KTH Information and

Communication Technology

Hybrid Integration of Active Bio-signal

Cable with Intelligent Electrode. Steps

toward Wearable Pervasive-Healthcare

Applications

Doctoral Thesis

By

GENG YANG

Electronic and Computer Systems

School of Information and Communication Technology

Stockholm, Sweden 2012

TRITA-ICT/ECS AVH 13:04

ISSN 1653-6363

ISRN KTH/ICT/ECS/AVH-13/04-SE

ISBN 978-91-7501-670-2

Royal Institute of Technology

School of Information and Communication Technology

Department of Electronic Systems

Forum 120.

SE-164 40 Kista, SWEDEN

Thesis submitted to the School of Information and Communication Technology (ICT), Department of

Electronic Systems (ES), Royal Institute of Technology (KTH), to be public defended on 2013-04-16 at 13:00

in Sal E, Forum, Kista, Stockholm, Sweden.

Copyright © Geng Yang, 2012

Tryck: Universitetsservice US AB

iii

Abstract

Personalized and pervasive healthcare help seamlessly integrate healthcare and

wellness into people’s daily life, independent of time and space. With the developments in

biomedical sensing technologies nowadays, silicon based integrated circuits have shown

great advantages in terms of tiny physical size, and low power consumption. As a result,

they have been found in many advanced medical applications. In the meanwhile, printed

electronics is considered as a promising approach enabling cost-effective manufacturing of

thin, flexible, and light-weight devices. A hybrid integration of integrated circuits and

printed electronics provides a promising solution for the future wearable healthcare devices.

This thesis first reviews the current approaches for bio-electric signal sensing and the

state-of-the-art designs for biomedical circuit and systems. In the second part, the idea of

Intelligent Electrode and Active Cable for wearable ECG monitoring systems is proposed.

Based on this concept, we design and fabricate two customized IC chips to provide a single

cable solution for long-term healthcare monitoring. The first chip is a digital ASIC with a

serial communication protocol implemented on chip to support data and command packets

transmission between different ASIC chips. Also, it has on-chip memory to buffer the

digital bio-signal. An Intelligent Electrode is formed by embedding the ASIC chip into the

conductive electrode. With the on-chip integrated communication protocol, a wired sensor

network can be established enabling the single cable solution. The ASIC’s controlling logic

is capable of making dynamic network management, thus endows the electrode with local

intelligence. The second chip is a fully integrated mixed-signal SoC. In addition to the

digital controller implemented and verified in the first chip, another 2 key modules are

integrated: a tunable analog front end circuits, and a 6-input SAR ADC. The second chip

works as a networked SoC sensor. The command-based network management is verified

through functional tests using the fabricated SoCs. With the programmable analog front end

circuits, the SoC sensor can be configured to detect a variety of bio-electric signals. EOG,

EMG, ECG, and EEG signals are successfully recorded through in-vivo tests.

iv

This research also explores the potential of using high accurate inkjet printing

technology as an inexpensive integration method and enabling technology to design and

fabricate bio-sensing devices. Performance evaluation of printed electrodes and

interconnections on flexible substrates is made to examine the feasibility of applying them

in the fabrication of Bio-Patch. The reliability of the inkjet printed sliver traces is evaluated

via static bending tests. The measurement results prove that the printed silver lines can offer

a reliable interconnection. In-vivo test results show that the quality of ECG signal sensed by

the printed electrodes is comparable with the one gained by commercial electrodes.

Finally, two Bio-Patch prototypes are presented: one is based on photo paper substrate,

the other on polyimide substrate. These two prototypes are implemented by heterogeneous

integration of the silicon based SoC sensor with cost-effective printed electronics onto the

flexible substrates. The measurement results indicate the SoC operates smoothly with the

printed electronics. Clean ECG signal is successfully recorded from both of the

implemented Bio-Patch prototypes. This versatile SoC sensor can be used in various

applications according to specific requirements. And this heterogeneous system combining

high-level integrated SoC technology and inkjet printing technique provides a promising

solution for future personalized and pervasive healthcare applications.

Keywords: ASIC, SoC, bio-medical electronics, bio-sensor, Bio-Patch, wearable healthcare

system, pervasive healthcare, CMOS, SAR-ADC, Intelligent Electrode, Active Cable,

multi-parameter bio-signal sensor.

iii

Contents

Abstract iii

Contents v

List of Papers viii

List of Acronyms xiii

Acknowledgements xv

1. Introduction ..................................................................................................................... 1

1.1 Motivation and Pervasive Healthcare .......................................................................1

1.2 Work Performed ........................................................................................................3

1.3 Author’s Contributions..............................................................................................5

1.4 Thesis Outline ...........................................................................................................7

1.5 Beyond the Scope of this Work.................................................................................8

2. Bio-electrical Signal Sensing and Current Approaches................................................ 9

2.1 Introduction...............................................................................................................9

2.2 Principles of Bio-potential Measurements ..............................................................10

2.3 Current Approaches for Bio-potential Sensing .......................................................13

2.3.1 Electrode Technology.................................................................................... 13

2.3.2 Latest State-of-the-art Biomedical Circuits and Systems ............................. 15

3. SoC Design, Implementation and in-vivo Tests ........................................................... 17

3.1 SoC Architecture and Implementation....................................................................17

3.1.1 Digital Core................................................................................................... 18

3.1.2 Bio-Electric SoC ........................................................................................... 21

iv

3.2 SoC in-vivo Tests ....................................................................................................27

3.3 Conclusion ..............................................................................................................32

4. System Integration and Bio-Patch Implementation ....................................................33

4.1 Printed Electrode.....................................................................................................33

4.2 Paper based Bio-Patch ............................................................................................37

4.3 Static Bending Tests................................................................................................39

4.4 Polyimide (PI) based Bio-Patch..............................................................................42

4.5 Conclusion ..............................................................................................................45

5. Summary and Future Works .........................................................................................47

5.1 Summary.................................................................................................................47

5.2 Future Works...........................................................................................................48

Bibliography ........................................................................................................................51

List of Papers

The publications produced from the research activities performed in this PhD dissertation

are listed below. All the conference papers and the journal articles are peer-reviewed.

Among the publications, 7 journal articles and conferences papers have been selected to be

appended at the end of this thesis. A brief summary of each of them is presented in the

following lines.

List of Papers included in the thesis:

Journal Papers:

Paper I: G. Yang, L. Xie, M. Mäntysalo, J. Chen, T. Hannu, and L.-R. Zheng

“Bio-Patch Design and Implementation Based on a Low-Power System-on-Chip and

Paper-based Inkjet Printing Technology” IEEE Transactions on Information

Technology in Biomedicine (T-ITB), vol.16, no.6, pp.1043-1050, Nov. 2012.

Paper II: G. Yang, J. Chen, L. Xie, J. Mao, T. Hannu, and L.-R. Zheng “A Hybrid Low

Power Bio-Patch for Body Surface Potential Measurement” IEEE Journal of

Biomedical and Health Informatics (J-BHI), 2013 (accepted).

Conference Papers:

Paper III: G. Yang, J. Chen, F. Jonsson, T. Hannu, and L- R. Zheng, “A

multi-parameter bio-electric ASIC sensor with integrated 2-wire data transmission

protocol for wearable healthcare system,” Design, Automation & Test in Europe

(DATE 2012) pp. 443-448. Mar. 2012.

Paper IV: L. Xie, G. Yang, M. Mäntysalo, F. Jonsson, and L.-R. Zheng, “A

System-on-Chip and Paper-based Inkjet Printed Electrodes for a Hybrid Wearable

Bio-Sensing System,” IEEE 34st Annu. Int. Conf. of the Engineering in Medicine and

Biology Society(EMBC2012), Aug. 2012.

Paper V: (invited) G. Yang, J. Mao, T. Hannu, and L- R. Zheng, “Design of a

Self-organized Intelligent Electrode for Synchronous Measurement of Multiple

Bio-signals in a Wearable Healthcare Monitoring System” IEEE Proc. Of the 3rd

ix

International Symposium on Applied Sciences in Biomedical and Communication

Technologies (ISABEL 2010), pp. 1-5, Oct. 2010.

Paper VI: G. Yang, J. Chen, F. Jonsson, T. Hannu, and L- R. Zheng, “A 1V 78uW

Reconfigurable ASIC Embedded in an Intelligent Electrode for Continuous ECG

Applications” 31st Annual International Conference of the IEEE Engineering in

Medicine and Biology Society (EMBC 2009), pp.2316-2319, Sep. 2009. (One of 15

best student paper finalists)

Paper VII: G. Yang, J. Chen, Y. Cao, T. Hannu, and L- R. Zheng, “A Novel Wearable

ECG Monitoring System Based on Active-Cable and Intelligent Electrodes,” IEEE

Proc. of 10th International Conference on e-Health Networking, Applications and

Services (HealthCom 2008), pp.156-159, Jul. 2008.

List of Papers not included in the thesis, but related:

Journal Papers:

1. L. Xie, G. Yang, M. Mäntysalo, L. L. Xu, F. Jonsson, and L.-R. Zheng ” Heterogeneous

Integration of Bio-Sensing System-on-Chip and Printed Electronics” IEEE Journal on

Emerging and Selected Topics in Circuits and Systems (JETCAS), vol.2, no.4,

pp.672-682, Dec. 2012.

2. J. Chen, L. Rong, F. Jonsson, G. Yang, and L.-R. Zheng, “The Design of All-Digital

Polar Transmitter based on ADPLL and Phase Synchronized ∆Σ Modulator,” IEEE Journal

of Solid-State Circuits (JSSC) vol.47, no.5, pp.1154-1164, May 2012.

Conference Papers:

3. (Invited) G. Yang, J. Chen, F. Jonsson, T. Hannu, and L- R. Zheng, “Bio-chip ASIC and

printed flexible cable on paper substrate for wearable healthcare applications” ACM Proc.

Of the 4th International Symposium on Applied Sciences in Biomedical and Communication

Technologies (ISABEL 2011) Article No. 76, Oct. 2011.

4. Q. Wan, G. Yang, Q. Chen, and L-R. Zheng, “Electrical Performance of Inkjet Printed

Flexible Cable for ECG Monitoring,” IEEE 20th Electrical Performance of Electronic

Packaging and Systems, (EPEPS 2011), pp.231-234, Oct. 2011.

5. G. Yang, J. Chen, T. Hannu, and L- R. Zheng, “An ASIC Solution for Intelligent

x

Electrodes and Active-cable Used in a Wearable ECG Monitoring System,” International

Conference on Biomedical Electronics and Devices (BIODEVICES 2009), pp.209-213, Jan.

2009.

6. G. Yang, J. Chen, T. Hannu, and L- R. Zheng, “Intelligent Electrode Design for

Long-Term ECG Monitoring at Home” IEEE Proc. of the 3rd International Conference on

Pervasive Computing Technologies for Healthcare (PervasiveHealth 2009), pp.1-4, Apr.

2009.

7. G. Yang, Y. Cao, J. Chen, T. Hannu, and L- R. Zheng, “An Active-cable Connected

ECG Monitoring System for Ubiquitous Healthcare,” IEEE Proc. of 3rd International

Conference on Convergence and hybrid Information Technology (ICCIT 2008),

pp.392-397, Nov. 2008.

Summary of the Appended papers:

Paper I: G. Yang, L. Xie, M. Mäntysalo, J. Chen, T. Hannu, and L.-R. Zheng “Bio-Patch

Design and Implementation Based on a Low-Power System-on-Chip and Paper-based

Inkjet Printing Technology” IEEE Transactions on Information Technology in Biomedicine

(T-ITB), vol.16, no.6, pp.1043-1050, Nov. 2012.

A Bio-Patch prototype is demonstrated in this paper. Both the electrodes and

interconnections are implemented by printing conductive nano-particle inks on a flexible

photo paper substrate using inkjet printing technology. A Bio-Patch prototype is developed

by integrating the SoC sensor, a soft battery, printed electrodes and interconnections on a

photo paper substrate. The Bio-Patch can work alone or operate along with other patches to

establish a wired network for synchronous multiple-channel bio-signals recording. The

measurement results show that ECG and EMG are successfully detected in in-vivo tests.

Contribution of the author: Original idea, most of the experimental work, and the draft

of the manuscript.

Paper II: G. Yang, J. Chen, L. Xie, J. Mao, T. Hannu, and L.-R. Zheng “A Hybrid Low

Power Bio-Patch for Body Surface Potential Measurement” IEEE Journal of Biomedical

and Health Informatics (J-BHI), 2013 (accepted).

Three key technologies, including mixed-signal SoC technology, inkjet printing

technology and anisotropic conductive adhesive (ACA) bonding technology, are employed

to implement a wearable Bio-Patch prototype for body surface potential measurement. The

electrodes, interconnections and interposer are implemented by inkjet-printing the silver ink

xi

precisely on a flexible polyimide (PI) substrate. The reliability of printed silver traces on PI

substrate is evaluated by static bending tests. ACA is used to attach the SoC to the printed

interposer and form the flexible hybrid system. Measurement results show that the

implemented Bio-Patch can concurrently detect ECG signals from 8 chest points.

Contribution of the author: Original idea, most of the experimental work, and the draft

of the manuscript.

Paper III: G. Yang, J. Chen, F. Jonsson, T. Hannu, and L- R. Zheng, “A Programmable

Bio-Electric SoC Sensor with 2-Wire Data Transmission for Scalable 14-Channel

Synchronous BioSignal Measurement” Design, Automation & Test in Europe (DATE 2012)

Mar. 2012.

This paper presents the second tape-out of the Bio-Chip: a fully integrated SoC sensor

for multiple bio-electric signals recording. It consists of an analog front end circuit with

tunable bandwidth and programmable gain, a 6-input 8-bit SAR ADC, and a reconfigurable

digital core. The SoC is fabricated in a 0.18-µm 1P6M CMOS technology, occupies an area

of 1.5 × 3.0 mm2, and totally consumes a current of 16.7 µA from a 1.2 V supply. Circuit

performances are reported, including measured gain and bandwidth, CMRR, and input

referred noise. In-vivo tests are also performed to verify the SoC’s performance. EMG, ECG,

and EEG signals are successfully recorded using the implemented SoC sensor.

Contribution of the author: SoC design and implementation. All the test work for SoC

electrical characteristics measurement and functional verification. All the in-vivo tests to

measure bio-signal from volunteers’ body. The draft of the manuscript.

Paper IV: L. Xie, G. Yang, M. Mäntysalo, F. Jonsson, and L.-R. Zheng, “A System-on-Chip

and Paper-based Inkjet Printed Electrodes for a Hybrid Wearable Bio-Sensing System,”

IEEE 34st Annu. Int. Conf. of the Engineering in Medicine and Biology

Society(EMBC2012), Aug. 2012.

This paper describes a hybrid wearable bio-sensing system, which combines the

implemented SoC, and cost-effective Printed Electronics on a flexible paper substrate using

inkjet printing technology. The electrodes are inkjet printed on photo paper substrate and

connected to the SoC for measurements. In-vivo test results show that the quality of ECG

signal sensed by printed electrodes is comparable with commercial electrodes, with noise

level slightly increased The measurement results also indicate the SoC sensor operates

smoothly with the printed electronics. The printed electrodes are flexible, light and thin,

which make the final system comfortable for end-users.

Contribution of the author: Original idea, parts of the experimental work, and the analysis

of the measurement results.

Paper V: G. Yang, J. Mao, T. Hannu, and L- R. Zheng, “Design of a Self-organized

Intelligent Electrode for Synchronous Measurement of Multiple Bio-signals in a Wearable

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Healthcare Monitoring System” IEEE Proc. Of the 3rd International Symposium on

Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010), pp. 1-5,

Oct. 2010.

In addition to the digital core, a gain-bandwidth selectable analog front-end circuit

should be designed and integrated on chip. It allows the circuit to accommodate different

bio-potential signals. A SAR ADC is also included to convert the amplified bio-signal to a

digital format. A full integration solution is achieved by integrating the three key parts

together. In this paper, we present the architecture of the analog front end circuit, and the

structure of the implemented SAR ADC. Post-layout simulation results show that the

analog front end circuit is featured with a tunable gain, and for each selected gain, the

bandwidth is also adjustable via external pins. Since during silicon chip manufacture

process, device mismatch may lead to CMRR degradation, Monte Carlo analysis

(Mismatch only) is performed to investigate the variation of the CMRR, where the

simulation result is much closer to the real circuit performance.

Contribution of the author: The digital core is the re-used IP core from the previous

ASIC. All the design, simulation and lay-out work related to the analog front-end.

Simulation and parts of the lay-out work for SAR ADC. The draft of the manuscript.

Paper VI: G. Yang, J. Chen, F. Jonsson, T. Hannu, and L- R. Zheng, “A 1V 78uW

Reconfigurable ASIC Embedded in an Intelligent Electrode for Continuous ECG

Applications” 31st Annual International Conference of the IEEE Engineering in Medicine

and Biology Society (EMBC 2009), pp.2316-2319, Sep. 2009.

After the function verification on FPGA, the proposed digital core is fabricated in a

standard 0.18 µm CMOS technology (the analog front end circuit and A/D convertor are not

included on chip). The ASIC occupies an area of 1.5 mm × 1.5 mm and is encapsulated in a

CQFP-64 package. Experiments are made to measure the ASIC power consumptions under

different supply voltages and operating frequencies. The minimum power consumption of

78 µW is observed with 1.0 V core voltage running with 6 MHz operating frequency. The

tiny silicon size makes the ASIC possible and suitable to be embedded into an Intelligent

Electrode, and the low power consumption makes it feasible for long-term continuous ECG

monitoring.

Contribution of the author: The digital ASIC implementation following the ASIC

design flow (including VHDL code synthesis, floor-plan, place and route, DRC and

post-layout simulation), PCB design for test board, function verification for the fabricated

chip, power consumption measurement, and the draft of the manuscript.

Paper VII: G. Yang, J. Chen, Y. Cao, T. Hannu, and L- R. Zheng, “A Novel Wearable ECG

Monitoring System Based on Active-Cable and Intelligent Electrodes,” IEEE Proc. of 10th

International Conference on e-Health Networking, Applications and Services (HealthCom

2008), pp.156-159, Jul. 2008.

xiii

In this paper, for the first time, we propose the idea of Intelligent Electrode and Active

Cable for wearable ECG monitoring systems. The block diagram of the digital core and the

architecture of the Active Cable are illustrated. The structure of the command frame and the

algorithm of the operation process are also presented. The digital core is embedded in the

Intelligent Electrode and works as the controlling unit. Its function verification is performed

using FPGA boards. Command packets and data loads are successfully transmitted between

different nodes.

Contribution of the author: The original idea, test method, all of the VHDL coding, all

the experimental work, and the draft of the manuscript.

List of Acronyms

ADC Analog to Digital Convertor

AFE Analog Front End

ASIC Application-Specific Integrated Circuit

Ag/AgCl Silver/Silver Chloride

BW Bandwidth

CMOS Complementary Metal Oxide Semiconductor

CMFB Common Mode Feed Back

CMRR Common Mode Rejection Ratio

CBIA Current Balanced Instrument Amplifier

ECG Electrocardiogram, heart signal (also known as EKG)

EEG Electroencephalogram, brain signal

EOG Electrooculogram, eye signal

EMG Electromyogram, muscle signal

FPGA Field-Programmable Gate Array

FFT Fast Fourier Transform

FSM Finite State Machine

GND Ground

HCI Human Computer Interface

IC Integrated Circuits

ICT Information and Communication Technology

IRN Input-Referred Noise

SoC System on Chip

RAM Random Access Memory

xiv

RFID Radio frequency identification

DPRAM Dual Port Random Access Memory

SPRAM Single Port Random Access Memory

SAR-ADC Successive Approximation Register Analog-Digital Converter

SCL Serial Clock

SDA Serial Data

PWR Power

REF Reference

RF Radio Frequency

PCB Printed Circuit Board

PI Polyimide

PHA Personal Health Assistant

Acknowledgements

I want to express my most sincere thanks to my advisors Prof. Hannu Tenhunen and

Prof. Li-Rong Zheng. They are always supportive and encouraging. I appreciate them for

offering me this position, also for giving me this opportunity to conduct an exciting and

independent research work covering Electrical Engineering and Biomedical Engineering. I

appreciate Prof. Li-Rong’s ability to provide me an uplifting perspective which has inspired

me throughout my research work. Many thanks to Prof. Hannu for his supports allowing me

to join EIT ICT Doctoral Training Program.

IC design is a complicated process. Project leaders and experienced colleagues show

their supports in various ways. Special thanks to Dr. Fredrik Jonsson and Dr. Jian Chen,

who are excellent circuit designers providing me with practical suggestion and support

during circuit design and test. I am grateful to former colleague Ying Cao who helped me a

lot on the DRC of my first ASIC. The constructive discussions with these intelligent

colleagues have inspired me throughout my research, and I believe they are quite valuable

in my future research work. Also many thanks to the powerful server Xcellent, who is a

reliable friend with powerful computational capability, operating much faster than previous

servers.

I am grateful to colleague Li Xie, Matti Mäntysalo (Tampere University of Technology,

Finland), Qiansu Wan, Jia Mao and Linlin Xu who contributed a lot on the bio-sensor

integration, circuit layout, and sensor node miniaturization works by applying the printed

electronics on the flexible substrates.

Many thanks go to Ning Ma, Yang Li (Chang Chun University of Science and

Technology, China), Awet Yemane Weldezion, Shahab Mokarizadeh, as well as the

colleagues in the Department of Electronic Systems, who helped on the bio-signal

extraction experiments, either making contribution as test volunteers or helping on the data

recordings. I also want to thank all colleagues at KTH who make this Ph.D journey more

enjoyable: Dr. Zhuo Zou, Dr. Yasar Amin, Dr. Liang Rong, Dr. Ana Lopéz Cabezas, Yi

Feng, Jue Shen, Zhi Zhang, Zhiying Liu, Qin Zhou, Chuanying Zhai, Xueqian Zhao, Ming

Liu, Shaoteng Liu, and Pei Liu.

xiii

Special thanks go to Dr. Qiang Chen and Dr. Zhonghai Lu for their practical advice on

the daily life in Sweden. Besides, I’d like to thank our secretary Agneta Herling and Alina

Mutntenu for their administrative assistance in travelling and other issues. Thanks for the

maintenance works provided by the people working in IT-Service Group. Special thanks to

Eskil and Fredrik Liljeblad who rescued the IC design files from the crashed hard-drive. I

could not imagine how much time and work would be spent on the redesign of the entire IC

if the four-year design files were permanently lost. To my good friends, Dr. Huimin She and

Dr. Botao Shao, together with whom I came to Sweden, the land of Nobel Prize, and at the

same time started the journey of Ph.D study, thanks for your help and encouragement all

along the way.

Last, but not the least, I give my deepest gratitude to my wife Zhangwei Yu, who is an

outstanding researcher in fiber optics, and I believe that one day in the future she will be an

excellent scientist. For each of my published paper, there is an invisible contribution from

her, reviewing my manuscript and giving me valuable comments. Sincere thanks for her

sacrifices that she has made to let me achieve this goal. Also my son, Joey Jiuning Yang,

like his name, he brings me lots of joys into my life. And to my elder sister in China, your

endless concerns encourage me all the way, I will never let you down. I appreciate my

parents for their endless love and irreplaceable supports. I am also grateful for their

financial support which allowed me to have the college education for bachelor and master

degrees.

The research presented in this dissertation is financially supported by Vinnova (The

Swedish Governmental Agency for Innovation Systems) through the Vinn Excellence

centers program. Also I am grateful for the financial support from the China Scholarship

Council (CSC) for my first three years Ph.D study.

Geng Yang

September 2012

Stockholm

1

Chapter 1

Introduction

1.1 Motivation and Pervasive Healthcare

Consistent with the global population trend, many countries are facing aging problems.

Chronic diseases are becoming the major causes of the death. In EU countries, the heart

disease is the most common cause of death [1]. According to US National Center for Health

Statistics, major chronic diseases such as heart disease, cerebrovascular disease, and

diabetes account for 35.6% of death in US in the year 2005 [2]. Statistics show that over

one-fifth of the world’s elderly population (aged 65 and over) lives in China in 2006, and

the percentage of China’s elderly populace will be tripled to 24% by the year 2050 [3].

The prevalence of chronic diseases inevitably increases the total expenditure on

healthcare and poses a grim challenge to the current healthcare systems worldwide [3].

Traditional healthcare and well-being services are usually offered within hospitals or

medical centers. Citizens with chronic diseases as well as the people in post-surgery state

need continuous monitoring of their health condition, especially the vital signs, until their

health status becomes stable. Patients, as well as their families, also need to collaborate

with their doctor and medical professionals to get informed about their states. Until now,

the monitoring of the health condition of such people is usually accomplished within

medical centers or hospital environments. As a result, measurements of vital signs and the

corresponding diagnosis are carried out in controlled environments. However, this solution

is costly, inefficient and inconvenient for the people with the need of routine checks, since

the patients need to frequently visit the hospital, sometimes on a daily basis, or even worse,

need a long-stay. There are huge requirements to move the routine medical check and

healthcare services from hospital to the home environment, thus release the hospital beds

and other limited resources to the people with urgent needs.

Pervasive and personalized healthcare is a promising solution to provide efficient

2

medical services which could significantly lower down the healthcare cost. Pervasive

healthcare may be understood from two perspectives [4, 5]. Firstly, it is the development

and application of ambient intelligence and assistive technologies for human-centered

healthcare and wellness management. Secondly, it tries to increase both the coverage and

quality of healthcare by making healthcare available to anyone, anytime, and anywhere by

removing location, time and other restraints. It takes the advantages of related advanced

technologies in nowadays, such as advanced information and communication technologies

(ICT) and materials technologies, etc, to implement the design concepts and integrate the

healthcare seamlessly into everyday life [6-9]. Several examples towards pervasive,

ubiquitous and personalized healthcare can be found in the following applications:

Pervasive computing is employed to deliver elder care in [10], which is a system with many

elements of pervasive computing to create intelligent living spaces for the elderly. A

customized sensor node and software are realized in [11] for individual pervasive health

applications, where the underlying hardware and software can be tailored for specific

applications. The results reported in [12-15] show RFID technology applied in different

medical care domains, which create smart medical care or smart hospital. Sensor

technology is regarded as a key driver in the development of home healthcare. References

[16-21]show different body sensors, intelligent sensors, and textile sensors, which are

utilized in personal or pervasive healthcare.

Electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and

Electrooculogram (EOG) signals are the most common bioelectrical signals measured in

clinical practice or for research purposes. They are bioelectrical signals generated by the

activity of the heart, muscles, brain and eyes respectively, showing the characteristics of a

dedicated physical or mental activity. The recording and monitoring of these bio-signals is

used in a large number of applications: such as heart disease diagnosis, muscle activation

analysis, eye movement detection and epilepsy diagnosis. In addition to the bioelectrical

signals, body temperature, blood pressure, and blood glucose, etc. are also commonly

measured vital parameters useful for assessing on the physical condition of a human being.

There are several applications that might require continuous recording of bio-signals

for relatively long time. Two illustrative examples of these applications are Holter

monitoring, as shown in Fig. 1-1(a), and long term recording of multi-channel EEG signals

for epilepsy diagnosis, as shown in Fig 1-1. (b).

A Holter monitor is able to record ECG data for around 24-hour. Currently this is

3

regarded as one of the most efficient methods to detect irregular heart activities. However, a

measurement lead is required to connect one electrode to the Holter device. As a result, for

the Holter monitor, typically 6 to 10 wires are required.

For an EEG monitoring system in the traditional clinical area, more connecting cables

are needed. For example, 20-40 wires are needed for a typical epilepsy EEG monitoring

system. Additional electrodes and connecting cables could be added to the standard set-up

when higher spatial resolution is demanded for a particular application.

In both of the afore mentioned applications, a large number of cumbersome wires are

worn around the body or head all day long, which incur tangling problems, moreover, make

the recording process uncomfortable and inconvenient for the patients. It becomes an

obstacle for the wider use of continuous monitoring system in the daily life. If the

connecting cables bring too much inconvenience, users resist the adoption of wearable

devices. In addition, cable noise and interference bring considerable technical challenges to

the design of signal acquisition and conditioning circuits, which may involve extra needs of

shielding or guarding techniques.

1.2 Work Performed

Based on the above considerations, we propose a wearable bio-sensing system which

(a) (b)

Figure 1-1. (a) ECG Holter monitoring system. (b) High spatial density EEG system (TU

Braunschweig).

4

can be used for continuous home healthcare, as shown in Fig. 1-2, with an application

scenario of multi-channel ECG measurement. A small microchip is embedded in each

electrode, which endows the electrode with local intelligence. In this sense, the electrode is

called Intelligent Electrode. With the integrated microchip, the Intelligent Electrode is able

to obtain high quality bio-signal by conducting the bio-signal amplification, filtering,

quantization, buffering, and transmission tasks directly on-site each electrode.

Different from traditional approaches where the measurements are performed in a

stationary or central device (centralized process), in this work, the bio-signal acquisition

and transmission tasks are distributed onto each electrode (distributed process). By sharing

a serial communication link (Active Cable), the Intelligent Electrodes can communicate

with each other in a serial manner. The communication between Intelligent Electrodes are

command based. The signal flow is illustrated in Fig. 1-2. Intelligent Electrodes detect the

bio-signals on different sites of human body. These bio-signals are converted to digital

format and gathered in one Intelligent Electrode, where these data are wirelessly

transmitted to a smart phone or personal health assistant (PHA) for storage or real-time

processing/display. According to requirements, the data can also be sent to a remote server

or hospital/clinic center for storage, where further diagnosis can be made by the remote

physicians. The concept of Intelligent Electrode and Active Cable are firstly reported in the

attached Paper VII [22].

The advantages of this Intelligent Electrode and Active Cable based wearable

healthcare system include:

One-chip multi-sensor. The customized microchip is designed to sense a variety of

bio-signals enabling the Intelligent Electrode to detect multiple bio-signals.

GP

RS

/3G

N

etw

ork

Figure 1-2. Intelligent Electrode and Active Cable based wearable healthcare monitoring system.

5

Drastically reduce the number of connecting cables. All sensing electrodes are

connected by sharing a single Active Cable. As a result, user comfort is improved.

On-chip implemented serial communication protocol enables the establishment of a

body area network over a group of Intelligent Electrodes.

To realize such a system, principally two core research challenges are present:

1) low-power and high-performance integrated circuits,

2) new heterogeneous integration technologies enabling sensor nodes/devices

miniaturization.

Accordingly, the contributions and focuses of this thesis can be summarized as follows:

Design and implementation of the application-specific microchip and its function/

performance verification after chip fabrication.

Design, fabricate and test the customized electrode patterns and evaluate their

compatibility with the fabricated microchip through in-vivo tests.

Performance evaluation of the printed conductive metal traces, interconnections and

other patterns on flexible substrate.

Propose feasible solutions and implement prototypes to demonstrate the concept of

new wearable bio-sensing devices which could be used for next generation

personalized/pervasive healthcare.

1.3 Author’s Contributions

The main goal of this thesis is to propose, implement and evaluate the customized

microchip and the relative integration technologies to enable the realization of wearable

bio-signal monitoring systems for pervasive or personalized healthcare. To comply with the

key requirement for wearable systems, the microchip needs to fulfill the stringent function

requirements of bio-signal sensing, processing and transmitting. Great emphases are put on

the design and implementation of the microchip. The building blocks are integrated on chip

step by step. Starting with a digital controlling unit, followed by the design and

implementation of analog read-out circuit and A/D convertor, and finally a full integration

6

solution is made by integrating the 3 building blocks on the same chip. Until now, two

tape-outs have been made and the relative function/performance verification has been

conducted through in-vivo tests. The author’s contribution can be concluded as follows:

The configurable architecture of the digital controller is reported in [22]. For the first

time, we propose the dual operating modes of the custom designed digital controller:

Main-Mode (responsible for network management) and Sensing-Mode (responsible for

bio-signal measurement). The author is responsible for the behavioral definition and the

VHDL coding for the digital core’s two operation modes, including the serial

communication protocol definition, RAM read/write control, RAM switch control, the

definition of broadcast commands and unicast commands, the definition of command frame

structure and data frame structure, the scheme of transmitting or receiving (interpreting) the

command/data packet, the definition of serial port, ADC interface and wireless module

interface. The author is also responsible for the verification of the above behavioral level

codes on FPGA platforms. The concept and measurement results are reported in [22].

After the functional verification on an FPGA, the author moves forward with the

application specific integrated circuits (ASIC) implementation. The author is responsible to

implement the VHDL codes to a real digital controller by following the ASIC design flow.

The author is also responsible for the following measurements when the microchip is

fabricated. This is the first ASIC tape-out of this thesis work. UMC 0.18 µm CMOS

technology is used and the total silicon area is 1.5 mm × 1.5 mm. The digital ASIC was

fabricated in late 2008 and measurements were made in 2009. Design details are presented

in [23] and [24]. ASIC’s measurement results are reported in [25].

In addition to the digital controller, a full integration solution needs analog front end

circuit and A/D convertor. In paper [26], we report the circuit architecture of the analog

front end and the SAR ADC. Post-layout simulation results, including CMRR, Monte-Carlo

simulation, gain-bandwidth frequency response, and transient simulation of an ECG signal

segment are also included. The author is responsible for all related works reported in paper

[26]. With the accomplishment of this paper, the circuit is ready for the second tape-out.

The second tape-out is a mixed-signal System on Chip (SoC), including three parts: 1)

analog front end (AFE); 2) SAR ADC; and 3) digital core. The tape-out was made in late

2010 using UMC 0.18 µm 1P6M CMOS technology with a total size of 1.5 mm × 3 mm.

Consequently, the measurements were made in 2011. Digital signal transmission

experiments using the printed silver traces on paper substrates are successfully conducted.

7

Results can be found in [27] and [28]. A full report of the fabricated SoC’s performance is

presented in [29], including circuit schematic, theory analysis, measurement results for

circuit’s electric performances as well as experimental results obtained from healthy

volunteers. The author is responsible for all the related works reported in [29], including the

schematic, simulation and layout of the chip, as well as the experiments after chip

fabrication.

The performance of the printed electrodes is investigated in [30]. Test results show that

the printed electrodes using inkjet printing technology have a good compatibility with the

fabricated SoC sensor. The author’s contribution to this paper is mainly on the design of

SoC’s measurement PCB board and applying it in the in-vivo test.

Finally, system integration is performed by integrating the SoC on flexible substrates

using the inkjet printing technology. A paper-based Bio-Patch prototype is presented in [31].

The SoC sensor is attached on a flexible photo paper substrate, where inkjet printing

technology is employed to implement conductive electrodes and interconnections. ECG

signal is successfully obtained from in-vivo test. Due to the physical limitation of the photo

paper substrate, the author moves forward with a more stable polyimide (PI) substrate. A

new Bio-Patch prototype is proposed and implemented in [32]. An alternative packaging

method using printed interposer is also studied. The reliability of the inkjet printed traces is

evaluated via static bending tests. Finally, multi-channel ECG signals are successfully

recorded using the implemented Bio-Patch prototype [32].

1.4 Thesis Outline

The remaining parts of the thesis are organized as follows: Chapter 2 reviews the

principle of bio-potential signal measurements and gives an overview of current electrode

technology as well as current the-state-of-the-art designs in biomedical circuits and systems.

Chapter 3 presents the proposed fully integrated SoC for multi-parameter bio-signal

sensing. The circuit implementation and its electrical performances are demonstrated.

Different bio-signals, including ECG, EMG, EOG, and EEG are recorded through in-vivo

experiments. Chapter 4 shows the system integration and the implementation of Bio-Patch

prototypes using the inkjet printing technology. The performance of the printed electrodes is

investigated in this chapter. Two cases of hybrid integration of the SoC with printing

technology on the flexible substrate are also demonstrated: one is based on photo paper

8

substrate; the other on the PI substrate. Finally, Chapter 5 concludes the thesis, and

discusses the research works that could be continued in the future.

This thesis is a combination of multidisciplinary research works that crosses

Biomedical Circuits and System Design, Printed Electronics and Life Sciences. The main

emphases of this thesis are on the microelectronic design and its related system integration,

especially for the booming research areas such as Smart Sensors and Wearable Electronics

for Pervasive Healthcare applications.

1.5 Beyond the Scope of this Work

Biocompatibility is not studied in this work. The conductive electrodes can be

developed with different materials, for example, conductive metals, conductive polymer,

and textile electrodes etc. Various manufacture processes can be applied to fabricate these

electrodes, for example, chemical etching, inkjet printing, screen printing etc. However, in

this study, we focus on function verification of the implemented circuits, and investigate the

possibility and feasibility of integrating the silicon SoC with Printed Electronics to form a

hybrid bio-sensing system. The characteristics and biocompatibility of different electrodes

are not considered in this work.

Wireless transmission module is not integrated on the current Bio-Patch prototypes.

Two solutions are available for the wireless communication: 1) a fully integrated solution

with the radio circuits integrated on the same chip. In this case, the knowledge of integrated

radio frequency (RF) transmitters in [33] and [34] can be used. 2) using an off-chip wireless

module, where low energy Bluetooth [35] is a good candidate for RF solution.

9

Chapter 2

Bio-electrical Signal Sensing and Current

Approaches

2.1 Introduction

This chapter briefly introduces the nature of bio-electrical signals and the principles of

bio-electrical measurements. The bio-potential signals discussed below mainly include the

ECG, EMG, EOG, and EEG. They are widely used in clinical areas or non-clinical

applications, and corresponding to the activity of respective organs or tissues. The

bio-potential signals are generated due to the electrical activity of the living cells in human

body, and these bio-signals can be traced back to a cell level [36]. The electrical potential

generated by a propagating action through a nerve cell is very small and to record it with

non-invasively methods is specially challenging. The bio-potential signal detected on the

surface of the heart, brain or the human skin reflects the summation of the synchronous

activity of thousands or millions of cells/neurons that have similar spatial orientation [36].

Organs or tissues, such as the heart, brain, muscles and eyes, conduct their function

through endogenous electrical stimulation [37]. For example, the contraction of the cardiac

muscles on the heart during a cardiac cycle generates a bioelectrical activity that give rise to

the signal called ECG. Neuronal activity in the human cortex produce bioelectrical activity

that allows the recording of the EEG signal. The activity of muscles, such as contraction

and relaxation, generates bioelectrical potentials that when record produce an EMG signal.

Eye movements cause the dipole by resting potential of the eyes when changing direction,

resulting in a bio-potential signal known as EOG. Measurements of these bioelectrical

signals from the body can provide valuable information for medical diagnosis. For instance,

by analyzing an ECG waveform, abnormal heart beats or arrhythmias can be diagnosed.

10

EEG signals are commonly used to interpret and identify epileptic seizure events by the

neurologists. Nowadays, EEG is further investigated for brain-computer interface. EMG

signals are helpful in assessing muscle function level for the athletes, also they can be used

to drive the prosthesis for the disabled people. EOG signals can be used to trace the eye

movement, eye position, sleep state analysis and the diagnosis of balance disorders.

2.2 Principles of Bio-potential Measurements

Bio-potential recordings of ECG, EOG, EMG, and EEG are indispensible for clinical or

research applications. With the help of modern bioinstrumentation technology, they can be

recorded in a noninvasive way [38].

Table I lists the features, noise source of these bio-potential signals and their

representative applications [39] [40].

The common features of bio-potentials are:

Small amplitudes (from 10 µV to several mV),

Low frequency range (from DC level to several kHz).

The common problems encountered for the acquisitions of such signals are:

Environmental interference (power line, electromagnetic, etc.),

TABLE I

FEATURES AND APPLICATIONS OF BIO-POTENTIAL [39] [40]

BIO-SIGNAL AMPLITUDE

(MV)

BANDWIDTH

(HZ) MEASUREMENT ERROR SOURCE

SELECTED

APPLICATIONS

ECG 1-5 0.5-200 Motion artifact,

50/60 Hz power-line interface

Diagnosis of

arrhythmia.

EEG 0.001-0.05 0.5-100

RF noise, 50/60 Hz Sleep studies,

seizure detection

EMG 1-10 20-1500 RF noise, 50/60 Hz Muscle function,

prosthesis

EOG 0.4-1

DC-30 Motion artifact, skin potential Eye position

track, sleep state.

11

Biological interference (motion noise, noise from skin and electrodes, etc.).

Figure 2-1. illustrates the electrodes placement setup for the recording of specific

bio-potential measurements.

ECG Measurement

The ECG signal is often recorded by placing electrodes on the torso, as shown in Fig.

2-1a. The ECG signal has an amplitude in the range of 1~5 mV and frequency contents

ranging from 0.5 Hz to 200 Hz. The ECG is originated by the cardiogenic bio-electrical

activity of the heart when the cardiac muscle depolarizes or repolarizes during each

heartbeat. Usually multiple electrodes are used and ECG recording is widely accepted as a

good way to measure and diagnose abnormal rhythms of the heart [41], particularly

Figure. 2-1. Electrodes placement for the bio-potential signals measurements. (a) ECG recording using

right arm (RA), left arm (LA), reference (Ref), and six chest (Cen) electrodes. (b) EMG recording with

two electrodes places on biceps and the reference electrode placed on the forearm. (c) EEG recording

with selected electrodes locations from the standard 10-20 EEG lead system with an ear as reference. (d)

EOG recording with electrodes placed up and down and sides of the eyes along with a reference placed

behind an ear.

12

abnormal rhythms caused by the damaged tissue of the heart.

EMG Measurement

Surface EMG is a recording of electrical activity produced by muscles. It is generated

by muscle cells’ contraction. The signal can be used to evaluate muscles’ activation level.

Measured surface EMG potentials range between 1 mV and 10 mV, depending on the

contraction level of the muscle tissues, and frequency ranges from 20 Hz to 1000 Hz.

Figure 2-1b shows the preferred electrodes placement for the EMG detection, where two

electrodes (one pair of electrodes) are placed on the biceps, and a reference electrode is

placed on far electrically unrelated skin.

EEG Measurement

Surface EEG is a summation of synchronous activity of a great number of neurons that

have the same spatial orientation. Since the electrical potential generated by a single neuron

is too small to be picked, the EEG signal reflects the brain activity where thousands or

millions of neurons fire together. The electrical potentials captured on the scalp surface are

characterized by extremely small amplitudes, sometimes too weak to be distinguished from

the background noise, in the range of 1 µV to 50 µV, and the frequency ranges from 0.5 Hz

to 100 Hz. Due to this reason, EEG measurement poses a great challenge to the read-out

circuit.

Figure 2-1c demonstrates the placement of the selected scalp electrodes for the

standard 10-20 leads EEG system, where several sensing electrodes are located on the

user’s forehead, and one ear is taken as the reference. This multiple leads system allows the

diagnosis of seizure spikes as well as the feature extraction in sleep study [42, 43].

EOG Measurement

EOG is the resulting signal from the eyeballs movements [44]. Electrical potentials are

generated when the eyeballs move in a horizontal or vertical direction. These electrical

signals can be detected via electrodes placed on specific positions around the eye.

Corresponding electrodes placement setup is shown in Fig. 2-1d.

The main applications are for ophthalmological diagnosis and eye movements

tracking. The EOG signal is small (0.4 mV to 1 mV) and has low frequency components

(from near DC to 30 Hz) [45, 46]. Amplifiers with a suitable gain and a good band pass

response between near DC and 30 Hz are desirable.

The principles of bio-potential recording techniques, especially the detailed electrode

placement on the human body for ECG, EMG, EEG, and EOG measurements are

13

introduced above. The in-vivo bio-potential extraction experiments presented in Chapter 3

will follow the electrode deployment scheme presented in Fig. 2-1.

2.3 Current Approaches for Bio-potential Sensing

This section contains a brief overview about electrode technology as well as the latest

state-of-the-art circuits and systems for bioelectrical sensing.

2.3.1 Electrode Technology

All bio-electrical signal acquisitions are made with the aid of electrodes which may be

customized for specific needs [47]. The electrodes can be classified into different types

according to their invasiveness. In this thesis, we focus on the least invasive one: the

surface electrodes. Different types of electrodes are applied to interface the human body

with the recording instruments/devices. According to the structure, the non-invasive,

surface-attached electrodes can also be divided as: passive-electrode (no electronic inside),

and active-electrode (off-the-shelf components inside). In this thesis we propose the idea of

Intelligent Electrode (ASIC/SoC integrated inside), and based on this concept we further

develop several Bio-Patch prototypes, combining the benefits of the integrated circuits and

printed electronics, which will be discussed in Chapter 4.

Passive Electrode

Electrodes form an electrical interface between the human body and the sensing

electronic circuits. They are regarded as a class of sensors that transduce bio-potentials

from the human body to the measuring electronic circuits where the signals are sensed and

processed. The conventional adhesive passive silver/silver chloride (Ag/AgCl) electrodes

are commonly found in clinical and research applications today. The standard Ag/AgCl

electrodes have been well-characterized and studied over many decades [48-50]. With

proper preparation and electrodes deployment, they can provide good signal quality at a

relatively low cost.

However, drawbacks and several limitations associated to the use of Ag/AgCl gel

electrodes still do exist. For the monitoring of chronic disease, conventional Ag/AgCl

wet/pre-gelled electrodes can possibly produce irritation on the skin due to the long time

attachment on a specific location on the skin (for instance, burn units [51] or neonatal care

[52, 53]). In addition, the signal quality and system performance degrade when the

14

electrolyte gel dries out [48]. To address this problem, dry electrodes [48, 54] and textile

electrodes [55, 56] are proposed as alternatives. However, most often textile electrodes

present low conductivity limiting its applicability. For the dry contact electrode, comparing

with its counter part - wet electrode, it is sensitive to motion artifact due to the

comparatively unstable mechanical and electrical contact with the skin.

Active Electrode

In recent years, many attempts have been made to improve the traditional electrode

technology. Several research groups are developing active electrode. The bio-signal

amplification/buffering takes place at the electrode site, therefore an output signal with

much better quality can be achieved.

The relative low spread use of active electrodes, in contrast with the world wide spread

standard electrode, is driven by novel applications requiring continuous bio-potential

recording in very specific scenarios. Examples can be found in Fig. 2-2.

The latest developments on active electrodes are presented as follows: Thomas Degen

et al. presents a two-wired buffer electrode in [57], as shown in Fig. 2-2a. Yu Mike Chi et al.

developed a non-contact active electrode for ECG/EEG applications [54, 58-60], as shown

in Fig. 2-2b. Several attempts have been made to integrate active electrode in chairs (Fig.

2-2c) [61], and beds (Fig. 2-2d) [62] to obtain the user’s cardiac signal. A T-shirt based

wearable ECG monitoring system is proposed in [63].

Although active electrodes are featured with improved immunity against environmental

interferences [57], they are not used as often as expected yet. Firstly, a survey of published

works shows that active electrodes are almost exclusively designed using off-the-shelf

components, such as operational amplifiers [57]. The use of many off-the-shelf components

leads to a cumbersome sensor size. Moreover, as the components carrier, the PCB itself is

stiff. As a result, the characteristics of active electrode make the development of wearable

(a) (b) (c) (d)

Figure 2-2. The latest active electrode technology: (a) two-wired buffer electrode [57]; (b) non-contact

active electrode [58]; (c) active electrode in chairs [61]; (d) active electrode in bed [62].

15

measurement systems very challenging. Secondly, in addition to the signal leads, power

leads VCC and ground GND are required for each active electrode, increasing the number

of connecting cables in comparison with using passive electrodes. The resulting size and

difficulty to integrate with garments are major limiting factors when considering

implementing more complex circuits into the active electrode using the off-the-shelf

components.

2.3.2 Latest State-of-the-art Biomedical Circuits and Systems

With the developments in complementary metal-oxide semiconductor (CMOS)

technology, fully integrated design has shown great advantages in many advanced medical

applications, in terms of low-power consumption, high-level integration and high

performance [64-67]. Comparing with its counterpart: the off-the-shelf components based

active electrode, the IC solution is capable of integrating more complex circuit blocks with

a much lower power consumption and smaller physical size.

In the past decade, effort has been focused on developing biomedical circuits to enable

physiological monitoring in novel scenarios as well as personalized healthcare applications.

Since such biomedical circuits usually require battery operation or even being wirelessly

powered, great emphasis has been paid on ultra-low power design, thus enabling the

sensing circuits to operate for days or even months by a small battery or a wireless energy

scavenger system. In addition, since those sensing devices or sensors need to be used in

direct contact with the skin for relatively long time, more and more attentions are paid on

sensor miniaturization. The final goal with such miniaturization is that by making the

sensing circuit as small as possible, the resulting sensor should be unobtrusive and easy to

be attached on human body or integrated into a wearable system. Examples can be found in

battery-powered wearable or implantable medical devices: the bio-amplifier presented in

[60, 68-71] can sense ECG activity; the ASIC reported in [72] can detect heart-rate

variation; the implantable system in [73] senses the EMG signal for intelligent prostheses

control; acquisition of EEG is performed in [74-77]; neural recording and neural tissue

stimulator are presented in [78-86] and [87-93].

16

17

Chapter 3

SoC Design, Implementation and in-vivo Tests

In this chapter, the design and implementation of a Bio-electric SoC are presented. The

performance of the fabricated SoC is assessed via in-vivo test. A serial communication

protocol is implemented on chip, which enables the system to automatically detect the

sensors attached to the serial link, thus realizes a self-managed network. By configuring the

SoC to the specific gain and bandwidth, 4 different bio-signals, including ECG, EMG, EOG

and EEG are successfully recorded.

3.1 SoC Architecture and Implementation

The proposed SoC is a mixed-signal chip, which can be divided to 3 parts: 1) an analog

front end (AFE), which is used to amply and process the weak bio-signal; 2) a successive

approximation register analog to digital converter (SAR ADC), which is employed to

convert the analog signal to a digital format; and 3) a digital core, which is responsible for

controlling logic. The overall architecture of the Bio-electric SoC is presented in Fig. 3-1.

The proposed SoC is developed step by step. We firstly start with the digital core part.

It was designed and fabricated in 2008 and is the first IC tape-out of this thesis work. After

Figure 3-1. Overall architecture of the Bio-electric SoC.

18

a satisfactory functional verification of this digital ASIC, we moved forward to the design

of AFE and ADC, and finally a full integration was made by combining the 3 building

blocks on the same chip. The mixed-signal SoC is the second IC tape-out of this

dissertation work and was made in 2010.

3.1.1 Digital Core

As the controlling logic, the Digital Core is responsible for digital bio-signal

acquisition, buffer and transmission. In addition, the Digital Core also takes charge of

communication between sensor nodes and the network management. The architecture of the

proposed Digital Core is illustrated in Fig. 3-2. It includes the following features:

Various interfaces: ADC interface (responsible for ADC control and digital data

collection when the ADC output is ready.); RAM interface (use the on chip RAM

to buffer the digital bio-signal); wireless interface (the measured bio-signals are

gathered on one ASIC, and finally sent out via a wireless module).

On chip RAM, which enables the Digital Core to buffer the digitized bio-signal.

On chip implemented 2-wire serial transmission protocol, including the definition

of the frame structure for broadcast commands and unicast commands. With this

protocol, a body area network can be established and managed.

Since a typical body area network requires at least two types of sensor nodes: one is

responsible for sensing one or several specific bio-signals from human body, and the other

Figure 3-2. Architecture of Digital Core.

19

type operates as a controller to manage the whole network. Our innovative contribution in

this work is to propose and implement the two operating modes on the same chip. The

advantages of this dual-operation mode architecture include:

avoiding separate IC tape-out (also avoiding the use of two different types of chips

in real applications), thus lower down the total cost and simplify the assembly

process;

easy configuration: the chip’s operation mode can be easily configured by setting

the control pin to logic ‘1’ or ‘0’ using an external switch. The chip can be

configured either to a Sensing Node (SN) or the Main Node (MN).

The difference between the two operating modes exists in:

SN mode: The main function of a SN is to sample, process and buffer the bio-signal.

Each SN has a 4-bit address. The bio-signal from the subject’s skin is amplified and

digitized. Subsequently, the digital bio-signal is picked up and buffered temporarily in SN’s

on-chip RAM. If one RAM is full, the coming data are automatically switched to the

second RAM. Before the on chip memory is full, the stored data will be periodically

collected by the MN.

MN mode: The main role of the MN is to manage the whole network using a variety of

commands. SN-Chain Scan (SNCS) process is initiated when the system is powered up or

reset. During SNCS process, all SNs in the chain are scanned, the address of each active SN

is stored in the MN’s address buffer. Soon after SNCS is completed, bio-signal data

collection process is initiated: the MN visits each SN in a serial manner. Collected data are

buffered in a MN’s on-chip RAM. Before the storage is full, the MN forwards these data to

the outside world via an external wireless transmission module, then it continues with the

next round data collection.

20

The proposed Digital Core is implemented into a silicon chip by following the digital

ASIC design flow: In brief, firstly, according to function specification, the Digital Core is

divided into several function modules; each module is described in behavioral VHDL codes;

the codes are simulated and verified in Modelsim; then followed by the functional

verification using FGPA board; after that, the codes are synthesized using Synopsys and

finally floor-plan, place-and-route are performed using SoCencounter, and generated GDSII

file is sent to the foundry for ASIC manufacture. The Digital Core is fabricated in a 1P6M

0.18 µm mixed-mode CMOS technology occupying a total area of 1.5 mm × 1.5 mm. The

microphotograph of the fabricated ASIC is shown in Fig. 3-3a. A CQFP-64 package is

adopted to encapsulate the ASIC. A 6 cm × 5 cm circuit board is designed to verify the

ASIC function blocks and measure the power consumption, as shown in Fig. 3-3b.

The SNCS flow is described in Fig. 3-4a. The MN broadcasts a command packet

containing a target SN address to the Active Cable. If the target SN is available, an

acknowledgement frame is sent back immediately. Otherwise, if the target SN does not exist,

no acknowledgement will be generated before MN’s timer overflow. Subsequently, the MN

asserts the absence of the target SN, and continues with the next scan until all SNs are

scanned. The current system supports 14 SNs and 1 MN. Fig. 3-4b illustrates the structure of

the command packet. Fig. 3-4c shows the screenshot of SCL and SDA on an oscilloscope

during SNCS. The ‘start’ and ‘stop’ commands for the implemented serial communication

(a) (b)

Figure 3-3 (a) Microphotograph of the ASIC fabricated in 0.18 µm CMOS. (b) Measurement

board, ASIC with packaged and the silicon die of the fabricated ASIC.

21

protocol are compatible with Philips I2C, while the definitions for the rest of commands, for

instance, broadcast and unicast commands, are custom-designed. The average current

consumptions of the digital core for the two operation modes are almost the same: 12.7 µA for

the MN, and 12.6 µA for a SN from 1.2 V supply with a 1 MHz system clock. More

measurement results can be found in the attached Paper VI [25].

3.1.2 Bio-Electric SoC

After the implementation of the Digital Core, we move forward to the AFE design.

Since bio-potential signals exhibit weak amplitudes and low frequencies [94], these features

bring considerable challenges to the bio-signal read-out circuit. A desirable front end circuit

should have programmable gain and bandwidth, thus it can be configured to accommodate

different bio-signals. The circuit is also required to fulfill the stringent needs for the

bio-signal detection: high input impedance, low power and low noise. The circuit should be

SN

addr(4bit)

Mem start

addr(10bit)

Ack

(3bit)

Command frame (31bit)

...Pwr

Up

Broadcast

Cmd(4bit)

Mem stop

addr(10bit)

Bio-Data

CollectionSN- Scan

Syn-Sample

CMDSN- Scan

(a) (b)

(c)

Figure 3-4 (a) Command-based SN-Chain Scan (SNCS) process. (b) Packet structure for the

command frame. (c) Oscilloscope screenshot of the SCL and SDA.

22

able to pick up these weak signals from a noisy environment: amplify the target signal but

effectively reject the unwanted noise.

A programmable three-stage AFE is implemented in this design. The block diagram of

the AFE is illustrated in Fig. 3-1. Stage1 is mainly composed of a current balanced

instrumentation amplifier and a common mode feed back circuit. Stage1 provides a fixed

gain of 7. The differential outputs of Stage1 are AC-coupled to Stage2. The close-loop gain

of Stage2 is set to 12. Stage3 provides a tunable gain by tuning the equivalent capacitance

of its inner capacitor bank via three external switches. The in-band gain of the Stage3 can

be configured to eight different values ranging from 2 to 19. According to simulation results,

the three-stage front-end circuit can totally provide a tunable close-loop gain ranging from

45 dB to 64 dB. The low-pass frequency of the front-end can be tuned to 80 Hz, 260 Hz,

400 Hz or 1 kHz by varying the on-chip load capacitance through the external switches.

The three-stage gain-bandwidth tunable architecture allows the adjustment of the gain of

the amplification chain to an optimum value, while avoiding saturation distortion. The AFE

(a)

(b)

Figure 3-5. (a) Simplified architecture of the 6-input 8-bit SAR ADC. (b) Timing sequence of one

A/D conversion.

23

totally draws a current of 2.3 µA from a 1.2 V supply. Detailed schematics for each stage

and the corresponding theory analysis are reported in Paper III [29].

A fully integrated solution needs an ADC [95, 96], since the digital data are required

for data transmission and the backend digital signal processing. A SAR ADC is chosen as

the quantization module due to its relatively good tradeoffs between power efficiency,

conversion speed and resolution [97-99]. Fig. 3-5a illustrates the architecture of the

implemented 6-input 8-bit charge-redistribution SAR ADC [100]. It converts an input

voltage with binary-searching method. The circuit’s physical layout is implemented in a

fully differential architecture. The Bandgap module of the ADC generates a temperature

independent 800 mV reference (Vref) signal which severs as a reference voltage during A/D

conversion. A MUX with 3 control pins is used to select one from six inputs to connect to

the capacitive binary search array for A/D conversion. The input5 is reserved for the

amplified bio-signal from the AFE; the other five inputs are available for future extension

for other bio-signal sensing. A finite-state machine (FSM) controller controls the switches

of the ADC capacitor array during each conversion step. The timing sequence is shown in

Fig. 3-5b, where each A/D conversion is divided 8 steps, starting from the most significant

bit and ending with the least significant bit. Each A/D conversion takes 30 clock cycles. The

ADC sample rate is proportional to the external clock, for example, the ADC can provide a

sample rate of 1 KS/s with a external clock frequency of 30 kHz. Similarly, a sample rate of

3.3 KS/s can be obtained with a external clock of 100 kHz. The ADC takes an area of 480

µm × 580 µm and consumes 1.8 µA from a 1.2 V supply (30 kHz clock frequency).

Figure 3-6. Chip microphotograph with its building blocks marked.

24

The Bio-electric SoC is accomplished by packing the Digital Core, AFE, and ADC on

the same chip. The SoC is fabricated in a standard 0.18-µm Mixed-Mode 1P6M CMOS

process and totally occupies 1.5 mm × 3 mm silicon area with a operating voltage ranging

from 1.2 V to 3.3 V. The chip microphotograph is shown in Fig. 3-6. A printed circuit board

(PCB) is designed for SoC electric performance evaluation and in-vivo tests. A photograph

of the measurement PCB is shown in Fig. 3-7. The in-vivo experiments performed and

shown in the following parts of this chapter are based on this PCB.

In order to accommodate different bio-electric signals, the AFE is designed to have

tunable gain and bandwidth. An intuitive comparison is made in Fig. 3-8. The AFE

gain-bandwidth post layout simulation result is plotted in Fig. 3-8a, and gain-bandwidth

measurement result from a fabricated SoC is plotted in Fig. 3-8b. It can be observed that the

measured gain and frequency response are in line with the circuit simulation results. The

only difference observed that the measured gain is slightly smaller than the simulation

result, for example, the measured largest gain (63 dB) is a little bit smaller than the

simulated value (64 dB). This phenomenon may be due to the attenuation of the unit buffer

located at the output port of the AFE (the function of this unit buffer is to increase the AFE

driving capability). As a result, the measured gain is slightly smaller than the simulation

result. The gain or bandwidth can be independently adjusted via external pins. Totally 8

different gain, ranging from 45 dB to 63 dB, can be selected (three of them chosen for

Figure 3-7. SoC measurement PCB.

25

illustration in Fig. 3-8a and Fig. 3-8b). For each selected gain, four user-programmable

3-dB bandwidths can be obtained using bandwidth switches. The upper part of Fig. 3-8b

shows the case of 3-dB bandwidth adjustment with an almost consistent gain with the low

cut-off frequency around 0.3 Hz and the high cut-off frequency adjustable from 85 Hz to

850 Hz.

Due to its weak amplitude, the bio-signal is prone to be contaminated by different noise

sources. Practical problems may be encountered during bio-signal recording process which

0.1 1 10 100 100010

20

30

40

50

60

70

80

Gain = 64

Gain = 59

Gain = 51G

ain

(dB

)

Frequency (Hz)

BW adjustment

through BW switches

(a)

0.1 1 10 100 1000

30

35

40

45

50

55

60

65 63dB; 0.37~850 Hz

57dB; 0.3~1.3k Hz

Frequency (Hz)

Gain

(d

B)

BW Adjustment

Gain:62 dB; BW:0.30~200 Hz

Gain:62 dB; BW:0.25~160 Hz

Gain:61 dB; BW:0.33~85 Hz

49dB; 0.35~1.5k Hz

(b)

Figure 3-8. (a) AFE Gain-bandwidth simulation result. (b) AFE Gain-bandwidth measurement result.

26

arise from physiological and environmental electronic noise sources. Physiological sources

of interference are motion noise, muscle noise, etc. Electrical interference arises from the

usual sources: 50-Hz power lines, and radio frequencies (RF) noise which are electrically or

magnetically induced interference [40]. An effective approach to mitigate this noise

influence is to apply a differential architecture in the amplifier design. Fully differential

architecture is employed for the AFE design, which enables the effective rejection of the

common mode interference. As a result, any common mode signal fed to the AFE input

ports will be effectively removed, while the differential signals at the two sites will be

amplified. 50-Hz power-line interference is the most commonly observed noise in the

measured bio-signals [71, 101]. Since the power lines noise shows itself as a common mode

signal on the two input ports, it will be removed by the circuit subtraction.

The circuit noise is analyzed by connecting the buffer output to a signal analyzer: HP

Dynamic Signal Analyzer 35670A. In order to measure input-referred noise (IRN), AFE’s

two input terminals are shorted to ground and the output noise is measured with the signal

analyzer. The measured noise divided by the measured gain at the closet frequency point

equals IRN. Fig. 3-9 shows the IRN spectrum density. The noise floor of -145 dB

Vrms/√Hz (56 nV/√Hz) is observed. It can also be observed that the AFE’s low frequency

noise is mainly dominated by the flicker noise. At higher frequencies, the noise level

approaches the thermal noise limit of the circuit’s differential input pair. The SoC’s total

power consumption is 20 µW from a 1.2 V battery. The power breakdown is shown in Fig.

100

101

102

-180

-160

-140

-120

-100

Figure 3-9. Input-referred noise of the analog front-end.

27

3-10, where the power consumptions for Digital Core’s two operating modes (SN mode and

MN mode) are almost the same, around 15 µW from 1.2 V supply.

3.2 SoC in-vivo Tests

In order to evaluate the SoC’s performance, the fabricated SoC is verified in the in-vivo

tests by recording real bio-electrical signals from a healthy volunteer. Figure 3-11 illustrates

the gain-bandwidth setup which are applied to the following in-vivo tests. The gain of 49

Figure 3-10. SoC power breakdown.

0.1 1 10 100 100030

35

40

45

50

55

60

65

70

EEG: 63 dB; 0.37~850Hz

EOG: 57 dB; 0.25~190Hz

EMG: 57 dB; 17Hz~1.5KHz

ECG: 49 dB; 0.35Hz~1.5KHz

57dB;0.25Hz~190Hz

63dB;0.37Hz~850Hz

57dB;17Hz~1.5KHz

Frequency (Hz)

Ga

in (

dB

)

49dB;0.35Hz~1.5KHz

Figure 3-11. Gain and bandwidth setup for in-vivo tests.

28

dB with bandwidth between 0.35 Hz and 1.5 kHz is for ECG recording; the gain of 57 dB

with bandwidth from 17 Hz to 1.5 kHz is for EMG measurement; the gain of 57 dB with

bandwidth from 0.25 Hz to 190 Hz is for EOG signal; the gain of 63 dB with bandwidth

from 0.37 Hz to 850 Hz is for EEG signal.

Table II summarizes the amplitude and frequency characteristics of the target

Figure 3-12. Photograph for ECG measurement.

TABLE II

SOC SETUP FOR DIFFERENT RECORDING APPLICATIONS

Gain switch set BW switch set Bio-signal

Gain of AFE BW of AFE

‘010’ ‘000’ ECG (0.5-150 Hz)

(0.1 – 3 mV) 49 dB 0.35 Hz-1.5 kHz

‘100’ ‘000’ EMG(14-350 Hz)

(0.01 – 2 mV) 57 dB 17 Hz-1.5 kHz

‘100’ ‘111’ EOG (0-30 Hz)

(0.1 - 1 mV) 57 dB 0.25 Hz-190 Hz

‘111’ ‘011’ EEG (0.5-40 Hz)

(10 µV - 0.1 mV) 62 dB 0.37 Hz-850 Hz

29

bio-potential signals, and the SoC parameter setup for a specific recording application. Tyco

Arbo H124sg pre-gelled adhesive electrodes (24 mm diameter) are employed to interface

the SoC with the human body. The electrode placement for each individual test follows the

electrode deployment instruction illustrated previously in Fig. 2-1.

ECG measurement

The ECG measurement is performed with two electrodes placed on a subject’s torso,

and a third electrode as a ground electrode placed in the middle of subject’s chest. The

measurement PCB is powered by battery with SoC output connected to an oscilloscope. An

ECG wave with a clear QRS complex is visible on the oscilloscope, as shown in Fig. 3-12,

where the oscilloscope is stopped for photographing. It should be noted that the captured

waveform is raw data, since the 50-Hz power line noise is effectively rejected by the fully

differential AFE, no extra digital signal processing is needed in this work.

EMG measurement

For EMG measurement, the electrode pair is placed on the subject’s biceps of right arm.

EMG signal is detected when the subject contracts and relaxes his biceps. Figure 3-13

presents the measured 4-second EMG signal and its Fast Fourier Transform (FFT) of a

selected signal segment. The spectrum shows that most of the EMG energy locates in the

frequency range from DC to 300 Hz; and the dominant energy is between 10 Hz to 150 Hz,

which is in line with [102].

0 200 400 6000

2

4

6

8

10

12

Am

p (

mV

)

Frequency (Hz)

EMG FFT

1 2 3 4

0.6

0.8

1.0

Am

p (

V)

Time (s)

Figure 3-13. Measured EMG signal and its FFT.

30

EOG measurement

For the EOG signal detection, the sensing electrode is placed outside an outer canthus,

which provides a straightforward detection of eye horizontal movements. Figure 3-14

shows a captured EOG signal, where the subject is asked to read a book line by line.

Sequences of small saccades (denoted as ‘r’ in Fig. 3-14) occur when the eyes move to the

right over the words. A large saccade (denoted as ‘L’ in Fig. 3-14) is observed when the

eyes move back to the left to the beginning of the next line.

EEG measurement

Figure 3-14. Photograph for EOG measurement.

EEG Test

Recording Electrodes

Ground

Electrode

Figure 3-15. Photograph for EEG measurement.

31

EEG signal is difficult to detect, since its amplitude is quite small, sometimes even

smaller than the background noise. Moreover, EEG signal is difficult to interpret, since it

represents the comprehensive electrical activity of thousands or millions of neurons

transmitted via brain tissues to the scalp [43]. However, certain features can be interpreted.

Analysis of the EEG signals in frequency spectrum can reveal the signal power in different

frequencies, which are produced during various status of the brain, such as in sleep status or

wake up status [42]. There is even notable difference shown on the EEG signals in

frequency domain when the subject keep his/her eyes open or closed.

A photograph of EEG measurement scenario is presented in Fig. 3-15 using the SoC

measurement PCB. An eye blink experiment has been performed, where the subject is

asked to blink his eyes for several times before keeping his eyes closed for around 20

seconds. Fig. 3-16 demonstrates the recorded EEG waveform along with selected segments

for eyes-open and eyes-closed experiments in time-domain and frequency-domain. Alpha

wave (in frequency range of 8~13 Hz) is usually used as an indicator to verify if EEG signal

is successfully recorded using a specific recording device. The Alpha wave shows up when

the subject’s body and mind are at rest with eyes closed [103].

As shown in Fig. 3-16, the large spikes in the time-domain EEG waveform (upper part

Figure 3-15. Photograph for EEG measurement.

0 5 10 15 20 25 30 35 40

0.4

0.6

0.8

1.0

EE

G s

ignal (V

)

Time (s)

eyes open eyes closedeyes blinks

35 36 37 380.60

0.65

0.70

0.75

15 16 17 180.60

0.65

0.70

0.75

EE

G s

ign

al (V

)

Alpha Wave (Eyes closed)

No Alpha Wave (Eyes open)

EE

G s

ignal (V

)

Time (s) 10 20 30 40 50 600.000

0.001

0.002

0.003

0.004

0.005

FF

T A

mp (

V)

Frequency (Hz)

Eyes open

Eyes closed

Dominant Rhythm 10 Hz

Figure. 3-16. Measured EEG signal using the implemented SoC from the frontal electrode with eyes

blinks, and signal segments for eyes-open and eyes-closed cases in time-domain, frequency-domain.

32

of Fig. 3-16) correspond to the subject’s eye blinks [103]. Two EEG signal sections are

selected and enlarged (lower left part of Fig. 3-16). They correspond to the recording of

three-second time slot for “Eyes closed” and “Eyes open” cases. From the time-domain

waveform, it can be clearly observed that the Alpha wave appears when the subject’s eyes

are closed and disappears when the eyes are open. The FFT results (lower right part of Fig.

3-16) of the “Eyes closed” and “Eyes open” segments further demonstrate and prove the

experimental findings regarding the Alfa wave. When the subject’s eyes are closed, a

noticeable increase in power of Alpha band with frequency around 10 Hz is observed.

While for the eyes open case, the subject is stimulated by the environment, as a result, the

Alpha wave is abolished. Since the experiments are taken in a typical electrical engineering

laboratory, a slight sign of 50 Hz power-line interference is observed in the FFT result of

the captured EEG signal.

3.3 Conclusion

A fully integrated low-power Bio-electric SoC is presented. The SoC sensor is featured

with programmable gain and bandwidth to accommodate a variety of bio-signals.

Comparison between the simulation results and the measurement results is made. It can be

observed that the measured gain-bandwidth performance is in line with the simulation result.

The SoC is fabricated in a 0.18-µm standard CMOS technology. The implemented SoC

occupies a silicon area of 1.5 mm × 3 mm and totally consumes 20 µW from a 1.2 V supply.

Totally 8 different gain, ranging from 45 dB to 63 dB can be achieved. On chip

implemented serial communication protocol enables the establishment of a body sensor

network over a group SoC sensors. SoC’s performance is verified through in-vivo tests.

ECG, EMG, EOG, and EEG signals are successfully recorded using the implemented SoC

sensor. Brief analyses of the measured biosignals are also made which further prove the

performance of the implemented SoC. The measurement results show that the proposed

SoC can serve as a versatile bioelectric sensor and can be applied in many healthcare and

wellness related applications.

33

Chapter 4

System Integration and Bio-Patch

Implementation

The in-vivo tests presented in the previous chapter using the implemented SoC are

conducted by applying the following commercially available electrodes, Tyco Arbo H124sg

pre-gelled adhesive electrodes, on the surface of skin, and connecting the electrodes to the

sensing SoC using lead wires. Although the commercial electrodes provide a relatively

stable mechanical and electric connection, the size for electrode size and snap button is

fixed. Since the implemented SoC has tunable gain and bandwidth, it allows the interface

with customized electrodes with a much smaller size. Patterned electrodes and customized

interconnections are needed for sensor miniaturization. In this chapter, characteristics of

printed electrodes are investigated. Electrode optimization is made to find a suitable

electrode pair with proper size and distance compatible with the fabricated SoC. Moreover,

since the sensor needs to be applied on human body, user comfort is an important issue. For

printing the electrodes, a flexible substrate is employed as circuit carrier, which can be

easily bent to fit the curve of human body. High resolution inkjet printing technology is

utilized for printing the electrodes, the interconnections as well as other patterns directly

onto flexible substrates. In this way two prototypes of Bio-Patch have been successfully

implemented on photo paper substrate and polyimide substrate respectively.

4.1 Printed Electrode

Inkjet printing technology is employed in this design to print conductive electrodes and

circuit board. Compared with conventional PCB manufacturing, the inkjet printing is more

cost efficient and environmental friendly. The advantages of utilizing this technology

include the following:

34

1) Less processing steps. Inkjet printing tech supports roll to roll massive production.

The silver electrodes and all the interconnections can be made at the same time on the

same substrate. Less process steps means time and energy efficient.

2) Easy assembly. Users or healthcare providers do not need to snap the commercial

electrodes on/off each patch, thus save maintenance/operation time.

3) From environment point of view, compared with conventional PCB manufacturing,

inkjet printing tech is more environmental friendly, since it does not involve masks or

etching steps, thus avoids the use of harsh chemicals. Also, as a print-on-demand and

digital printing method, material waste is minimized considerably.

4) Electronic devices miniaturization and steps towards new packaging technology.

Inkjet-printing of nano-metal particles can be used to connect chips to package (e.g.

System-in-Package) or directly print circuits on boards (e.g. Chip-on-Board). More

compact patch/sensor can be achieved due to the thin profile of printed

electrodes/interconnections. The smaller/more unobtrusive the patch/sensor is the more

convenient and comfortable for the user to use.

5) A step towards full integration of hybrid biomedical systems. By utilizing the digital

inkjet printing tech, the hybrid system can take advantages of both the fully integrated

SoC and the emerging Printed Electronics.

In this research work, the digital inkjet printing technology is used to fabricate the

printed electrodes. It is a contact-free manufacturing technology, and does not involve any

complicated process steps, so passive elements can be easily fabricated in a short time at

relatively low cost [104-106]. Nano-particle sliver ink is directly printed on the flexible

substrate to form electrodes and circuit interconnections [107]. The printed structure is

sintered by heat in an oven, so that the nano-particles join together and form a continuous

structure that enables formation of metal clusters/plate. In addition, sintering step can

effectively remove the solvents and other additives present in the ink by thermal

decomposition and evaporation [108]. HP Advanced Photo Paper is selected as the substrate,

since it is low cost, environmental friendly and disposable after use. Moreover due to its

physical features, such as it is flexible, with smooth surface, good water resistivity, it

attaches well to the human body with respect to the skin convexity.

35

Paper based printed electrode can be considered as a potential alternative to the

commercial electrode, since it is featured with low manufacture cost and disposability after

use. In order to investigate the compatibility of the printed electrodes with the implemented

Bio-electric SoC, several pairs of electrodes are printed on the photo paper substrate. Figure

4-1 shows the fabricated samples of inkjet-printed silver electrodes and connection traces.

The diameter of the electrode is 15 mm, and the electrode distance varies from 6 cm to 14

cm. The performance of the printed electrode pairs is investigated by connecting them to

the SoC’s measurement PCB and examine the output signal quality on the oscilloscope.

The electrode pair with electrode distance of 12 cm is selected for demonstration, as

shown in Fig. 4-2. The in-vivo tests presented below are based on this electrode pair. Figure

4-3 shows the cross section view of the printed electrode. The thickness of the paper

substrate is approximately 250 µm. The thickness of silver layer is approximately in the

range of 2 ~ 5 µm. It is hard to clearly distinguish the interface between silver and paper

layers, due to the absorption of the ink to the paper and evaporation during sintering.

Printed Electrode Pair Pair A

Pair B

Pair C

Pair D

Pair E

Figure 4-1. Printed electrodes using silver nano-particle ink on a photo paper substrate.

15

mm

12 cm

Printed Electrode

Printed Sliver Trace

Figure 4-2. Printed electrode pair with electrode distance of 12 cm on a photo paper substrate.

36

The performance of the printed electrodes is firstly evaluated by connecting them to the

measurement PCB through the differential inputs of the SoC’s instrumentation amplifier.

Figure 4-4 shows the photograph of ECG in-vivo test scenario using the printed electrodes

and the standard measurement PCB. In this figure, the oscilloscope is stopped with captured

ECG signal on the screen; the printed electrodes are intentionally held outward of the chest

(instead of towards the chest) for demonstration purpose. During monitoring, the electrodes

are placed towards the chest, and an elastic bandage is used to apply mild pressure on the

electrodes, so they are well attached to the skin. A clean ECG waveform is obtained from

the output of SoC’s analog front-end and it can be easily observed on the oscilloscope

Figure 4-4. ECG test with paper based printed electrodes.

Silver trace

2~5 um

Photo Paper

250 um

Figure 4-3. Cross section view of the printed electrode.

37

shown in Fig. 4-4. For the EMG measurement, another electrode pair is placed on the

subject’s forearm. The bandage is applied to fasten the electrode to the arm to minimize the

motion artifact. EMG signal is detected when the subject contract and relax her hand, as

shown in Fig. 4-5. More investigation and measurement results regarding how the electrode

distance affects the amplitude of the detected signal are reported in attached Paper IV [30].

It can be observed that all the characteristics of the measured ECG and EMG waves are

clearly visible, without any digital signal processing. The measurement results prove that

the printed electrodes are adequate to detect the weak bio-potential signals and can operate

smoothly with the SoC sensor.

4.2 Paper based Bio-Patch

After the performance of the printed electrodes has been evaluated, we move forward to

a full integration solution by integrating the SoC sensor and the printed electrodes on the

same substrate.

Figure 4-5. EMG test with paper based printed electrodes.

38

Silver ink is directly printed on a photo paper substrate to make interconnections, thus a

paper-based circuit board is formed. Figure 4-6 shows the printed circuit board and

patterned electrodes on a photo paper substrate. A prototype of the proposed Bio-Patch is

implemented on a photo paper substrate, as shown in Fig. 4-7, with a size of 6 cm × 8 cm. It

is composed of three parts: a pair of printed electrodes, a soft battery, and the bio-electric

SoC. A flat Enfucell battery is adopted in the Bio-Patch implementation. The soft battery is

flexible and suitable for low power applications, such as micro-sensors, printing and

disposable applications. It provides a voltage source of 3.0 V with a battery capacity of 10

mAh. By attaching it on a subject’s chest, the proposed Bio-Patch can work as one channel

ECG sensor. The collected digital ECG signal can be transmitted to the outside world via a

Figure 4-7. Bio-patch implemented on a photo paper substrate with printed electrodes, interconnections,

bio-electric SoC and a soft battery.

Figure 4-6. Inkjet printed circuit board and patterned electrodes on a photo paper substrate.

39

wireless module. Bluetooth or custom designed wireless transmitter can be employed for

wireless transmission. A novel all-digital polar transmitter is investigated which is

implemented and tested on another chip. The measurement results are reported in [33]. The

Bio-Patch can work alone or operate along with other patches to establish a wired network

for synchronous multiple-channel bio-signals recording. The measurement results show that

ECG and EMG are successfully measured in in-vivo tests. More results are reported in

attached Paper I [31].

4.3 Static Bending Tests

A common concern encountered by almost all the inkjet printed circuit built on flexible

substrate is the crack probably generated when the substrate is under extreme bending. The

crack leads to electric path discontinuity (open circuit). For the photo paper based

Bio-Patch, during the in-vivo tests, the electrical performance is consistent even with a large

bending (but not folded) to fit the body convexity. However, sharp edge-folding on paper

substrates creates deformation and crack on the substrate. The metal trace becomes split

apart as well. Figure 4-8 presents the photographs of cracks in the silver trace, generated

when the substrate is folded.

Figure 4-8. Crack generated in the silver trace on photo paper substrate by large folding.

40

To assess the reliability of the printed sliver traces on the flexible substrate, static

bending test is performed. 2 types of flexible substrate, photo paper substrate and polyimide

(PI) substrate respectively, are selected for test. 3 identical samples are printed on photo

paper and PI with a length of 4.5 cm and width of 2 mm, as shown in Fig. 4-9. During tests, a

multimeter is used to measure the DC resistance of the printed silver trace. We gradually

decrease the bending radius of the printed samples to examine if metal crack or peel-off could

occur under any condition.

The measurement results of the bending tests performed on photo paper substrate are

summarized in Fig. 4-10, where the DC resistance varies as a function of bending radius. It

10 9 8 7 6 5 4 3 2 1 06

7

8

9

10

Re

sis

tan

ce (

oh

m)

Bending Radius (cm)

Sample1

Sample2

Sample3

Figure 4-10. Resistance variation versus bending radius on photo paper substrate.

(a) (b)

Figure 4-9. Static bending tests. (a) Test samples printed on photo paper substrate. (b) Test samples

printed on PI substrate.

41

can be observed that the resistance of the 3 printed samples remains stable (resistance

variation less than 1%) when the bending radius is decreased from 10 cm to 1.3 cm. However,

all 3 samples crack (the resulting resistance exceeds the maximum range of the multimeter)

when the photo paper substrates are bended to 1 cm radius.

For the PI based silver traces, throughout the entire experiment, no metal crack or

peel-off occurs, and the samples keep an almost consistent resistance even when the radius is

decreased to 4 mm (which means the sample is spiral-wound of 4 turns, a quite large bending

considering with the sample size). Finally, we make an extreme bending test by folding the

sample and pressing hard on it to form a sharp edge, as shown in Fig. 4-11. From the figure, it

can be observed that the connection of the silver trace on PI substrate keeps intact with the

resistance of around 30 Ω.

The measurement results indicate that the printed silver traces on PI substrate exhibit

remarkable advantages over the ones based on photo paper substrate, in terms of reliability

and flexibility. It can be reliably employed in the fabrication of conductive electrodes,

Active Cable and printed flexible circuit board. We move forward with the wearable

healthcare monitoring system integration using PI substrate. In addition, a novel packaging

method is applied on PI substrate to achieve more compact patch/sensor size, which will be

presented in the following parts of this chapter.

Figure 4-11. Static bending test for PI based silver trace, no crack occurs under extreme folding.

42

4.4 Polyimide (PI) based Bio-Patch

Photo paper based printing electronics shows its advantages in terms of low cost,

environmental friendly and disposable after use. However, due to its physical nature, the

paper substrate breaks when folded. As a result, cracks are generated in the printed metal

trace producing an open circuit. Bending test results presented in the previous section show

that PI based printed silver traces have better reliability. PI substrate also exhibits good

flexibility and suitable for wearable devices.

In addition, another drawback for the implemented photo paper based Bio-Patch is that

the size is relatively large for an on-body sensor (6 cm × 8 cm shown in Fig. 4-7.). From

IC package point of view, since currently many pins are for test and debug purpose,

package JLCC 84 (3 cm × 3 cm) is chosen. In many advanced medical applications, such as

wearable healthcare devices, a desirable bio-sensor should be unobtrusive, light with tiny

size [109]. We realize the current SoC package size is the bottleneck for the further

miniature of the Bio-Patch. This problem can be solved by employing a smaller IC package

(such as QFN or SOIC package). Alternatively, we try to deal with it in another approach

instead: using the inkjet printing technology presented in [110] to produce electrical

interconnections on an interposer and directly attach the flip SoC chip on the interposer,

thus avoid the use of a rigid ‘package’. With this method, it is possible to implement the

patch with a much smaller size at a lower cost, thus make the sensor node suitable for

on-body deployment. In the following parts of this section, we present a Bio-Patch

prototype implemented on PI substrate.

The PI based Bio-Patch prototype is formed by assembling the inkjet printed interposer

lead-frame, conductive electrodes, and printed circuit board on the PI substrate. Anisotropic

conductive adhesive (ACA) is applied in this prototype. It is fabricated by dispersing

conductive particles with tiny diameter in the adhesive. It has been widely used in current

flip chip packaging applications [111, 112]. ACA provides both electrical and mechanical

connections between chip pads and the substrate. The distinct advantages of using ACAs

include reduced package size and thickness, improved environmental compatibility, and

lower assembly temperature [113]. In addition, the process costs are lower compared with

traditional bonding/soldering approaches, due to fewer processing steps [114]. Furthermore,

ACA can be used in fine pitch applications as small as 40-um pad width and 80-um pad

pitch [115]. Static and cyclic bending tests performed in [115] also indicate that the ACA

joints are featured with sufficient reliability for consumer electronics.

43

In this design, ACA is employed to bond the Bio-electric SoC chip onto the PI

interposer to form a sensor node. A cross-section view of the overall flip chip assembly

process using ACA is shown in Fig. 4-12. In the initial stage, the adhesive is not conductive

in any direction, as conductive particles do not touch each other due to their tiny particle

size and low density dispensed in adhesive, as shown in Fig. 4-12a. When pressure and heat

are applied, the conductive particles between the chip pad and substrate are deformed or

breached, so the electrical contact can be made in vertical direction (perpendicular to the

plane of the die), while in the horizontal direction it still keeps non-conducting, as shown in

Fig. 4-12b. During the assembly process of this work, a thin layer of ACA is applied over

the printed leads on the interposer. The flip chip is then aligned and attached onto the

interposer with a Fine Placer, which also provides proper pressure and heat required for this

packaging process.

The inkjet printed interposer is shown in Fig. 4-13a, The interposer with the attached

SoC using ACA is shown in Fig. 4-13b. In order to ensure the electrical and mechanical

contacts between the die and substrate, a protecting mold (medical compatible epoxy from

EPOXY-Tech) is applied over the flip chip area, as shown in Fig. 4-13c. The assembled

interposer has a total dimension of 18 × 18 × 0.03 mm3. The physical size of the interposer

(a) (b)

Figure 4-12. Assembly process with ACA. (a) Silicon chip alignment. (b) Thermo- compression bonding.

(a) (b) (c) (d)

Figure 4-13. SoC bonding and packaging. (a)Interposer on PI substrate. (b) Flip chip bonding with ACA.

(c) Protecting mold. (d) Sensor node assembly.

44

is much smaller and thinner than a standard JLCC-86 package (30 × 30 × 3 mm3

, which

was used as the chip container in previous prototype). The integration of the tiny-size SoC

on the printed interposer makes the sensor node miniaturization possible. Finally, the

interposer is flipped and attached to a larger PI mid-layer using ACA to form a sensor node,

where several passive components are applied to configure the chip to a proper working

condition, as shown in Fig. 4-13d. Figure 4-14 shows the final assembled Bio-Patch

prototype with the size of 16 cm × 16 cm which is accomplished by mounting the sensor

node using conductive paste on the printed circuit board. Active Cable with redundant

structure is printed on the PI substrate to connect each sensor node. The electrodes are also

fabricated via the inkjet printing technology and attached beneath the Bio-Patch. The

electric connection between the electrode and a sensor node (one input of the AFE) is made

via through-hole in the substrate.

The use of PI substrate and the integration of the bare die on the PI enable an ultra-thin

patch solution with a total thickness less than 1.5 mm. 8 sensor nodes are deployed and

mounted on specific points of a printed flexible circuit board layer (corresponding to 8

positions on the subject’s chest). A flat soft battery from Enfucell (0.7 mm thickness, 1.4 g

weight) is employed as the power supply. To protect the circuit, a thin plastic foil is applied

to cover the patch. The Bio-Patch can be easily attached to a subject’s chest by an elastic

bandage or utilizing medical compatible adhesive at the bottom layer to ensure a good

contact. Concurrent multi-channel ECG signal measurements are performed using the

implemented Bio-Patch prototype. More results are reported in the attached Paper II [32].

Figure 4-14. Prototype of PI based Bio-Patch.

45

4.5 Conclusion

In this chapter, we present the implementation work of applying the Bio-electric SoC

into a wearable healthcare device. Customized electrodes are fabricated using inkjet

printing technology, and their performances are investigated by connecting them to the SoC

and examined through in-vivo tests. The reliability of inkjet printed silver traces on flexible

substrate is evaluated through the static bending test. The purpose of this work is not

limited to compare the printed electrodes with the commercial ones. The main purpose is to

present the concept of a hybrid bio-sensing system and to demonstrate a workable prototype

using digital inkjet printing technology to facilitate the integration of the customized SoC

on a flexible substrate. In addition, we intended to evaluate the possibility and feasibility of

integrating the silicon SoC with Printed Electronics. Two prototypes of Bio-Patch are

implemented, the first one is based on photo paper substrate and the second one is built on

PI substrate. The patches get rid of cumbersome connecting wires and avoid complicated

electrodes configuration/placement, increasing the comfort of the user. Preliminary

measurement results show that ECG signals are successfully measured when applied on

subject’s chest. The photo paper base Bio-Patch has advantages in terms of low cost and

disposability after use, while the PI based Bio-Patch is featured with better reliability and

concurrent measurement of multi-channel ECG signals.

.

46

47

Chapter 5

Summary and Future Works

5.1 Summary

This thesis presents and investigates the design and implementation of hybrid sensor

devices for enabling wearable pervasive healthcare systems. An Intelligent Electrode has

been proposed by integrating a compact SoC into a conductive electrode. A serial

connection method has been proposed to interconnect multiple Intelligent Electrodes or

sensor nodes in a serial manner to establish a body sensor network. This method can

significantly improve user’s comfort by drastically reducing the number of connecting

cables. A low-power, low-noise Bio-electric SoC has been designed and fabricated to meet

the requirements for weak bio-electrical signal acquisition, as well as the requirements for

serial communication and network management. This thesis has focused not only on

customized circuit design and implementation on silicon chip, but also on sensor

miniaturization and system integration through printing technology on flexible substrates,

as well as the corresponding function verification and performance evaluation through

in-vivo tests:

A fully integrated SoC has been implemented. The SoC has programmable gain and

bandwidth to accommodate different bio-electrical signals. Experiment results from

in-vivo tests have proven that the fabricated SoC can be used to measure ECG, EMG,

EOG, and EEG signal. Also, the tunable gain and bandwidth enable the SoC to

interface with customized electrodes with various size and distance to form a novel

sensor or sensing system. On-chip memory allows the buffering of the digital

bio-signal. The implemented serial communication protocol enables the output of

each sensor node as serial data stream, and allows the establishment of a body sensor

network.

48

The performance of printed electrodes and interconnections on flexible substrates

have been investigated and the results confirm the inkjet printing technology can be

successfully employed to print circuit patterns and sensors. By connecting the

printed electrodes to the SoC measurement board, high quality ECG signal and EMG

signal have been successfully measured. Experimental results show that the printed

electrodes are fully compatible with the implemented SoC. It is feasible to print

customized electrodes and other patterned electronics on flexible substrates such as

photo paper or PI substrate. Static bending tests have been applied to verify the

reliability of the printed patterns on flexible substrates. Sliver traces printed on photo

paper substrate and PI substrate show good electrical performance. The photo paper

substrate is featured with low cost and disposability after use. PI based silver lines

exhibit remarkable reliability, which can survive from extreme bending test.

Sensor miniaturization and system integration using inkjet printing technology on

flexible substrates have been investigated. The design concept of combining

compact SoC and printed electronics to form a hybrid-integrated system with the

potential to enable wearable healthcare applications has been demonstrated. Two

functioning Bio-Patch prototypes have been implemented: one is photo paper based,

and the other one is on the PI substrate. Preliminary experiment results have shown

that ECG signals are successfully measured through in-vivo tests.

5.2 Future Works

Advanced biomedical technology is indispensible from personal and global healthcare

and wellbeing since it contributes to improve the life quality, and saving lives in many

occasions. As long as there are people suffering from diseases, progresses on biomedical

technologies will never stop. Some remarks are added below, which are the key features or

function blocks could be added or improved in the future to make it a more advanced

system.

High resolution, low power ADC: For sophisticated medical device, such as EEG

recorder, high resolution ADC is required, for example 12-bit or 14-bit ADC,

which can identify quite small changes on the detected signal. Meanwhile, low

power is also required to effectively extend the battery life for a portable or

wearable medical system.

Integration of wireless link: A full integration solution needs a wireless

transmission module to transmit the measured bio-signals to a smart phone or base

49

49

station. The integration of wireless transceiver can further reduce the sensor size

and make the whole system more compact as well.

More sensor interfaces: Future healthcare monitoring systems require

multi-sensory bio-signals for assessing the patient health condition. More sensor

interfaces are needed to accommodate more external sensors, and therefore achieve

the target ‘one-chip multi-sensor’.

Explore the developed measuring and system integration technologies in

non-clinical area, such as sport and fitness. A frequency-tunable sine wave

generator can be designed and implemented on chip to measure electrical

bio-impedance for body composition assessment, which is a useful parameter to

track in fitness and wellness applications.

Display and diagnosis on smart phone: One attractive application is to use a smart

phone to receive and display the measured bio-signals and even make a diagnosis

in certain cases. This way the user can directly check his/her health condition via

the smart phone, which makes the user actively engaged in their own care process,

i.e. enabling ‘self-care’.

Explore other technologies which could be integrated to form a more comfortable

and more efficient wearable health care system, like the use of Smart Textiles and

textile-electronic integration technology to form smart garments.

50

51

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