Wearable electronic sensors for vital sign monitoring€¦ · Wearable Electronic Sensors for vital sign monitoring Mémoire Marouen Besrour Sous la direction de : Michel Duguay,

Wearable Electronic Sensors for vital sign monitoring

Mémoire

Marouen Besrour

Maîtrise en génie électrique

Maître ès sciences (M. Sc.)

Québec, Canada

© Marouen Besrour, 2018

Wearable Electronic Sensors for vital sign monitoring

Mémoire

Marouen Besrour

Sous la direction de :

Michel Duguay, directeur de recherche

Younès Messaddeq, codirecteur de recherche

iii

Résumé

On propose dans ce mémoire un nouveau type de capteur pour la mesure des fonctions

respiratoires et cardiaques à des fins médicales. Le système offre la possibilité de mesurer

le rythme respiratoire et la profondeur de respiration et de transmettre les données vers une

station locale pour une analyse plus poussé et un diagnostic. Le capteur proposé est basé

sur une approche électromagnétique où on utilise deux antennes posées sur la cage

thoracique du patient. Lorsque le patient inspire et expire l’air avec ses poumons, le

diamètre de la cage thoracique de ce dernier va augmenter et par conséquent la distance

entre les deux antennes aussi. Le système mesure l’écart relatif entre les deux pour extraire

le rythme respiratoire. Le point clé du capteur est d’encoder le signal de respiration sous

forme de différence de phase entre l’onde émise et l’onde reçue conférant au système une

bonne immunité contre les bruits des signaux externes.

Le design a été implémenté sur un PCB (46mm x 46mm) pour fournir une preuve de

concept de la méthode proposée. Les tests ont été conduits sur trois sujets de deux sexes et

d’âges distincts. Les données mesurées démontrent que le système fonctionne sur

différentes morphologies physiques. Finalement, le capteur a été capable de recueillir avec

grande précision le rythme respiratoire et même la fréquence cardiaque.

iv

Abstract

We propose in this project a wearable electronic Patch Radar sensor that can monitor

respiration rate and respiration depth continuously in real-time and transmit data to a base

station for analysis. The device relies on a two-antenna configuration. Both antennas are

bent to the patient chest, and when the patient breathes, the mechanical movement of the

chest wall changes the distance between them. The system measures the relative distance

between the antennas to extract the respiration pattern. The key feature of the sensor is that

it transduces respiration movements to phase shifts in RF wave signals which make it very

robust against external interferences.

The design was implemented on a PCB (46mm x 46mm) to demonstrate a proof of concept

for the proposed device. The system was able to acquire respiration signals and even

cardiac frequency. Experimental results are presented for three different subjects, an adult

male and female and a child. The data gathered gives enough sensitivity and accuracy to

state that the device can work with different physical morphologies.

v

Table of Contents

Résumé ....................................................................................................................... iii

Abstract ...................................................................................................................... iv

Table of Content ......................................................................................... v

Table of Figures ..................................................................................................... viii

List of Acronyms ..................................................................................................... xi

Acknowledgements ............................................................................................... xiii

General Introduction ............................................................................................. 1

1 Introduction and State of the Art of Wearable Electronic Sensors ...3

1.1 Social Challenges for Wearable Electronic Sensors .............................. 4

1.2 State of the Art for Wearable Electronic Sensors ................................... 5

1.2.1 Wearable Electronic Sensors Worldwide Initiatives .....................................6

1.2.2 Near Field Communication NFC based monitoring systems ........................9

1.2.3 IR-UWB and SoC CMOS UWB .................................................................10

1.2.4 Multi-material Fiber Antenna ...................................................................... 13

1.3 Chronic Obstructive Pulmonary Diseases COPD Financial

Burden ................................................................................................................. 16

1.4 Project Motivations and Impacts ............................................................. 21

2 Patch Radar System: Apparatus and Methods ......................................22

2.1 Wearable Electronic Sensors ......................................................................22

vi

2.2 Design Specifications ...................................................................................24

2.3 Impulse-Radar Ultra Wide Band IR-UWB Principle ..........................25

2.4 Proposed Solution ..........................................................................................26

2.5 Sensing Principle ........................................................................................... 27

2.6 Mathematical Model ..................................................................................... 28

2.7 Apparatus Design ..........................................................................................32

2.7.1 Central Processing Unit CPU ......................................................................33

2.7.2 Phase Detector .............................................................................................34

2.7.3 Wireless Transmitter ....................................................................................35

3 System Implementation and Experimental Results ..............................37

3.1 Apparatus PCB Layout ................................................................................37

3.2 Sensing Head Antennas ...............................................................................39

3.2.1 Wide Band Antenna S11 Parameter .........................................................41

3.2.2 Wide Band Antenna S12 Parameter .........................................................42

3.3 Sensor characterization ................................................................................43

3.4 Experimental Setup .......................................................................................46

3.5 Respiratory Rate Data ..................................................................................49

3.5.1 Adult Male Subject ..................................................................................51

3.5.2 Adult Female Subject ...............................................................................52

3.5.3 Child Subject ............................................................................................53

General Conclusion .............................................................................................. 55

vii

Perspectives .............................................................................................................. 56

References ................................................................................................................. 57

viii

Table of Figures Figure 1: System overview: AMON; wrist-worn medical device with GSM/UMTS link

to the TMC [3] .....................................................................................................................7

Figure 2: Arrangement of the ECG, respiration, and thoracic oximetry sensors for BioTex prototype [5]............................................................................................................8

Figure 3: NFC based bellyband [33] .............................................................................10

Figure 4: Block diagram of the correlation-based radar demonstrator [42] ..................11

Figure 5: (a) SoC UWB Pulse Radar System Architecture (b) Micrograph of the radar chip [43] .............................................................................................................................12

Figure 6: (a) Schematic and radii of the hollow-core polymer-clad silica fibers with a deposited thin (~150 nm) film of silver used to fabricate dipole antennas, (b) fiber antenna design for 2.4 GHz, and (c) fiber antenna weaved into a cotton fabric with minimally-invasive attributes [52]. ......................................................................................................13

Figure 7: Prototype of the respiration sensor based on a multi-material fiber [51] ......14

Figure 8: Schematic representation of the multi-material fibers spiral antenna integrated into a smart textile and experimental setup........................................................................15

Figure 9: Global magnitude of deaths from non-communicable diseases [22] ............16

Figure 10: Mental health and cardiovascular diseases are top drivers of lost output Breakdown of NCD cost by disease type [24] ...................................................................17

Figure 11: Chronic Obstructive Pulmonary Disease COPD health care costs will double from 2010 to 2030 [24] ......................................................................................................18

Figure 12: 80% of Syrian Refugees have access to smartphones and the internet[26].19

Figure 13: U.S. medical electronics market, by application, 2013 - 2024 (USD Million) [32] .....................................................................................................................................20

Figure 14: General System Blocs Diagram of Wearable Electronic Sensors ...............22

Figure 15: Impulse Radar UWB Sensing Principle ......................................................25

Figure 16: Sensor Placement on the Chest ....................................................................26

Figure 17: Spatial Phase Shifting Principle ..................................................................27

ix

Figure 18: Mathematical Modeling of the thoracic cavity ............................................28

Figure 19: Mathematical Modeling of the Lungs during breathing ..............................29

Figure 20: Thoracic Wall expansion modeling in distance and phase shift ..................30

Figure 21: Phase shift modeling From the PCB lines to the chest wall ........................31

Figure 22: Patch Radar Sensor Blocs Diagram .............................................................32

Figure 23: PIC18F13K22 CPU Blocs Diagram ............................................................33

Figure 24: AD8203 Phase detector (a) Transfer Curve (b) Control circuitry to set conversion ratio ..................................................................................................................34

Figure 25: Xbee Pro S2C transceiver ............................................................................35

Figure 26: Printed Circuit Board PCB Layout design ..................................................37

Figure 27: Printed Circuit Board PCB Photo ................................................................38

Figure 28: Wide Band Antenna Sensing Head .............................................................39

Figure 29: Incident and reflected waves in a two-port network [53] ............................39

Figure 30: Wide Band Antenna S11 Parameter ............................................................41

Figure 31: Wide Band Antenna S12 Parameter ............................................................42

Figure 32: Sensor Linearity Regression ........................................................................44

Figure 33: Sensor Implementation ................................................................................46

Figure 34: Sensor Position on the chest ........................................................................47

Figure 35: Experimental Setup with sensing head bent on subject chest .....................48

Figure 36: Artificial Breathing rate in time domain with three distinct phases of quiet, shallow and quiet breathing ...............................................................................................49

Figure 37: Male Adult Breathing rate in time domain with three distinct phases of quiet, shallow and soft breathing .................................................................................................50

Figure 38: Male Adult Breathing rate in time domain with three distinct phases of quiet, hold and soft breathing.......................................................................................................50

x

Figure 39: Male Adult Breathing rate in time domain in four positions Standing, Sitting, Walking and Lying with varying breathing cycles of Shallow, Apnea, Shallow and Quiet ...................................................................................................................................51

Figure 40: Female Adult Breathing rate in time domain in four positions Standing, Sitting, Walking and Lying with varying breathing cycles of Shallow, Apnea, Shallow and Quiet ...................................................................................................................................52

Figure 41: Male Adolescent [18-25 years old] Breathing rate in time domain in four positions Standing, Sitting, Walking and Lying with varying breathing cycles of Shallow, Apnea, Shallow and Quiet .................................................................................................53

xi

List of Acronyms

WES Wearable Electronic Sensors

MEMS Micro-Electrical Mechanical System

CPU Central Processing Unit

NFC Near Field Communication

ÙWB Ultra Wide Band

RFID Radio-Frequency Identification

RSSI Received Signal Strength Indicator

RR Respiratory Rate

HR Heart Rate

BT Body Temperature

BP Blood Pressure

IPG Impulse Generator

CMOS Complementary Metal Oxide Semiconductor

GDP Gross Domestic Product

NCD Non-Communicable Diseases

COPD Chronic Obstructive Pulmonary Disease

LMIC Low/Middle Income Countries

CDC Centers for Diseases Control and prevention

SpO2 Peripheral Capillary Oxygen saturation

CAGR Calculated Annual Growth Rate

xii

FFT Fast Fourier Transform

EM Electro Magnetic

PCB Printed Circuit Board

DC Direct Current

RF Radio Frequency

ADC Analog to Digital Converter

ASIC Application Specific Integrated Circuit

UART Universal Asynchronous Receiver-transmitter

ASCII American Standard Code for Information Interchange

SPI Serial Peripheral Interface

SMT Surface Mount Technology

SMA SubMiniature version A semi-precision coaxial RF connectors

VNA Vector Network Analyzer

xiii

Acknowledgements

I would like to thank Allah for giving a chance and the power to achieve all that I have

today in my life and more.

I would like to dedicate this work to my parents and friends who supported me on every

level through this project, emotionally and through every step. I cannot express enough

how grateful I am to have you.

Finally, I would like to thank Prof. Michel Duguay who accepted to supervise my work

and who guided my efforts all along this project and from which I had the chance to learn

a lot during the last two years. I would also like to thank Prof. Younés Messaddeq for

accepting to be my co-supervisor on this mémoire and for his support through the work

done in this project.

1

General Introduction

Humanity witnesses an unprecedented technological breakthrough in all significant

scientific fields since the last two centuries.

These technological advancements made possible many wonderful inventions we find in

our everyday life going from calculators and smartphones to satellites. One of the major

fact that contributes the most to this revolution is the breakthrough in electronics devices

and microcomputers.

Nowadays, hospitals are equipped with complex electronics equipment providing

physicians the most efficient tools to offer the best medical care possible. We certainly

can support the affirmation that it probably would not be the case without the

aforementioned development in electronics and microelectronics chips.

As a relatively indirect consequence, we noticed an increase in quality of life since the

last 50 years, which yielded an unprecedented population growth rate all over human

known historical records, consequently leading to an elderly population and rises new

health issues and needs.

According to Lutz and al, the current world population is likely to reach 10 billion by the

next three decades. The elderly population in New Zealand (65+)have been significantly

growing in the last century ranging from 200,000 in 1951 to an estimated 1,400,000 in

2051 [1]. Comparing to the overall population, this represents a 10% growth. If we

assume the same or close quality of life in many other developed countries, we may very

well experience the same trends, which, here we mark, represent or will represent very

soon a challenge and an opportunity to develop new health institutions and systems.

Elderly people have tendency to live in their homes and thus we shall find continuous

real-time monitoring of their well-being and health very useful especially with automated

tools as portable electronic devices.

2

In addition, embedded electronics systems can be bent to patients to acquire and save or

monitor their health state providing physicians with an entirely new set of continuous

data recorded over a relatively wide range of time, unlike standard medical procedures

are undertaken over short time examinations. The least we can state is that the real-time

recorded data gathered continuously provides more valuable, rich and consistent

information about the patient.

For these reasons, Wearable Electronics Sensors WES present a real opportunity to

achieve continuous health monitoring. More specifically, we are operating at the

intersection of two disciplines, Electronics and Medical Care to build tools and new

therapies for new health practices and services.

Despite their tremendous potential to impact our lives, wearable electronics sensors still

face many hurdles. Many prototypes and products are available on the market, but still,

the spread is not as wide as awaited. This proves again the previous point and which

should be dealt with at the very first step of any wearable sensor design.

3

Chapter 1

Introduction and State of the Art of Wearable Electronic

Sensors

Introduction

Wearable Electronics Sensors are bendable devices that can be attached to humans to

measure, acquire and monitor physiological parameters of interest. The development of

such systems has been accelerated by the prominent growth in the world’s population with

an elderly majority. Consequently, this gives rise to new health needs. Indeed, people with

chronic health issues and the elderly need continuous attention thus promoting the 24/7

monitoring necessity. Since continuous hospitalization is undesirable and too costly, new

solutions have to be designed and built. This points the urgent need to develop wearable

electronics sensors that allow a wide variety of applications. In fact, on a random normal

day, hospitals are crowded with non-emergency patients. The automation of preliminary

diagnoses (Assessing Vital Signs condition) may be beneficial to medical staff in terms of

time and available human resources.

Health problems occur over an extended time frame before a significant symptom emerges.

This can be the result and consequence of bad health habits or lifestyle like smoking, eating

non-healthy food or not practicing any physical activity. Monitoring a person’s daily

activity or vital signs variability makes it possible to determine his lifestyle and judge

whether or not it is healthy. In the contrary case, a physician can propose adjustments and

still monitor the progress of the patient continuously and non-invasively while the patient

carries on his everyday life activities which will considerably decrease the probability of

health issues for the patient without compromising his comfort.

The same can be said for many other health problems. Before a significant cardiovascular

or respiratory problem takes place, some minor symptoms show up as heart rate variability

pattern or respiratory pattern. Daily Continuous monitoring can allow to track down these

4

specific symptoms and anticipate a major cardiac or respiratory attack by delivering a pre-

emptive care to the patient which will save a large part of the health budget, billions of

dollars which can be redirected to educational and preventive campaign to inform the

public and the population about healthier habits and lifestyle and the usefulness of

continuous health monitoring.

The UK rugby team uses the same pre-emptive approach to reduce the injury risks of its

players. In fact, two accelerometers/pressure sensors are bonded to both legs of the player

and a physician monitors analyzes their gait pattern to determine if they balance their

weight equally on both feet. If not, the risk that the player will be injured in the next 48

hours even in daily training is high. Thus, pre-emptive care is offered to anticipate a

significant injury that keeps him from the playground which reduces after-injury health

treatment (hospitalization, surgery, re-education, massage therapies…etc. ) costs and salary

spent without any given performance. [2]

1-1 1.1 Social Challenges for Wearable Electronic Sensors

Despite their tremendous potential to impact our vision of health, wearable electronic

sensors still face many hurdles. We have to take into account many factors, apart from

Technical and Health challenges, while designing such devices:

- Low Cost - Non-invasiveness - Social/Ethical factor

Breakthroughs in sensors are needed to achieve low-cost devices and enable their access

for low-income population in an attempt to increase their market spread. MEMS (Micro

Electro Mechanical Systems) are a living proof of this concept. They are very cheap to

mass fabricate and still generate very rich analog reliable data. For instance, we cite the

accelerometers and microphones we find everywhere in our daily life. Examples are the

microphones in our computers, cell phones, webcams, baby monitors, and walkie-talkie

5

radios. Other examples are accelerometers in our airbag systems, smart watches, and cell

phones.

The very first letter in the acronym WES stands for wearable. Moreover, thus, the device

we shall design needs absolutely to satisfy the rule of invisibility “wear and forget” as we

consider our watches, eyeglasses, hats and shoes as extensions of ourselves. In this way,

the system would be as non-invasive as possible.

As smart wearables acquire sensitive health data about patients or users, we shall ensure

the design of appropriate secure encryption protocols for data handling and transmission

to achieve data privacy. Users are quite skeptical when their privacy is not assured and

therefore they are less tempted to wear the device if they dont not feel comfortable about

their personal data.

Finally and most importantly, it is crucial and fundamental that our design complies with

the three rules/requirements/specifications stated above as we hope to greatly impact our

society by improving the health system and, as we nobly fix as a higher moral duty, to

improve and save as many lives as possible.

1.2 State of the Art for Wearable Electronic Sensors

As a result of the technological breakthroughs in electronics in the last two decades, we

have seen an increase of interest in sensing and monitoring devices, especially for

medical health applications. More emphasis is being put on wearable mobile medical

monitoring systems based on textiles and wireless sensing networks. Various types of

small physiological sensors are embedded within these systems along with transmission

modules and processing units.

WES has a considerable potential to change and revolutionize the health care model by

providing inexpensive, unobtrusive wearable solutions for 24/7 continuous medical

health monitoring.

6

It seems logically reasonable before we start proposing a possible design to review and

compare previous system implementations reported in the literature according to multiple

classifications factorswhich will provide a big clear picture of what already exists and

will serve as a reference to approach and assess technological advantages and drawbacks

of the current state of the art in WES solutions.

1.2.2 Wearable Electronic Sensors Worldwide Initiatives

Many Projects were initiated worldwide towards this driving trend, and many successes

have been achieved in wearable electronics for medical applications.

In 2004, a European-founded project AMON [3] developed a smart wrist-worn device to

acquire several vital signs. The system can monitor ECG, Blood Pressure, and Blood

Oxygen Saturation SPO2. The device comprises several embedded sensors and a

microprocessor unit that uses complex algorithms to extract the relevant information via

signal processing, after which results are sent via a communication unit to a remote

computer for analysis Figure 1.

The device enables continuous monitoring and emergency notifications over a GSM-link

protocol and proves to be an effective wireless wearable medical device.

7

Figure 1: System overview: AMON; wrist-worn medical device with GSM/UMTS link

to the TMC [3]

The device is composed of five units, the central processing unit CPU, the power supply

and display units, the communication and sensor units for data sampling and transmission.

Some wearable systems have been developed towards specific applications such as

monitoring posture position using integration of multi-core technologies. The device is a

textile-based intelligent wearable vest for real-time multi-posture monitoring [4]. Many

textile-based initiatives were reported in the literature as an attempt to develop a smart

garment capable of monitoring vital signs and providing the information all unobtrusively

and in a non-invasive way [5-7].

8

Figure 2: Arrangement of the ECG, respiration, and thoracic oximetry sensors for

BioTex prototype [5]

Biotex is an incarnation of one of those attempts. A multi-sensors integrated vest was

designed to acquire and analyze body fluids and temperature [5]. The device can also

monitor cardiovascular activity and ECG. Sodium and pH sensors were integrated into the

vest along with sensors for sweat rate and ECG Figure 2. The overall system comprises a

network of sensors providing several physiological parameters in real-time.

Wearable electronics sensors can even be used in extreme conditions to monitor a person’s

vital signs. High altitude climbing brings a person into a harsh and extreme environment

so that collecting and monitoring health data in real time can be a critical matter of life and

death. The MagIC Wearable system allows tracking of hikers in extreme and high altitude

environments [6]. A shirt is strapped with belt-like sensors and electrodes in direct contact

with the skin to have access to ECG, respiration and oxygen saturation vitals. The prototype

was put under test during an expedition to the Everest Mountain while a group of hikers

climbed to the top. Accelerometers were embedded in the smart shirts to keep track of the

altitude.

9

HealthCare is continuously being innovated and revolutionized by the integration of

electronics. Hundreds of systems and wearable devices have been developed towards

medical applications and vital sign monitoring [8-18].

The most common are smartwatches [14-17] or smart textile garments because they are

highly non-invasive and are widely spread among the user community as an everyday

artifact.

While a variety of vital signs can be captured easily with wearable sensing devices, non-

invasive measurements are relatively rare. These complex and smart systems are not yet

widespread in society. Respiratory measurement is one of the most critical vital signs to

monitor. Keeping track of respiration is fundamental in revealing and treating chronic

respiratory diseases. While the latter is easily acquired with any belt-based sensor around

the chest, the comfort of the patient/user is drastically decreased.

1.2.2 Near Field Communication NFC based monitoring systems

Real-time monitoring of Respiratory rate will help to uncover chronic respiratory diseases

more efficiently. Many systems are reported in the literature regarding this matter, the

majority share a common feature of unobtrusiveness and invisibility in the form of a

contactless device. While device architectures may vary, the fundamental principle upon

which such systems are designed for contactless sensing are the same. Using two advanced

and robust wireless communication protocols UWB and NFC, many research teams

demonstrated the possibility/proof of concept of such a device with no less accuracy and

comfortability.

Near Field Communication NFC is a short-range wireless communication that can transmit

data from two points at very high speed between two electronic devices. The protocol is

based on RFID technology and uses both active and passive designs. Some papers report

in the literature the use of NFC as a sensor to monitor respiration rate and other

physiological data.

10

Figure 3: NFC based bellyband [33]

Mongan et al.demonstrated the principle with a bellyband passive RFID system. The

device elongates while the patient breathes and the signal is collected by an active external

RFID interrogator Figure 3. The system is meant to detect sleep apnea by monitoring

respiration rate and feeding the data to a machine-learning algorithm to categorize every

respiration phase. The RFID interrogator evaluates the RSSI (Received Signal Strength

Indicator) as a time-variant signal to extract respiratory variation. Similar related work is

presented in [34-41] pointing the use of NFC as an efficient and accurate tool to monitor

physiological data.

While the system shows high data accuracy and robustness, the patient needs to be in front

and close to the RFID interrogator (about 4 inches) and signal integrity might be

compromised by other close passive RFID devices.

1.2.3 IR-UWB and SoC CMOS UWB

Ultra-Wide Band UWB is a medium range wireless communication protocol over a broad

band of the spectrum ranging from 3.1 GHz to 10.6 GHz. UWB has recently attracted many

research teams to use it as a sensing mean for contactless monitoring of Respiration Rate

RR. Using higher frequency for detection increases the precision and accuracy of the

11

sensor. An impulse radar UWB system was designed by Schleicher et al [42], for ultra-fine

movement detection and respiration rate monitoring.

Figure 4: Block diagram of the correlation-based radar demonstrator [42]

The device sends an impulse wave towards the chest of the patient and collects the received

signal. Then they evaluate the flying time and retrieve exact distance measurements from

which they extract ultra-fine thoracic chest displacement Figure 4. After which, respiratory

and cardiovascular activity are identified and stored for analysis and monitoring Figure 4.

The bottleneck in the previous design is the Impulse Generator IPG which is a critical part

that limits the device sensitivity and precision. By integrating an IPG using CMOS

technology, Zito et al [43], reported a mobile pulse radar system for RR monitoring Figure

5.

12

(a) (b)

Figure 5: (a) SoC UWB Pulse Radar System Architecture (b) Micrograph of the radar

chip [43]

The System can be fully portable providing the patient with a contactless non-obstructive

sensor for continuous remote respiratory rate monitoring. The device can even be

integrated into a car steering wheel or hospital beds to provide an ideal sensor for everyday

use. Although preliminary results are satisfying and show acceptable precision, there is one

considerable but nonetheless important issue that needs to be mentioned. The patient’s

chest must face the sensor at any given time, which reduces drastically the patient’s ability

to move freely and proceed with everyday common activities.

13

1.2.4 Multi-Material Fiber Antennas

Multi-material fiber antennas integrated into textiles are a very decisive technology and

probably the future of smart-textiles industries where clothes would, in the near future,

wirelessly communicate with smartphones and computers [51-53].

Figure 6: (a) Schematic and radii of the hollow-core polymer-clad silica fibers with a

deposited thin (~150 nm) film of silver used to fabricate dipole antennas, (b) fiber

antenna design for 2.4 GHz, and (c) fiber antenna weaved into a cotton fabric with

minimally-invasive attributes [52].

Hollow core fiber filled with a thin layer of Silver 150nm thick are used as transmitting

and sensing antenna figure 6(a). The fiber can be easily sewed into textile as in figure 6 (c)

and provide a large spectrum of applications ranging from healthcare sensing to data

transmission and communication.

14

Figure 7: Prototype of the respiration sensor based on a multi-material fiber [51]

P.Guay et al [51], proposed a respiration sensor based on a wirelessly-communicating

multi-material fiber antenna at 2.4GHz. The prototype is presented in figure 7 where a half-

spiral fiber-based antenna is integrated into textile at the center chest of the thoracic cage.

With breathing movements, the half-spiral shape of the antenna will slightly and

periodically extend and geometrically change according to respiration. The central

frequency at 2.4GHz would then shift by some decades in MHz range.

15

Figure 8: Schematic representation of the multi-material fibers spiral antenna

integrated into a smart textile and experimental setup

The implementation of the fiber sensor is represented in figure 8 where the antenna

stretches due to the load caused by chest expansion. The T-shirt is wired to the vector

network analyzer VNA and to the computer. During respiration, the central frequency on

the VNA would shift periodically through time. Very reliable data were acquired and

measured with the Smart T-shirt Prototype but the accuracy has not been yet compared to

clinical data. Last step would be to switch to a portable wearable version of the prototype

is which can become tricky due to the high operating frequency of the antenna at 2.4GHz.

16

1.3 Chronic Obstructive Pulmonary Diseases COPD Financial Burden

There is no doubt to assert that health costs worldwide are a substantial economic burden.

This is especially true for chronic diseases. In low to middle-income countries, the

economic situation adds weight to this burden as it represents a higher percentage of the

GDP.

According to the World Health Organization, Non-Communicable diseases NCD’s are the

top 4 death causes worldwide, and projections show they still be till 2030 [19].

In 2008, cardiovascular and chronic respiratory diseases caused 60% of these deaths, and

80% of them happened to be in Low-Income countries [20].

In 2015, the total death count from both Cardiovascular and chronic respiratory diseases

reached 22 million and is projected to increase to 27% in 2030 [21].

Figure 9: Global magnitude of deaths from non-communicable diseases [22]

The global magnitude of deaths caused by non-communicable diseases in 2004 has reached

35 Million for a total percentage of 59% Figure 9.

17

According to [23], if nothing is done to reduce the risk of chronic diseases, an estimated

US$84 billion of economic production will be lost from heart disease, stroke, and diabetes

alone in Low to Middle-Income countries between 2006 and 2015.

Five Non-Communicable Diseases NCD’s (Diabetes, Cardiovascular disease, Chronic

Respiratory Disease, Cancer and Mental Health conditions ) will cost to the global

economy US$ 47 trillion between 2010 and 2030. Together, mental health and

cardiovascular diseases account for almost 70% of this global burden Figure 10.

Figure 10: Mental health and cardiovascular diseases are top drivers of lost output

Breakdown of NCD cost by disease type [24]

Chronic respiratory diseases and cardiovascular diseases represent 43% of the top drivers

of lost output from NCD Figure 10.

18

The total cost of Respiratory diseases in the entire 28 countries of the European Union is

US$ 408 billion annually [25]. This number is expected to double in the next decade Figure

11.

Figure 11: Chronic Obstructive Pulmonary Disease COPD health care costs will

double from 2010 to 2030 [24]

With the widespread proliferation of technology and especially smartphones even in Low-

Middle-Income Countries LMIC’s, a great opportunity arises to provide/give access/ensure

access to sustainable health care by enabling Wearable Electronics sensors.

This was the case with Syrian refugees in Turkey in 2016. Eighty percent 80% of them had

access to the internet through their smartphones. Wearable Electronics sensors can provide

health condition monitoring and can be far less complicated and less expensive than

smartphones Figure 12.

19

Figure 12: 80% of Syrian Refugees have access to smartphones and the internet[26]

In 2013, twelve thousand Canadians lost their life due to Chronic lower respiratory diseases

[27]. Chronic lower respiratory diseases are diseases that affect the lungs. The most deadly

of these is the chronic obstructive pulmonary diseases (COPD), which makes it hard to

breathe. Most cases of COPD are preventable, but damages cannot be reversed. If the

disease is found early, though, it might be possible to slow it down. Typical symptoms are

shortness of breath, an ongoing cough, or chest tightness; a doctor can give a simple test

for COPD then but likely too late to stop it. Although, with a device that monitors your

breathing rate and variability 24/7, earlier symptoms can be detected at very early stages

of the infection and make the treatment more effective and possibly curable [28].

In recent years, the rapid development of wireless communications, microelectronics and

embedded technologies have had a striking impact on medical electronics market which

we qualify as an exponential growth. In fact, the global medical electronics market was

worth USD 94 billion in 2015, and the forecasts are that it will increase by 7.9% CAGR

(Calculated Annual Growth Rate) over the next decade to reach USD 219 billion in 2024

[29].

According to the CDC, health expenditures in the United States reached 17.4% of the GDP

in 2014, which represent a total of USD 3.0 Trillion [30].

20

According to Health Canada, health expenditures in Canada reached 11% of the GDP in

2016, which represents a total of CAD 228 Billion [31].

In 2012, more than 117 million people in the world suffered from at least one chronic

disease and 40% had more than two chronic conditions. Moreover, around 13.6 million

people suffer from chronic obstructive pulmonary disease.

The burden of these diseases eventually falls on the country and results in higher insurance

rate, loss of productivity, and increased taxes rate. The use of medical electronics produces

faster diagnosis and treatments eventually reducing the number of sick patients and could

even lead to a massive cut saving in the health expenditures budget.

Furthermore, this is anticipated to boost the medical electronics market Figure 13 which

will eventually lower the manufacturing prices for wearable electronic sensors.

Figure 13: U.S. medical electronics market, by application, 2013 - 2024 (USD

Million) [32]

In the previous figure, Figure 13, we notice the exponentially increasing rate for the Patient

Monitoring section driving us even more towards continuous portable monitoring systems

21

and their ability to rapidly detect, treat and even prevent health issues. This also points out

the dominant and growing potential of the medical electronics market which will

significantly facilitate the proliferation of such devices.

1.4 Project Objectives

Our primary objective through this project is to investigate and design a new wearable

sensor toward continuous respiratory functions monitoring. Our focus is to provide a new

method to accurately read respiration signal with a user-comfort wearable device.

Investigation of the technical literature is the key step to initiate a proper scientific

approach. We want to identify and explore the most adequate methods to measure

respiration and cardiac pulse in real time. To achieve so, we based our approach on the

following steps:

- Literature review and State of the art review

- Identify relevant methods

- Define most adequate approach to a proper design

- Build the Sensor

- Characterize System performances

- Experimental Tests and Validation

Conclusion

UWB based systems [42-49] proves to be an efficient way to monitor Respiratory Rate

RR and Heart Rate HR in a non-invasive and accurate way. The contactless method offers

an unprecedented apparatus to collect vital signs with absolute invisibility opening the new

possibility to develop and design low-cost low-power wearable electronic sensors for

medical care that can satisfy the rule wear and forget.

Regarding these systems, we opt to design a contactless wearable invisible low-power low-

cost sensor for respiratory rate monitoring.

22

Chapter 2

Patch Radar System: Apparatus and Methods

2.1 Wearable Electronic Sensors

Figure 14: General System Blocs Diagram of Wearable Electronic Sensors

Wearable Electronics Sensors can acquire many physiological conditions:

- Vital signs (Respiration Rate RR, Heart Rate HR, Body Temperature BT, Blood Pressure BP, Oximetry SpO2…etc.)

Vital signs are not limited to what we mention here, but those are the most relevant to

determine the good wealth of a person during a quick check diagnosis prior to any medical

practice.

- Gait Analysis

Healthy persons tend to have a good weight balance equally distributed on both feet. Gait

Analysis is the measure and tracking of a person’s style of walking. Elderly people or

injured people tend to slightly disrupt this equilibrium due to their physical condition.

Tracking and monitoring of gait analysis can provide physicians good source of

information about their patients’ health and recovering.

- Spinal Posture

As people grow older, bones thickness and robustness tend to decrease. The spinal cord is

the central series of a mall bones supporting our upper body. Monitoring a person posture

23

can provide rich information about their activities and well-being. When people get tired,

they usually tend to have an incline spinal posture. When people are less active or

emotionally less satisfied, their spinal posture tend to be more inclined too.

- Sweat Rate

Sweat is the energy burned by the body during activities. The rate of this process provide

a good indicator about the type of activity and the health of the patient.

- Parkinson disease

Random shaking seizures are common with a Parkinson diagnosis. By monitoring their

frequency of occurrence and their duration, physicians can develop custom treatments for

their patients and monitor in real-time their progress.

The general functional block diagram of a conventional wearable electronic sensors should

include a sensor and a readout circuitry Figure 14. Presently, the challenge in the design is

the sensor part. Sensing requires a physical transducer that can convert the analog physical

entity into an electronic digital signal.

Sensors types fall under various categories:

- Chemical (Gas Sensors…) - MEMS (Accelerometers, Gyroscopes, Microphones…) - Piezoelectric (Pressure Sensors…) - RF Sensors (Radars, Proximity Sensors…) - Laser (Optogenetics sensors, Distance, Temperature, Gas,…) - Electronic (Coils, Capacitors, PN Junctions for Proximity, Distance, Light

intensity…)

24

2.2 Design Specifications

It is crucial and fundamental in each rigorous scientific work to frame the problem we state.

Thus, we shall describe what we have and what we want in concise and clear terms. The

“What we have” part is naturally the problem and the “What we want” is a description of

a possible solution among numerous others.

This work deals with exploiting and measuring a physical phenomenon which is respiration

movements. Our vision is, then, to build a practical system, a sensor, that executes this

task.

For this reason, we need to set the objectives, establish the system functions and define the

boundaries and constraints on which we shall build the apparatus:

-We want to build a system, more specifically, a stand-alone wearable electronic system

capable of acquiring at least one vital sign starting with the respiration rate (RR).

-The wearable electronic sensor needs to be integrated into a textile fabric or bent on it

non-invasively so as not to reduce the user comfort.

-The system has to be autonomous, self-battery powered and stand-alone for a relatively

long period.

- The system should be able to send the acquired data to a base station for storage and

analysis.

-Finally, it shall comply with the rule “wear and forget”.

Since contactless techniques were reported to be an efficient tool to achieve non-invasive

monitoring of vital signs, we chose to design a UWB based system to monitor breathing

rate in real-time.

25

2.3 Impulse-Radar Ultra Wide Band IR-UWB Principle

Figure 15: Impulse Radar UWB Sensing Principle

UWB based systems share a common disadvantage that is the patient has to be in front of

the device and the sensing head facing the chest wall. In this manner, the distance between

the sensor and the thoracic wall can be evaluated while varying during breathing and

respiration rate can be extracted Figure 15. In case the antennas are too close to the body,

the relative time of the reflected wave would be too short and hard to acquire with a good

precision which would render the device unpractical for a user-free-mobility application.

26

2.4 Proposed Solution

Figure 16: Sensor Placement on the Chest

One of the most effective ways to measure the breathing rate is to take advantage of the

mechanical displacement produced by the lungs on the thoracic wall during air inhaling

and exhaling. We make use of the spatial propagation properties of electromagnetic waves

in the air to transduce this very repetitive mechanical displacement Figure 16.

We propose to use antennas as a sensing head. Our method is not to emulate a radar system

but instead to use the antennas for displacement through phase shift measurements.

27

2.5 Sensing Principle

Figure 17: Spatial Phase Shifting Principle

We propose in this work to bend the antennas onto the patient chest wall and evaluate the

distance in between. In this case, the antennas are moving dynamically with chest

expansion during respiration activity.

Chest expansion movement during regular daily quiet breathing state is estimated around

0.1mm to several millimeters [53-54]. During relaxed heart beating, the thoracic wall

moves back and forth by an estimated 0.08mm [55].

High-frequency EM waves allow highly precise distance measurements. Indeed, we can

measure the time delay very precisely from the reflected wave and thus extract the chest

expansion movement during respiration.

We propose to transduce the mechanical displacement produced by respiration activity to

a phase shift of an electromagnetic wave thus rendering the system strongly robust against

noise interferences Figure 17.

28

We continuously transmit a sine wave at 3-GHz between transmitting Tx and receiving Rx

antennas and compare the phases at both ends. By breathing in and out, the lungs will

produce a repetitive movement that will cause the initial distance between both antennas

to vary. Each displacement will change the initial phase shift, and thus we can extract a

respiratory pattern.

2.6 Mathematical Model

Figure 18: Mathematical Modeling of the thoracic cavity

This section describes the mathematical modeling we propose for calculating the phase

shift that would be produced by thoracic oscillations. Since respiration is rhythmic and the

thoracic cavity is filled and emptied by air repeatedly, we propose to model the lungs by a

29

circle around the chest with a changing radius representing respiration movement Figure

18. We want to estimate approximately the phase shift that would be occasioned by

respiration and heart beating movements.

Figure 19: Mathematical Modeling of the Lungs during breathing

The two points of interest A and B represent the location where we want to bend both

antennas on the chest Figure 19. The relative distance between the two antennas of the

sensing head is approximated to oscillate according to the following equation Eq. 1:

=

sin(2 + )+

sin(2 + ) (1)

30

Where:

: Chest extension distance during respiration cycle

: Chest extension distance during heart beating cycle

: Respiration Rate frequency

: Heart Rate frequency

: Initial phase of the sensing sine wave at the beginning of respiration cycle

: Initial phase of the sensing sine wave at the beginning of heart beating cycle

Figure 20: Thoracic Wall expansion modeling in distance and phase shift

Our approach consists in transducing the respiration rate to a relative distance change, then

to a phase shift 2∆. Where = ∆+ + ∆ and = + + .

According to [3], the chest wall can expand to several millimeters during breathing

depending on the subject age, weight and state (Quiet or shallow breathing).

At 3 GHz the wavelength is 10 cm which means that the phase shift at d = 10 is

θ = 0 [360]. Now, if we place the antennas at quarter wavelength we can expect the

phase shift to bounce around θ = 90.0 ± 2∆ [360]. Using a specific circuit to detect

phase shifts we can reconstruct the respiratory signal. The elementary phase deviation is

3.6 / and since breathing action displace the chest wall by a few centimeters, let us

31

assume by 2 cm at minimum and 5 cm at maximum, we can expect a phase shift change

between ∆ = 3.6 and ∆ = 9.0 which at the end adds up to ′ = 90.0 ±

3.6 and ′ = 90.0 ± 9.0 (see Figure 20).

Δζ Detector

AD8302

Tx RxSensor Head

Battery3.7-V Li-th

CPUPIC18F13K22

Wireless P(w) Receiver

Universal Qi Rx

RF Osc.3.1-GHzCVCO55CC

Wireless Tranceiver

Xbee S2C

Δζ

A’ B’

θ ΔζΔζ

θ’

A B

Δζ

Δζ Δζ

Figure 21: Phase shift modeling From the PCB lines to the chest wall

In an attempt to improve the mathematical model, we could have considered the phase shift

that occurs along the PCB traces and transmission lines as demonstrated in figure Figure

21.

We chose here to neglect these effects on our first approximation by considering very short

transmission lines and optimal PCB floor planning to minimize the distance between the

transmitter and the inputs of the phase detector. By setting the latter two as design

constraints, we can rely on the approximation that neglects phase shifting occasioned by

them

32

2.7 Apparatus Design

Figure 22: Patch Radar Sensor Blocs Diagram

Figure 22 illustrates the building blocks of the proposed breathing sensor. We use an RF

oscillator at 3 GHz to generate the sine wave, two antennas for the sensing end and a phase

detector to which we feed both RF signals to calculate the phase shift. The phase detector

encodes the value of the phase difference into a corresponding DC voltage. The conversion

rate is set to be 10 mV/degree.

33

2.7.1 Central Processing Unit CPU

Figure 23: PIC18F13K22 CPU Blocs Diagram

The CPU, Figure 23, is the main component in the sensor architecture and where all the

data flow in and out after processing. The CPU is the brain of the system with an integrated

ADC converter for acquiring the analog data provided by the phase detector. It also

includes a UART circuitry necessary to connect with the wireless transmitter to send the

data back to the main station. The internal oscillator incorporated inside the chip minimizes

power consumption since our sensor will be battery powered and power supply will be

limited. In fact, in this case having an on-chip oscillator reduces power consumption and

the complexity of the external control circuitry. Also, the PIC18F13K22 has a sleep mode

option and wake-up on interruptions. Finally, the input/output pins allow easy connection

34

with all surrounding circuits for a maximum flexibility and performances in embedded

applications.

2.7.2 Phase Detector

(a) (b)

Figure 24: AD8203 Phase detector (a) Transfer Curve (b) Control circuitry to set

conversion ratio

The AD8302 is an analog ASIC explicitly designed to compare high-frequency waves and

detect phase and gain changes. The output of the chip makes it very easy to be acquired by

the ADC of the CPU since it is a time-varying DC signal that depends on the phase/gain

shift/difference of the inputs. We only make use of the phase shift detection feature of this

chip as to maximize data integrity. Gain detection would provide the same results but with

much more interferences from surrounding sources.

The Datasheet specifies that the chip max frequency of operation is 2.7 GHz, but we can

still make it work at 3 GHz. In fact, by setting the initial phase shift around 90.0, as

calculated in section 2.6 of this chapter, we bias the phase detector in the linear down slope

of its transfer curve Figure 24 (a) thus maximizing overall linearity of the sensor.

35

2.7.3 Wireless Transmitter

Figure 25: Xbee Pro S2C transceiver

The Xbee Pro S2C, Figure 25, is a reliable low-power close range transceiver for embedded

applications. The communication with surrounding chips is assured by either an SPI

protocol or UART. The data rate is also of high performance, but in our case, it is irrelevant

since human vital signs have an inertial behavior of few seconds. The data encryption is a

standard UART protocol where messages are sent as strings of text and characters

according to the ASCII code in their hexadecimal format. The power supply necessary is

convenient for a battery powered system and the use of CMOS logic make it compatible

with a wide range of commercial chips. However, the encryption protocol used between

modules transmission is based on Zig-bee specifications to ensure data integrity and

privacy. The transmission frequency is 2.4 GHz but in some cases other modules are

adapted for wide range applications and hence their central frequency is 900 MHz.

36

Conclusion:

We have proposed in this chapter a novel way to detect and monitor cardiovascular and

respiratory signals. The sensing principle is based on phase shifting of high-frequency

electromagnetic waves emulating a reverse-like radar feature to detect chest wall

movements and reconstruct the physiological data. The mathematical formulation of the

problem gives us an approximation of the physical quantities we have to acquire and shows

the great potential of the approach. Lastly, the first sketch of the proof-of-concept prototype

is presented as a fully integrated patch-like embedded sensor with battery powered and

wireless recharging features enabling considerations for waterproof sealed packaging. The

central critical part of the sensor is the phase detector ASIC that provides a time-varying

DC signal linearly dependent on the phase difference of the input waves. The frequency

domain transformation from the inputs 3 GHz to the output base-band DC domain is the

key feature that allows convenient implementation of the patch radar sensor.

37

Chapter 3

System Implementation and Experimental Results

Introduction:

This chapter includes all the experimental results gathered from multiple tests on 3 different

subjects: adult male, adolescent female and adolescent male. The test results are obtained

with a demonstrator PCB implemented according to the design specifications and

mathematical modeling developed in the previous chapter.

3.1 Apparatus PCB Layout

Figure 26: Printed Circuit Board PCB Layout design

The PCB design was developed using Altium Designer software. It is a two-layer

demonstrator PCB including only the phase detector and control circuitry along with the

RF voltage controlled oscillator. Figure 26 shows the layout and floor planning for the

components and the SMA connectors used to reduce noise through the inputs and outputs

connections.

The RF transmission lines have been carefully designed to match 50Ω resistance with

compliance to the technology used to develop this demonstrator PCB. The dielectric

38

constant of the material is 4.2 and the height and thickness of respectively the PCB and the

copper are 39 mil and 1.377 mil. This yield 1 mm wide trace lines to match 50 ohm

resistance to the input of the transmitter, phase detector and antennas SMA connectors.

Surface mounted components SMT were used for the control circuitry of the phase

detector. A bottom and Top photo of the final soldered PCB is presented in Figure 27.

Figure 27: Printed Circuit Board PCB Photo

39

3.2 Sensing Head Antennas

Figure 28: Wide Band Antenna Sensing Head

For the sensing head we used two wideband oval shape antennas presented in Figure 28.

Since the sensing head will be bent on the chest, whether as a patch or sewed into textile,

we shall account for body absorption of the signal power, and hence transfer parameters

need to be characterized.

Figure 29: Incident and reflected waves in a two-port network [53]

40

An antenna can be modeled by a two-port network as is in Figure 29 where a(x) and

b(x) represent the values of the incident and reflected waves respectively.

The parameters , , , are the scattering parameters which represent the

reflection and transmission coefficients defined as follows:

= ()

()()

: input reflection coefficient with output properly terminated

= ()

()()

: forward transmission coefficient with output properly terminated

= ()

()()

: output reflection coefficient with input properly terminated

= ()

()()

: reverse transmission coefficient with input properly terminated

The scattering matrix is defined as:

[]=

Where [] is:

()()

=

()()

The scattering parameters give us relevant data about the system performances. We can

validate the design of the sensing head antenna at the working frequency by evaluating

these parameters.

41

3.2.1 Wide Band Antenna S11 Parameter

Figure 30: Wide Band Antenna S11 Parameter

Characterization by S11 curves is presented in Figure 30. Two tests have been performed,

the first with the antenna floating in the air (blue curve) and the second with the antenna

onto the chest (red curve). Since our application does not require long distance transmission

and data integrity but rather short-range transmission of a continuous analog sine wave, we

consider the power transferred acceptable at a specific frequency if the reflection is more

than -10 dB. For the frequency of interest 3 GHz, the magnitude is around -10 dB for both

setup positions (close and far from the body). We also notice that slightly more power is

reflected for the on-body setting since the immediate surrounding dielectric medium is

changed and hence less power is transmitted by radiation.

42

3.2.2 Wide Band Antenna S12 Parameter:

Figure 31: Wide Band Antenna S12 Parameter

S12 parameter is presented in Figure 31. We notice the difference in signal magnitude for

the two different setups again. We explicitly verify that the power transferred between

both ports is enough at 3 GHz precisely and in a broad RF frequency band (from 1 GHz

to 11 GHz).

43

3.3 Sensor Characterization:

Let be a set of data point (, ), (, )…(, ). Where (, ) is a point in the data

cloud representing the system response to the input . (, ) would be respectively the

distance between the antennas in the sensing head and output voltage of the sensor.

We try to model the relationship between the output voltage Y and the distance between

the sensing head antennas X. Data points were gathered at 3 GHz frequency and at a

distance range from 0 mm to 60 mm.

Linear regression is used to characterize the system. Linear regression is a common method

in statistics where we try to fit a data cloud X with a linear equation Y using least-squares

calculations.

= +

Least squares regression is the most common method for linear data fitting. It calculates

the best fitting line by minimizing the sum of the squares of the vertical deviation of each

point to the linear line.

To determine the slope of the best fitting line we calculate as:

=∑ ( − )( − )

∑ ( − )

To compute the y-intercept point b:

= −

The strength of the linear relationship is quantified by the regression coefficient or

correlation coefficient R. The value of R ranges between -1 and 1. The closer the value is

to the range edges, the stronger the linear approximation is.

= 1

− 1

−

−

44

Where:

: Number of points – Data cloud size

: Mean Value of X distribution

: Mean Value of Y distribution

: Standard Deviation of X distribution

: Standard Deviation of Y distribution

0 10 20 30 40 50 60 70

Distance (mm)

180

200

220

240

260

280

300

320

340

360

380

Y = 2.62*X + 195

Ou

tpu

t [m

V]

Data

Linear

Figure 32: Sensor Linearity Regression

Experimental measurements are demonstrated on Figure 32. The linear regression model

was computed as:

= 195+ 2.62

The squared regression coefficient is evaluated at 0.973 yielding a correlation

coefficient as high as = 0.986.

45

This result qualifies the dependence of the output voltage from the sensor and the inter-

antennas distance as highly linear. The relationship between the input and output of the

sensor can be approximated to a linear curve with a correlation factor of 98.6%. Although,

the previous result seem high, some residual non-linearity will affect the system

performances and limit the accuracy of the respiration sensor. In-vivo and in-vitro

experimental tests are the only way to determine the accuracy of the device by comparing

results with clinical data.

46

3.4 Experimental Setup:

Figure 33: Sensor Implementation

47

The sensor implementation presented in Figure 33 shows every block of the design and

how signals are flowing inside. The RF Oscillator transmits a continuous analog sine wave

at 3 GHz through the TX antennas while the same phase delayed wave is received through

the RX antenna. Both waves are injected at the same time to the phase detector and phase

difference is evaluated.

The output of the Phase detector AD8302 is a DC time-varying signal corresponding to the

phase shift calculated by the chip. According to the settings of the control circuitry for the

phase detector, we can vary the conversion rate between phase shifts in degrees and output

DC signal in Volts. The implemented configuration provides a 10 mV/deg conversion rate

as an analog signal. Depending on the resolution we want to achieve we can adjust the

corresponding Analog-to-Digital Converter ADC resolution in number of bits to obtain the

necessary sensitivity and precision. Another option would be to change the phase detector

configuration to more sensitive conversion rate. Either way, the output signal is very

convenient for further signal processing since it's already in baseband. In addition, we stress

the fact that the signal is analog and hence rich of data just waiting to be extracted.

Figure 34: Sensor Position on the chest

48

One of the several possible and optimal positions to have a maximal elongation of the

thoracic cage and hence the maximum of sensitivity is near the heart cavity on the right

pectoral muscle. This specific position also enables the possibility to detect heart beating

movements as shown in Figure 34 and Figure 35.

Figure 35: Experimental Setup with sensing head bent on subject chest

3.5 Respiratory Rate Data

The following section presents the data we acquired with the proposed sensor design. The

fundamental purpose of the device is to monitor respiratory functions and hence not only

rate or breaths per minute but also respiration depth.

49

0

40

Time [s]

Ou

tpu

t [m

V] 30

20

10

2015105

Quiet Shallow Quiet

Figure 36: Artificial Breathing rate in time domain with three distinct phases of quiet,

shallow and quiet breathing

The Figure 36 emulates an artificial breathing rate with three distinct phases. This test was

done to validate the output signal shape during respiration.

The next two figures are data gathered on a male adult subject where the participant was

asked to breathe according to a specific pattern. We see the difference in respiration rhythm

between quiet, shallow and apnea phases in Figure 37.

Also, apnea phases are distinguishable which offers a new monitoring solution for sleep

apnea detection. We also notice the respiration depth and whether or not the subject is

breathing full lungs. This feature can help to determine if a patient is lung-injured or if he

has a chronic condition of respiratory dysfunction as seen in Figure 38.

50

Figure 37: Male Adult Breathing rate in time domain with three distinct phases of

quiet, shallow and soft breathing

0 2 4 6 8 10Time [s]

545

550

560

570

Ou

tpu

t [m

V]

ApneaQuiet Quiet

Figure 38: Male Adult Breathing rate in time domain with three distinct phases of

quiet, hold and soft breathing

51

3.5.1 Adult Male Subject [25-45 years old]

Figure 39: Male Adult Breathing rate in time domain in four positions Standing,

Sitting, Walking and Lying with varying breathing cycles of Shallow, Apnea, Shallow

and Quiet

The same male adult subject was put to another test scenario where he was asked to breathe

in 4 different positions while standing, sitting, walking and lying in Figure 39. We can

notice that the device is perfectly working in all four different positions we often encounter

in our everyday life activities. This proves the device potential and aptitude for continuous

monitoring during everyday activities. Nevertheless, we notice that data acquired during

walking activity is noisier than the others and this was expected from motion artifact effect.

A very interesting fact needs to be mentioned here though, during apnea phase while the

subject was sitting, he was asked to hold his breath with lungs full of air then empty. During

this phase, while there are no thoracic movements from respiration activity we notice a

small magnitude of repeated oscillations pointing towards one single origin which is

52

heartbeat movements. We conclude that the sensor is sensitive enough to detect even

heartbeat displacements on the chest wall and thus we can reconstruct the cardiac signal.

3.5.2 Adolescent Female Subject [18-25 years old]

Figure 40: Female Adult Breathing rate in time domain in four positions Standing,

Sitting, Walking and Lying with varying breathing cycles of Shallow, Apnea, Shallow

and Quiet

In this section and the following one, the same test was repeated on a female adolescent

and a male adolescent subjects. The results are presented in Figure 40 and Figure 41. Tests

were conducted on two different genders, male and female, and two different range of age.

The device succeeded to acquire time variant respiratory signals from all participants and

in four different positions. We notice though a smaller sensitivity from the experiment done

with the male adolescent which is again expected since younger people rely more on

abdominal contraction to breath in and out than on thoracic movements.

53

3.5.3 Male Adolescent Subject [18-25 years old]

265

275

285

295

300

Ou

tpu

t [m

V]

Time [s]

Male Adolescent Sitting

0 36302010

Male Adolescent Standing

Time [s]0 36302010

290

295

300

305

310

Ou

tpu

t [m

V]

Male Adolescent Lying

Time [s]0 36302010

205

210

220

230

240

Ou

tpu

t [m

V]

Male Adolescent Walking

Time [s]0 36302010

290

295

300

305

310

Ou

tpu

t [m

V]

Figure 41: Male Adolescent [18-25 years old] Breathing rate in time domain in four

positions Standing, Sitting, Walking and Lying with varying breathing cycles of Shallow,

Apnea, Shallow and Quiet

The experiments gathered very conclusive results on the efficiency and feasibility of the

idea behind the patch radar sensor. We can affirm that we successfully achieved a proof of

concept of the proposed monitoring device. We shall though mention a last important note.

If we compare the apnea phases between the three different subjects, we can extract the

maximum breathing depth of each when they switch from full apnea to emptying their

lungs completely from air. The adult male respiration depth slope is around 35 mV, for the

female and the child they are 20mV and 5mV respectively. These values represent the

amount of air we breathe in and can be matched to numerical values in a proper unit.

54

Conclusion:

A demonstrator PCB have been implemented to test the design developed through this

project. Several experimental results have been gathered and processed showing high

accuracy and performance. The present results give a strong proof of concept of the

proposed new method to monitor respiratory functions. Experimental results were

collected on three different subjects with different age and gender and in several different

common positions in everyday activity. While the first objective of the project was to

monitor respiratory rate, placing the device on the chest near the heart cavity, the sensor is

sensitive enough to detect even heart rate displacements. Our system can work over a large

band of RF frequencies and the sensing head can integrated different antennas shapes and

types. This proves the robustness of the design of the sensor as frequency and antennas

independent.

55

General Conclusion

We certainly strive to develop continuous health monitoring techniques and provide new

set of Medical data but also to enable faster emergencies response time. In fact, by

achieving 24/7 real-time monitoring through wide-range wireless technology, we can

remotely auto-detect emergency situations and dispatch EMTs (Emergency Medical

Technicians) on site as soon as possible.

Real-time and Continuous health monitoring will make it possible to study and analyze

very meticulously and closely physiological data. Thus, we can identify and categorize

vital sign patterns occurring just before major Cardiac or Respiratory accidents so we could

act pre-emptively enabling an innovative health practice as Remotely Pre-Emptive Medical

Care.

Physiological data can be fed to Machine Learning Systems based on Artificial Intelligence

algorithms to identify and associate vital sign patterns associated with pre-critical health

issues. The process will be semi-automated, and we can expect, in the very near future,

physicians and doctors or even EMTs contacting high-risk patients, or even remotely

diagnose their health condition.

Physicians spend about two minutes to evaluate and assess vital signs of one patient which

in case of a large number, as may happen in a public hospital during a bus accident for

instance, can become a tedious task and a considerable loss of time. By automating vital

sign acquisition and analysis, we provide doctors and medical staffs with efficient tools to

deliver the best health service on time.

One of the exciting aspects of wearable electronics sensors is we are working toward

integration into textiles of these small-size invisible smart systems. The integration has to

be non-invasive rendering the wearable device common everyday artifact is hidden in plain

sight and users will not even notice them. In fact, people do not feel obstructed by their

own clothes and even some bendable systems are now considered wearables like

smartwatches (Fitbit...etc) and smart glasses (google glasses...etc).

56

Successful integration of WES (Wearable Electronics Sensors) into smart textiles will

enable a completely new horizon of applications ranging from health and security to fitness

and comfort.

The point of this latter is that continuous monitoring of Respiratory functions can help to

offer and develop better treatments, save more people and even lead to saving a large cut

of the health budget giving the accuracy and non-invasive features of such devices.

Perspectives

We propose many applications directly related to this work:

-Smart T-shirt: Providing continuous real-time monitoring of vital signs (Heart Rate HR

and Respiratory Rate RR...etc.).

-Smart Seatbelt: To be integrated into a car seatbelt which provides HR and RR and detects

alcohol consumption. The system should also be able to automatically call emergency

services in case of a car accident or Cardiac/Respiratory problem.

-Smart Diapers: For elderly people and newborn monitoring to improve comfort and

hygiene in wearing diapers and simplify nurses care procedures.

The applications cited here are a few among many others more we may strive to design

and build as opportunities unlocked by wearable electronics sensors and their silent and

invisible integration into textiles.

As future perspectives for the work presented in this report, we propose to upgrade some

building blocks of the design such as the wireless transmitter and RF oscillator to maximize

power consumption savings. Furthermore, we propose to apply signal processing

techniques to the data gathered from the system and compare them to results obtained in

hospitals environment. Finally, we propose to run clinical tests in a real environment

outside the lab, in a hospital environment and validate the design for professional use. We

also propose other in-vivo tests such as 24/7 monitoring of real patients and analyze data.

57

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Wearable electronic sensors for vital sign monitoring€¦ · Wearable Electronic Sensors for vital sign monitoring Mémoire Marouen Besrour Sous la direction de : Michel Duguay,