
care and other services are not counted
2.7 Feasibility/Limitations
To implement the libraries provided in the security slides we would require the nRF8001
(BLE 4.0). We would also need to protect our intellectual property by utilizing ATMEL
datasheets to flash or burn our sketch onto the chip and lock the bits. We would also
need to create a REST encryption for the wearable device as well as the mobile devices
utilizing the devices data to ensure protection of data from insurance companies. We
would need an upgrade from our existing ATMega328-PI to a ATMega2560 as well as a
designer for a injection old for our wireless finger module which will have the nRF8001,
ATMega2560, and power supply soldered onto a printed circuit board. We would also need
to calibrate each user based on interruptions during sleep as well as accuracy problems
with the pulseoximetor.
Possible inaccurate results for the pulseoximtor are as follows:
• Indifference towards hemoglobin in terms of oxygen and carbon monoxide
• External Interference
• Irregular signals
• Blood Volume Deficiency
• Hemoglobin Deficiency
• Methemoglobin
Along with calibrating for sleep interruptions (Fig. 2.7), there are clearly a number
of other calibrations for each client. First, we would need to determine the percentage
of carbon monoxide in their blood and whether this varies due to their smoking habits.
We would also need to solve the problem we normally ran into which was both external
interference (light) and irregular signals due to the movement of the pulseoximeter. Both of
these would be fixed by the designer of the injection mold which would block our light
as well as firmly fit on the clients finger. Using the BME280 sensor we would send data
to the NEST to determine whether the specific room your in either needs the door open
(automated) or simply to turn the temperature up to account for poor air circulation of the
house. Due to the reliance of hemoglobin in measuring heart rate for the pulseoximeter we

would need to conduct health tests to determine if the client has anemia or a high percentage
of methemoglobin. Normally a person has only two percentage of their hemoglobin as
methemoglobin but chemical exposure can create this oxygenless hemoglobin which can
skew the readings. The last and most important aspect of our project is cutting down
on cost. We clearly need to do more research on downscaling our system to be a more
affordable and look towards alternative sensors and platforms.
Figure 2.7: Calibration for sleep interruptions

3. Concluding Remarks
3.1 Future & Business Model
We can apply this prototype beyond the smart healthcare system for IoT applications. Some
concepts of this prototype project may be fit into the following IoT solutions as well as an
available commercial products.
• an alternative to people’s personal training coach if they do jogging or other exercise.
They can used to track and record people’s performance over time and give better
training plan
• It can used in the training of pilot. As the ambient temperature, humidity, barometric
pressure and altitude can affect a pilot performance, this project might help with the
training of pilot or other specialized training program.
• Use to protect worker’s working condition. If employers force employees to work
under prohibited condition defined in federal law and other regulations, then the
employees may use this as a proof for further legal action.
• If allowed a wide collection of data depends on age, occupation, geolocation, policy
makers, insurance company, health care providers and hospitals can make forecast
based on different scenario for different group.
• With the global warming, and the higher chance of severe weather conditions. Re-
searchers can use cloud based data from this medical side to study global warning
impact on health related issue. People can collaborate together to fight the global
warming. There might be also a creation of potential new financial exchange market,
like how carbon exchange market related to CO2.
• Can use to make better man management for companies. As Ref. [3], worker ab-
senteeism depends on the external variables. It’s possible to use big cloud data to
optimize the working condition to ensure best efficiency.
• ···
This will requere a lot of work, but hey it will be worthy and fun!
• Bioinformatics and computer science

20 Chapter 3. Concluding Remarks
– Data minig
– Machine Learning
– Big Data Analysis
– Neural Networks
– Visualization Resources
• Statistics and Signal Processing
– Probability Density Function
– Point Spread Function
– Full width at half maximum
– Convolution
• Hidden Patterns Recognizance and Nonlineardynamics
– SVM and other nonlinear regression methods
– Identify, category and quantify hidden patterns
– Quantify the hidden patterns
– Nonlineardynamics of sophisticated function
• Sensor
– New type of power source
– Moor’s Law, new architecture
– New biophysics sensor
– Improved algorithm for better precision
• Social
– Cooperation from different organizations and countries
– Legal and Privacy Issues
– Main Stream Recolonization
– Professional Training
3.2 Conclusions
In this report, We have discuss the hardware & software setup, security issues, analytics,
feasibility/limitation of our project. We show that our project is a promising candidate for
the IoT solution of long term health monitor tracking of a patient. We perform the detailed
discussions on the security, analytics and limitation of our current hardware & software
structure. Under further development, our project and the concept discussed in this report
can be commercialized for a variety of IoT applications.
3.3 Acknowledgements
We’d like to acknowledge extend our heartfelt gratitude to Prof. Jon Kuhl and Prof. Erwei
Bai, for their guidance, encouragement and support to us throughout the semester. We’d
also like to thank our peer classmates who help us in the development of our project.

4. Responsibilities and Contributions List
4.1 Responsibilities and Contributions List
Responsibilities and Contributions from each member of Group 7:
Benjamin M. Reynolds
Benjamin was responsible for creating the web application that hosts the temperature
and heart rate graph APIs, as well as implementing an Oauth 2.0 authorization protocol
via Google+ API. He developed the website using the XAMPP web development tool
for ofﬂine development in PHP, Javascript, and HTML, as well as integrating Bootstrap
CSS for website layout and navigation. He also was involved in sensor data transmission
from Arduino to Raspi, Raspi to Firebase, and Firebase to Web application. He also wrote
Section 2.3 in the project report.
Chao Geng
Chao did the most of the hardware part of the project.He soldered the sensors and combined
the two libraries for the sensor that can works for Arduino, he also did the communication
part of transmitting data from Arduino to Raspi and the uploading Firebase part. He also
wrote part of the powerpoint slides and the project report.
Joseph D. Carr
Worked in line with Benjamin to manipulate the Bluetooth shield and Nordic library
to send data from the Arduino to Raspberry Pi, and Raspberry Pi through ethernet to
Firebase. Created all slides and scripts for slides aside from the analytics created by Yichao.
Researched a signiﬁcant amount of future feasibility and limitations of the home monitor
should a future IoT team decide to develop this project with encryption for transit and
at rest for both iOS, Android, and a web-based platform. He wrote Sections 2.1, 2.3, 2.6
and 2.7 of the project report.
Yichao Wang
Yichao did most of the literature research of the project and the analytic part of the project.
Based on his literature research and analytic analysis, he thought about the potential

22 Chapter 4. Responsibilities and Contributions List
applications of the IOT project for the team.
During the project development, He involved in the design of hardware structure. He
also helps with the coding with collecting sensor data from Arduino Uno, communication
between Arduino & Raspi via Bluetooth LE. He was responsible to design the notiﬁcation,
Firebase data structure, the integration of the Arduino Uno and Raspi code to reach the
project goal. He also helped Benjamin with the design of the webpage for the real time
monitor of the data collected.
He wrote the analytics, considerations of the data collected in the project presentation.
He was the main author of the project report, he wrote Chapter 1, hardware part before
Fig. 2.2 of Section 2.2, and all of Sections 2.4 and 2.5 and Chapter 3.

5. References
References
[1] A. Delyukov, Y. Gorgo, G. Cornélissen, K. Otsuka, and F. Halberg, Biomedicine &
Pharmacotherapy 55, s84 (2000).
[2] A. Delyukov, Y. Gorgo, G. Cornelissen, K. Otsuka, and F. Halberg, International Journal
of Biometeorology 45, 90 (2001).
[3] S. E. Markham and I. S. Markham, International Journal of Biometeorology 49, 317
(2005).
[4] R. Goya-Esteban, I. Mora-Jimenez, J. L. Rojo-Alvarez, O. Barquero-Perez, F. J. Pastor-
Perez, S. Manzano-Fernandez, D. A. Pascual-Figal, and A. Garcia-Alberola, Biomedical
Engineering, IEEE Transactions on 57, 1366 (2010).
[5] D. Shaposhnikov, B. Revich, Y. Gurﬁnkel, and E. Naumova, International Journal of
Biometeorology 58, 799 (2014).
[6] Y.-C. Hong, J.-H. Rha, J.-T. Lee, E.-H. Ha, H.-J. Kwon, and H. Kim, Epidemiology 14,
473 (2003).
[7] C. Kyobutungi, A. Grau, G. Stieglbauer, and H. Becher, European Journal of Epidemi-
ology 20, 693 (2005).
[8] T. Messner, V. Lundberg, and B. Wikström, International Journal of Circumpolar
Health 61 (2002).
[9] M. Morabito, A. Crisci, R. Vallorani, P. A. Modesti, G. F. Gensini, and S. Orlandini,
Stroke 42, 593 (2011).
[10] F. M. S. Coelho, B. F. C. d. Santos, M. Cendoroglo Neto, L. F. Lisboa, A. S. Cypriano,
T. O. Lopes, M. J. d. Miranda, A. M. H. Ávila, J. B. Alonso, and H. S. Pinto, Arquivos
de neuro-Psiquiatria 68, 242 (2010).
[11] F.-C. Sung, L.-Y. Huang, Y.-L. Huang, and Y.-C. Wang, “Population-based study
on risk of stroke associated with temperature change,” in “23th ISEE Conference.
http://ehp03.niehs. nih.gov/isee/PDF/isee11Abstract00336.pdf.”, , vol. 20 (2013),
vol. 20.

24 Chapter 5. References
[12] Y.-H. Lim and H. Kim, Epidemiology 22, S13 (2011).
[13] M. Goldberg, N. Giannetti, R. Burnett, N. Mayo, M. Valois, and J. Brophy, Occupational
and Environmental Medicine 65, 659 (2008).
[14] P. D. Houck, J. E. Lethen, M. W. Riggs, D. S. Gantt, and G. J. Dehmer, The American
Journal of Cardiology 96, 45 (2005).
[15] E. Cuadrado-Godia, I. Subirana, and J. Roquer, Cerebrovasc Dis 26, 348354Johnston
(2008).
[16] J. Dawson, C. Weir, F. Wright, C. Bryden, S. Aslanyan, K. Lees, W. Bird, and M. Walters,
Acta Neurologica Scandinavica 117, 85 (2008).
[17] R. Sennerstam and K. Moberg, Public health 118, 349 (2004).
[18] O. Rossi, V. Kinnula, J. Tienari, and E. Huhti, Thorax 48, 244 (1993).
[19] L. B. L. Estela, International Journal of Biometeorology 42, 77 (1998).
[20] T. Makie, M. Harada, N. Kinukawa, H. Toyoshiba, T. Yamanaka, T. Nakamura,
M. Sakamoto, and Y. Nose, International Journal of Biometeorology 46, 38 (2002).
[21] H. E. Landsberg, Weather and Health: An Introduction to Biometeorology (Doubleday,
1969).
[22] J. R. Mather, Climatology: Fundamentals and Applications (Macgraw hill, 1975).
[23] D. M. Ludlum, “Presidential weather,” in “The Weather Factor,” (Springer, 1984), pp.
100–151.
[24] M. A. Persinger and M. Nolan, Perceptual and Motor Skills 59, 719 (1984).
[25] J. Rotton and E. G. Cohn, Handbook of Environmental Psychology. 2nd ed. New York,
NY: Wiley pp. 481–498 (2002).
[26] D. M. Driscoll and D. N. Stillman, International Journal of Biometeorology 47, 21
(2002).
[27] L. P. Rothfusz, National Weather Service Technical Attachment (SR 90–23) (1990).
[28] S. C. Vickroy, J. B. Shaw, and C. D. Fisher, Journal of Applied Psychology 67, 97 (1982).
[29] P. A. Hancock, The Journal of General Psychology 120, 197 (1993).
[30] O. Axelson, Work, Environment, Health 11, 94 (1973).
[31] P. A. Bell, D. B. Garnand, and D. Heath, The Journal of General Psychology 110, 197
(1984).
[32] C. ANDERSON and K. DENEVE, Psychological Bulletin 111, 347 (1992).
[33] Y. Chen and W. Chen, Annals of Biomedical Engineering 39, 2922 (2011).
[34] P. Gupta, “Implementing security in a personal security device,” Ph.D. thesis, The
University of California Los Angeles (2013).
[35] G. B. Arfken, H. J. Weber, and F. E. Harris, Mathematical Methods for Physicists (Academic
Press, Boston, 2013), 7th ed.

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