multiple sensor platforms for hydrogen and human...
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MULTIPLE SENSOR PLATFORMS FOR HYDROGEN AND HUMAN PHYSIOLOGICAL MOVEMENT SENSING
By
XIAOGANG YU
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2011
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© 2011 Xiaogang Yu
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To my parents
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ACKNOWLEDGMENTS
I would like to express my sincere gratitude to my advisor Dr. Jenshan Lin for his
advice, encouragement, and mentoring throughout my PhD study. I have truly enjoyed
doing research under his guidance over the years. Moreover, Dr. Lin’s patience and
kindness to other people are things I admire greatly. I would also like to thank my
committee members, Dr. Fan Ren, Dr. Huikai Xie, and Dr. Eric McLamore for their time
and precious comments.
I am also thankful to my colleagues (Changzhi Li, Yan Yan, Mingqi Chen, Zivin
Park, Raul Chinga) in the Radio Frequency Circuits and Systems Research Group, for
all the help and happiness they offered.
I would like to thank my parents for their encouragement and unconditional
support. I dedicate this dissertation to my family, whose love gives me the courage to
my life.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ........................................................................................... 11
ABSTRACT ................................................................................................................... 12
CHAPTER
1 INTRODUCTION .................................................................................................... 14
1.1 Background ....................................................................................................... 14
1.2 Recent Progresses on Hydrogen Sensing ........................................................ 15
1.3 Recent Progresses on Physiological Movement Sensing ................................. 17
1.3.1 Theoretical Breakthroughs ...................................................................... 17
1.3.2 RF Front-end Architectures ..................................................................... 17
1.3.3 Advances in Signal Processing Techniques ............................................ 21
1.3.4 Miniaturization and System-on-chip ......................................................... 23
2 MULTIPLE WIRELESS SENSOR PLATFORM USING ALGAN/GaN HIGH ELECTRON MOBILITY TRANSISTOR DIFFERENTIAL DIODE SENSORS ......... 24
2.1 Hydrogen Sensors with Different Fabrication Technologies ............................. 24
2.2 Experiments with Differential Sensor Pairs ....................................................... 25
2.3 Wireless Multiple Sensor System...................................................................... 27
2.3.1 System Overview..................................................................................... 27
2.3.2 Detection Circuits .................................................................................... 29
2.3.3 Zigbee Wireless Network ......................................................................... 30
2.3.4 Wireless Sensor Network Monitoring Software ....................................... 31
2.3.5 Monitoring States, Transitions, and Actions ............................................ 32
2.3.6 Packages ................................................................................................. 32
2.4 Field Test .......................................................................................................... 36
2.5 Summary .......................................................................................................... 37
3 MULTIPLE DOPPLER RADAR SENSOR PLATFORM FOR TWO-DIMENSIONAL HIGH-SENSITIVITY HUMAN PHYSIOLOGICAL MOVEMENT DETECTION ........................................................................................................... 39
3.1 Challenges of Body Movements ....................................................................... 39
3.2 Principle of Noncontact Vital Sign Detection ..................................................... 40
3.3 Two-Dimensional Random Body Movement Cancellation ................................ 44
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3.4 Sensitivity Improvement Using Doppler Radar Array ........................................ 47
3.5 DC Offset Compensation .................................................................................. 50
3.6 Experiments ...................................................................................................... 52
3.7 Limitation of Sensitivity Improvement ................................................................ 54
3.8 Limitation of Real-time Large Body Movement Cancellation ............................. 57
3.9 Summary .......................................................................................................... 57
4 SYSTEM LEVEL INTEGRATION OF HANDHELD WIRELESS NONCONTACT VITAL SIGN SENSOR RADAR .............................................................................. 59
4.1 Challenges of Portable Applications ................................................................. 59
4.2 Vital Sign Detection System Architecture .......................................................... 60
4.3 Baseband Signal Processor Design.................................................................. 64
4.4 Receiver Chain Noise Analysis ......................................................................... 67
4.4.1 LNA and Gain Block ................................................................................ 67
4.4.2 Mixer with LO Input .................................................................................. 67
4.4.3 Baseband Amplifier ................................................................................. 69
4.4.4 Complete Noise Performance Evaluation Model ..................................... 69
4.5 Experiments ...................................................................................................... 71
4.5.1 Two-tone Actuator Movement ................................................................. 72
4.5.2 Human Respiration and Heart Beat Measurement .................................. 73
4.5.3 Guideline for Selecting the Sampling Frequency ..................................... 75
4.5.4 The Effect of Output SNR on Detection Accuracy ................................... 75
4.5.5 The Trade-off between Output SNR and Detection Accuracy ................. 77
4.6 Summary .......................................................................................................... 79
5 INTEGRATED VITAL SIGN RADAR SENSOR WITH ON-BOARD ANTENNA ...... 80
5.1 Integration of Vital Sign Radar and Antennas ................................................... 80
5.2 Transmitting and Receiving Antenna Arrays Design ......................................... 80
5.3 Orientation of the TX and RX Antennas ............................................................ 83
5.4 Simulation of the Coupling between TX and RX Antennas ............................... 84
5.5 System Integration of the Vital Sign Detector with On-board Antenna .............. 86
5.6 Low-power Design, Link-budget, and Emission Safety ..................................... 88
5.7 Summary .......................................................................................................... 91
6 CONCLUSIONS ..................................................................................................... 92
LIST OF REFERENCES ............................................................................................... 93
BIOGRAPHICAL SKETCH .......................................................................................... 101
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LIST OF TABLES
Table page 2-1 Wireless hydrogen sensor board bill of material ................................................. 33
4-1 RF transceiver board bill of material ................................................................... 63
4-2 Receiver chain components noise specification ................................................. 67
5-1 Dimensions of the patch antenna array. ............................................................. 83
5-2 Received RF power estimate for 5.8 GHz integrated vital sign sensor. .............. 89
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LIST OF FIGURES
Figure page 1-1 Topology of the hydrogen sensor network reported in Sensors [24]. .................. 16
1-2 Quadrature homodyne vital sign radar architecture. ........................................... 18
1-3 Double-sideband heterodyne vital sign radar architecture. ................................. 19
1-4 Direct IF sampling heterodyne vital sign radar architecture. ............................... 20
1-5 Self-injection locking vital sign radar architecture [78]. ....................................... 21
2-1 Microscopic images of differential sensing diodes.. ............................................ 26
2-2 Absolute and differential current of HEMT diodes.. ............................................ 27
2-3 Star network layout. ............................................................................................ 28
2-4 Block diagram of wireless multiple hydrogen sensor system. ............................. 29
2-5 Sequence of transceiver module operation. ....................................................... 31
2-6 Images of wireless sensor network monitoring software .................................... 34
2-7 An image of the hydrogen sensing website showing the real-time responses of the hydrogen sensors. .................................................................................... 35
2-8 State flow diagram of the hydrogen sensor network software monitoring mechanism. ........................................................................................................ 35
2-9 Individual hydrogen sensor package.. ................................................................ 36
2-10 A photograph of base station including wireless receiver and computer. ........... 36
3-1 Block diagram and setup of the vital sign detection system. .............................. 41
3-2 Baseband I/Q signals: time domain signal and frequency domain spectrum.. .... 43
3-3 Block diagram of the vital sign detection system with Doppler radar array. ........ 45
3-4 Simulation of 2-D random body movement cancellation.. ................................... 46
3-5 Amplitude of Bessel functions............................................................................. 48
3-6 Respiration and heartbeat sensitivity improves as the number of detectors increases. ........................................................................................................... 49
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3-7 Heartbeat spectra when DC offset is present in various detector settings .......... 51
3-8 Illustration of DC offset compensation algorithm. ............................................... 51
3-9 Photograph of the RF radar array and TX/RX antennas. .................................... 52
3-10 Two dimensional random body movement cancellation using multiple detectors array. ................................................................................................... 53
3-11 Amplitude of Bessel functions. ............................................................................ 55
3-12 Respiration and heartbeat sensitivity peaks at 12 sensors and 8 sensors, respectively......................................................................................................... 56
4-1 Block diagram of the vital sign detection system. ............................................... 61
4-2 Photograph of the RF transceiver board and signal processor board ................. 62
4-3 Block Diagram of the RF transceiver board ........................................................ 63
4-4 Flow diagram of the spectrum analysis algorithm ............................................... 65
4-5 Photo of the digital signal processor board ......................................................... 66
4-6 Noise figure of active mixer and passive mixer in 0.13 um CMOS. .................... 68
4-7 Two-tone actuator movement experiment setup ................................................. 72
4-8 Theoretical results vs. experimental results of the two-tone actuator experiment .......................................................................................................... 73
4-9 Human respiration and heart beat measurement setup. ..................................... 74
4-10 Detected baseband signal and spectra in non-contact vital sign detection. ........ 74
4-11 Simulated baseband signal and spectrum in non-contact vital sign detection. ... 76
4-12 Detected baseband signal and spectrum in non-contact vital sign detection. ..... 78
4-13 Simulated receiver output SNR. ......................................................................... 78
5-1 Patch antenna array model used in on-board antenna design. .......................... 81
5-2 Patch antenna array radiation pattern. Maximum gain 11.5 dB is achieved. ...... 82
5-3 S11 of patch antenna array. The antenna resonates at 5.8 GHz. ....................... 82
5-4 H-plane patch antenna array orientation. ........................................................... 84
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5-5 Mutual coupling simulation model in Ansoft HFSS. ............................................ 85
5-6 S12 of the on-board TX and RX patch antenna array. ........................................ 86
5-7 Photograph of the integrated vital sign radar sensors with on-board antennas. ............................................................................................................ 87
5-8 Photograph of the real-time integrated vital sign radar software......................... 88
5-9 IEEE RF safety standard C95.1-2005. ............................................................... 90
5-10 Power density of the integrated noncontact vital sign detector. .......................... 91
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LIST OF ABBREVIATIONS
HEMT High electron mobility transistor;
VCO Voltage controlled oscillator;
LCD Liquid crystal display;
PIO Parallel Input/Output;
LED Light-emitting diode;
RAM Random-access memory;
CMOS Complementary metal–oxide–semiconductor;
RMS Root mean square;
f Frequency;
fc Carrier frequency;
VCO Voltage controlled oscillator;
CW Continuous wave;
TX Transmitter;
RX Receiver;
RF Radio frequency;
IF Intermediate frequency;
LO Local oscillator;
LNA Low noise amplifier;
BPF Band-pass filter;
FFT Fast Fourier transform;
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
MULTIPLE SENSOR PLATFORMS FOR HYDROGEN AND HUMAN PHYSIOLOGICAL
MOVEMENT SENSING
By
Xiaogang Yu
December 2011
Chair: Jenshan Lin Major: Electrical and Computer Engineering
This dissertation begins with a demonstration of the integration of multiple sensor
techniques with state-of-the-art hydrogen sensing devices. The proposed multiple
sensor system uses six Zigbee transceivers to collect hydrogen density information from
the dispersedly deployed AlGaN/GaN high electron mobility transistor (HEMTs)
differential sensing diodes. The collected hydrogen density information is transmitted
wirelessly to the base station for data logging and tracking of each individual sensor.
The software at the base station defines and implements the monitoring states,
transitions, and actions of the hydrogen sensing system. The software also is able to
warn the user of potential sensor failure, power outages, and network failures through
cell phone network and Internet. Real-time responses of the sensors are displayed
through a web site on the Internet. The sensing system has shown good stability for
more than 18 months in an outdoor field test.
After that, Chapter 3 is devoted to a presentation of the integration of multiple
sensor techniques with noncontact vital sign detection. Using the multiple vital sign
sensor platform, two-dimensional random body movement cancellation is achieved. The
multiple sensor system includes four detectors, an 8-channel data acquisition module,
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and a computer for spectrum analysis. Each of the detectors consists of a radio
frequency transceiver, a baseband analog circuit, and a power management circuit. The
multiple sensor platforms also strengthen the detecting sensitivity on the respiration and
heartbeat. A DC offset compensation algorithm is introduced to free the body movement
cancellation from disturbance of unwanted DC offset. Experiments were performed with
a human subject in laboratory environment. Results were analyzed to verify the
improved detection performance at the presence of 2-D human body movement. The
limitation of sensitivity improvement and body movement cancellation are demonstrated
with simulation results.
Chapter 4 details the hardware design of the individual portable Doppler radar for
noncontact vital sign detection. Topics including RF transceiver board design, baseband
signal processor design, sampling frequency selection guideline, and noise analysis for
the receiver chain of the detector will be discussed.
Finally, a system integration of noncontact vital sign detector with antennas on-
board will be presented in Chapter 5.
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CHAPTER 1 INTRODUCTION
1.1 Background
Multiple sensor technology is a key component in the science and applications of
sensing physical, chemical, and biological phenomena. The relative low cost of sensors,
the availability of high speed communication networks, and the increased computational
capability have enabled great research interests and advances in this area. In recent
years, new techniques for sensing two particular phenomena, hydrogen and human
physiological movements, have enjoyed great advances. The integration of multiple
sensor technology with these new techniques is the next logical step in the evolution of
hydrogen and physiological movement sensing.
In the area of hydrogen sensing, the sensors are required to detect hydrogen near
room temperature with minimal power consumption and weight and with a low rate of
false alarms. Due to their low intrinsic carrier concentrations, GaN- and SiC-based wide
band gap semiconductor sensors are developed to operate at lower current levels than
conventional Si-based devices and offer the capability of detection to ∼600◦C [1]–[23].
The ability of electronic devices fabricated in these materials to function in high
temperature, high power, and high flux/energy radiation conditions enable performance
enhancements in a wide variety of spacecraft, satellite, homeland defense, mining,
automobile, nuclear power, and radar applications.
In the area of human physiological sensing, the concept of noncontact vital sign
detection has been demonstrated in various publications before 2000 [26]-[30]. After
2000, more microwave sensing systems [31]-[81] with lower power, smaller package,
improved sensitivity, and longer detection range have been developed to detect the
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physiological movements, i.e. heart beat and respiration. The microwave sensing
system transmits a radio frequency, single-tone continuous-wave (CW) signal, which is
reflected off of a target and then demodulated in the receiver. CW radar with the human
body as the target will receive a signal with its phase modulated by the time-varying
chest-wall position. Demodulating this phase will then give a signal proportional to the
chest-wall position that contains information about movement due to heartbeat and
respiration. This technique enabled noncontact detection of vital signs of humans or
animals from a distance away, without any sensor attached to the body. The non-
intrusive nature and penetration capability through the building materials bring unique
property to home healthcare monitoring, search-and-rescue for earthquake or fire
victims, security, and military applications.
1.2 Recent Progresses on Hydrogen Sensing
In the field of hydrogen sensing, recent developments in the early 2000s have
shown the promising performance of AlGaN/GaN high electron mobility transistors
(HEMTs) for use in hydrogen sensing [4]-[23]. The high electron sheet carrier
concentration of nitride HEMTs provides an increased sensitivity relative to simple
Schottky diodes fabricated on GaN layers. This dissertation will present the work of a
multiple sensor platform using a differential pair of AlGaN/GaN HEMT diodes for
hydrogen sensing near room temperature [25]. This multiple sensor configuration
provides a built-in control mechanism to reduce false alarms due to temperature swings
or voltage transients. The design and optimization of the detection circuitry, digital signal
processing, wireless network, and monitoring states to maintain an accurate and
reliable system were investigated.
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Figure 1-1. Topology of the hydrogen sensor network reported in Sensors [24].
In terms of wireless network design for hydrogen sensors, a wireless sensor
network [24] was reported for in-situ monitoring of atmospheric hydrogen concentration
in 2003. In that network design, the system consists of multiple sensor nodes, equipped
with titania nanotube hydrogen sensors, distributed throughout the area of interest; each
node is both sensor, and data-relay station. Figure 1-1 shows the experimental setup of
the one-way peer-to-peer sensor network. Node 2 transmits the sensor information to
Node 3 since it is the only node within the transmission range of Node 2. Similarly,
Node 4 is the preceding node of Node 3 due to its proximity, and Node 1 is the
preceding node of Node 4. This peer-to-peer setup enables extended wide area
monitoring. However, the potential failure of any preceding sensor node will break the
afterward data-relay path and will result in the malfunction of the sensor network. This
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dissertation will present a hydrogen sensor network using star topology. In the star
topology, the sensor nodes are individually connected to the base station. The failure of
one sensor will not affect the functioning of the whole sensor network.
1.3 Recent Progresses on Physiological Movement Sensing
1.3.1 Theoretical Breakthroughs
In the field of noncontact vital sign detection, researchers working on noncontact
vital sign detection have spent great efforts to achieve accurate and robust performance
while solving many technical challenges, especially in the years from 2008 to 2010 [42]-
[80]. As one of the main challenges, the influence of clutter noise and phase noise has
been solved by the range-correlation effect by applying the same transmitted signal to
the receiver as the reference signal [33]. Another challenge, the null detection point
problem, was solved by frequency tuning in the double-sideband transmission system
[37] and complex-signal/arctangent demodulation in the quadrature direct-conversion
system [40][42]. In addition to the experimental efforts to improve the system
performance, theoretical analyses have been performed to study the Doppler non-
contact vital sign detection and provide guidelines for the designs. Achievements
include the analysis on the range correlation effect and I/Q performance benefits [33],
the modeling and analysis of the double-sideband transmission to eliminate the null
detection point [37], the spectral analysis of the non-linear phase modulation effect [42],
the analysis of the arctangent demodulation in quadrature receivers [40], and the
comparative study on different radio architectures for vital sign detection [39].
1.3.2 RF Front-end Architectures
There are various RF front-end architectures designed to achieve the theoretical
concepts outlined in the aforementioned publications. Five kinds of architectures have
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been reported: homodyne, heterodyne, double-sideband architectures, direct
intermediate frequency (IF) sampling, and self-injection locking.
Homodyne transceiver for vital sign detection is originally implemented using
single-channel direction conversion architecture. Although the detected signal of the
single-channel transceiver contains the vital sign information, it is very weak at certain
detection distances, i.e. null detection points. Quadrature direction conversion Doppler
radar is designed to eliminate the null detection point problem [33]. It is also found that
the quadrature baseband signals can be combined in software to perform complex
signal demodulation [42] or arctangent demodulation [40]. Figure 1-2 shows a block
diagram of the quadrature homodyne vital sign radar.
Figure 1-2. Quadrature homodyne vital sign radar architecture.
Before the debut of homodyne transceivers in 2001 [31][32], the heterodyne
transceiver had been the dominant design architecture for vital sign detection [29].
Since the heterodyne transceivers suffer the same null detection problem in single-
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channel homodyne transceivers, they have to be designed in quadrature architecture. In
2005, a double-sideband heterodyne architecture was proposed to eliminate the need of
generating quadrature LO signals [37]. Figure 1-3 shows a block diagram of the double-
sideband vital sign radar architecture. The heterodyne transceiver transmits both the
upper and lower sidebands in double-sideband configuration. The double-sideband
signal is reflected on the subject and received by the heterodyne receiver. By combining
the baseband signal of both the sidebands, the distance between optimal and null
detection points is changed to λIF/16. Since λIF is the wavelength at the IF stage, the
double-sideband configuration results in a much longer separation than the distance
(λRF/8) in conventional heterodyne transceiver.
Figure 1-3. Double-sideband heterodyne vital sign radar architecture.
In 2008, a direct IF sampling heterodyne transceiver for vital sign detection was
reported [50]. Figure 1-4 shows a simplified block diagram of the direct IF sampling
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transceiver. In this architecture, the output of the RF mixer is sampled and digitized by a
high speed ADC. The digital IF signal is demodulated by a digital quadrature
demodulator. The subsequent DSP is performed directly on the digital quadrature
signals. The direct IF sampling is free from the I/Q imbalance in an analog IF quadrature
demodulator and eliminates the DC offset calibration.
Figure 1-4. Direct IF sampling heterodyne vital sign radar architecture.
In 2010, a new self-injection locking approach was introduced to implement the
detection of vital signs [78]. A differential LC voltage controlled oscillator (VCO) with
injection port is used in the new architecture. The output of the VCO is amplified by a
power amplifier (PA) and transmitted toward the subject. The reflected signal is received
by the receiving antenna (RX) and sent to the injection port of the VCO as the injection
signal. The vital sign information modulated in the injection port signal is demodulated
by the self-injection locking mechanism. The self-injection locking architecture provides
higher signal gain at low modulation frequency and improved noise attenuation at long
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distance detection. A successful experiment has been achieved with a subject seated at
a distance of 50 cm. Figure 1-5 shows a block diagram of the self-injection locking
architecture of vital sign detection.
Figure 1-5. Self-injection locking vital sign radar architecture [78].
1.3.3 Advances in Signal Processing Techniques
The basic signal processing methods for vital sign detection are complex signal
demodulation [42] and arctangent demodulation [38]. In complex signal demodulation,
the baseband I/Q signals are multiplied together so that the complex signal is free from
residual phase and optimum/null detection problem. In arctangent demodulation, the
algorithm calculates the Doppler phase shift as ψ = arctan(Q/I); therefore, the
optimum/null problem is also eliminated.
Multiple-input, multiple-output (MIMO) and single-input, multiple-output (SIMO)
techniques have been introduced to detect vitals sign from multiple subjects [35][38].
Using these multiple output algorithms, it is proven by the generalized likelihood ratio
test (GLRT) that the distinguishing among 2, 1, or 0 subjects can be achieved. MIMO
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techniques are also used to cancel the random body movement of the subject and
improve the sensitivity of the vital sign detection system [42][81]. Since most of the
human body under test has random body movement, e.g. a seated person randomly
moving in two horizontal dimensions, the body movement presents a challenge to
detect successfully the vital sign movements. It has been reported recently that the
difference in phase characteristics of the vital sign movements and body movement
creates an opportunity for random body movement cancellation in single direction [51]
and in two dimensions and above [81].
Aside from increasing the number of the detectors, the improvement in signal
processing is also taking place in the increasing of the number of carrier frequencies. In
a multiple-frequency Doppler radar system, RF signals with different carrier frequencies
are transmitted toward the subject in very small beam angles so that the reflection point
of the RF signals are different. The differential measurement can be used to cancel
random body motions. A dual helical antenna and simple direct-conversion radar are
reported to use this differential measurement approach [58]. Two-frequency radar [43]
and multiple-frequency interferometric radar [56] are reported.
In the spectrum of the vital sign signals, the third and fourth harmonics of the
respiration signal is close to the heart beat frequency, leading to difficulty for extraction
of the correct heart beat signal. A parametric and cyclic optimization approach, referred
to as the RELAX algorithm, is designed to mitigate these difficulties. The
implementation of the RELAX algorithm in vital sign detection was reported in 2010 [73].
Other signal processing methods for vital sign detection include adaptive filtering [75],
Kalman filtering and principal component combining of quadrature channels [52], fast
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clutter cancellation [77], DC information preservation [46], and blind source separation
[44].
1.3.4 Miniaturization and System-on-chip
Recently, the realization of the detection in the compact portable system has
become a new focus of interest. Many of the applications such as sleep apnea
monitoring and earthquake search-and-find rescue require integration of the entire
system in small portable packages. An integrated noncontact vital sign detector was
developed for handheld applications [53]. Noise performance of the integrated detector
was investigated to guide the hardware design [74]. In addition, three reports of vital
sign sensor integrated circuits chip have been published [45][59][76].
This dissertation will present a multiple sensor platform for two-dimensional
random body movement cancellation. A portable noncontact vital sign detector for
handheld applications will be presented. Details of hardware design and noise analysis
of the individual sensor will be discussed. It will also introduce the new noncontact vital
sign detector with on-board antennas and real-time noncontact vital sign detection
software.
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CHAPTER 2 MULTIPLE WIRELESS SENSOR PLATFORM USING ALGAN/GAN HIGH ELECTRON
MOBILITY TRANSISTOR DIFFERENTIAL DIODE SENSORS
2.1 Hydrogen Sensors with Different Fabrication Technologies
There is great interest in detection of hydrogen sensors for use in hydrogen-fuelled
automobiles and with proton-exchange membrane (PEM) and solid oxide fuel cells for
space craft and other long-term sensing applications. These sensors are required to
detect hydrogen near room temperature with minimal power consumption and weight
and with a low rate of false alarms. Due to their low intrinsic carrier concentrations,
GaN- and SiC-based wide band gap semiconductor sensors can be operated at lower
current levels than conventional Si-based devices and offer the capability of detection to
∼600◦C [1–23]. The ability of electronic devices fabricated in these materials to function
in high temperature, high power, and high flux/energy radiation conditions enable
performance enhancements in a wide variety of spacecraft, satellite, homeland defense,
mining, automobile, nuclear power, and radar applications.
AlGaN/GaN high electron mobility transistors (HEMTs) show promising
performance for use in broadband power amplifiers in base station applications due to
the high sheet carrier concentration, electron mobility in the two-dimensional electron
gas (2DEG) channel, and high saturation velocity. The high electron sheet carrier
concentration of nitride HEMTs is induced by piezoelectric polarization of the strained
AlGaN layer and spontaneous polarization is very large in wurtzite III-nitrides. This
provides an increased sensitivity relative to simple Schottky diodes fabricated on GaN
layers [4–23]. An additional attractive attribute of AlGaN/GaN diodes is the fact that gas
sensors based on this material could be integrated with high-temperature electronic
devices on the same chip. The advantages of GaN over SiC for sensing include the
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presence of the polarization-induced charge, the availability of a heterostructure, and
more rapid pace of device technology development for GaN which borrows from the
commercialized light-emitting diode and laser diode businesses.
In this dissertation, we report on a demonstration of a hydrogen sensing system
using a differential pair of AlGaN/GaN HEMT diodes for hydrogen sensing near room
temperature. This configuration provides a built-in control diode to reduce false alarms
due to temperature swings or voltage transients. The design and optimization of the
detection circuitry, digital signal processing, wireless network, and monitoring states to
maintain an accurate and reliable system were investigated.
2.2 Experiments with Differential Sensor Pairs
AlGaN/GaN HEMT layer structures were grown on C-plane Al2O3 substrates by a
molecular beam epitaxy (MBE) system. The layer structure included an initial 2 μm thick
undoped GaN buffer followed by a 35 nm thick unintentionally doped Al0.28Ga0.72N layer.
The sheet carrier concentration was ∼1×1013 cm−2 with a mobility of 980 cm2/(V s) at
room temperature. We designed a mask that employed a differential diode
configuration, with a Pt-contact device as the active member of the pair and a Ti/Au
contact device as the control. Mesa isolation (the electrical components of an integrated
circuit are isolated, using P–N junction or dielectric isolation) was achieved with 2000Å
plasma enhanced chemical vapor deposited SiNx. The Ohmic contacts was formed by
lift-off of ebeam deposited Ti (200 Å)/Al (1000 Å)/Pt (400 Å)/Au (800 Å). The contacts
were annealed at 850◦C for 45 s under a flowing N2 ambient in a Heat pulse 610T
system. Schottky contacts of 100Å Pt for the active diode and 200Å Ti/1200Å Au for the
reference diodes were deposited by e-beam evaporation. Final metal of e-beam
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deposited Ti/Au (300 Å/1200Å) interconnection contacts was employed on the HEMT
diodes. Figure 2-1 shows an optical microscope image of the completed devices.
Figure 2-1. Microscopic images of differential sensing diodes. The opening of the active diode was deposited with 10nm Pt, and the reference diode was deposited with Ti/Au.
Figure 2-2 shows the absolute and differential forward current– voltage (I–V)
characteristics of the HEMT active (top) and reference (bottom) diodes, both in air and
in a 1% H2 in air atmosphere. For the active diode, the current increases upon
introduction of the H2, through a lowering of the effective barrier height. The H2
catalytically decomposes on the Pt metallization and diffuses rapidly to the interface
where it forms a dipole layer [23]. The differential change in forward current upon
introduction of the hydrogen into the ambient is ∼1–4mA over the voltage range
examined and peaks at low bias. This is roughly double the detection sensitivity of
comparable GaN Schottky gas sensors tested under the same conditions, confirming
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that the HEMT-based diode has advantages for applications requiring the ability to
detect hydrogen even at room temperature.
Figure 2-2. Absolute and differential current of HEMT diodes. (a) Absolute and differential current of HEMT sensor diode. (b) Absolute and differential current of HEMT reference diode.
2.3 Wireless Multiple Sensor System
2.3.1 System Overview
The wireless sensing system consists of six wireless sensor nodes and a base
station including a wireless receiver and a computer equipped with monitoring software.
The topology of the sensor network is star topology as shown in Figure 2-3. The star
topology reduces the chance of network failure by connecting all of the sensor nodes to
a central node.
Each sensor node consists of a differential sensor pair, detection circuits,
microcontroller, wireless transceiver, and power management circuits. The main part of
the detection circuits is an instrumentation amplifier used to sense the change of current
in the device. The current variation, embodied as a change in the output voltage of the
detection circuit, is fed into the microcontroller. The microcontroller calculates the
corresponding current change and controls the transceiver to transmit the data to the
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wireless network base station. The block diagram of the sensor node and the wireless
network base station are illustrated in Figure 2-3a.
The user-friendly hydrogen sensor network monitoring software on the base
station computer performs functions such as communication port setting, emergency
alarm, data collection, and data plot. The monitoring states, transition, and actions are
defined in this software. The software also sends the data to a remote web site through
the Internet. Internet users around the world can access the web page at any time and
see the plotted data exactly the same as those on the local sensor base station. The
block diagram of the remote sensing system is shown in Figure 2-3b.
Figure 2-3. Star network layout. The wireless sensing system consists of six wireless sensor nodes and a base station. The wireless network is enabled by IEEE 802.15.4 WPAN (Zigbee) technology. The Zigbee wireless communication nodes are operating at 2.4 GHz.
29
Figure 2-4. Block diagram of wireless multiple hydrogen sensor system. (a) The wireless sensing system consisting of differential sensor pair, differential sensor pair, detection circuits, microcontroller, wireless transceiver, power management circuits, receiver and computer. (b) The remote sensing system consisting of computer, web server, ASP.NET program, and hydrogen sensing web site.
2.3.2 Detection Circuits
In this design, the differential change of the forward current in the hydrogen sensor
device causes a voltage variation on the sensor node. This voltage variation is usually
very small as demonstrated previously. A differential input single-ended output
instrumentation amplifier is used to amplify precisely the variation to a level that will be
sampled correctly by a microcontroller. To obtain optimal input characteristics, two
30
voltage followers buffer the input signal. The input impedance of the buffers is very high
and allows the instrumentation amplifier to be used with high source impedances and
still have low error. Also the high input impedance tolerates the unbalanced source
impedance with no degradation in common mode rejection. The buffers drive the
balanced differential amplifier. The gain of the amplifier is set by the feedback voltage
divider. The bias voltage of the sensors is set to 1.8V to reduce the power consumption.
The circuits and specifications are shown in the detection circuit part of Fig. 2-3a.
The signal from the instrumentation amplifier is in the continuous analog form. In
order to transmit the data through digital wireless transceivers, the data should be
digitized first. A MSP430 ultra-low power microcontroller is used to perform the analog-
to-digital conversion. The choice of microcontroller is based on the power consumption
consideration. It is programmed to operate in low-power mode after the analog-to-digital
conversion operation and reduce the power consumed at the processor core. The on-
chip analog-to-digital converter (ADC) features a data transfer controller. This feature
allows samples to be converted and stored without CPU intervention.
2.3.3 Zigbee Wireless Network
An IEEE 802.15.4 WPAN (Zigbee) compliant 2.4GHz wireless sensor network has
been set up for data transmission, to accommodate a number of hydrogen sensor
nodes implemented in the system. The Zigbee compliant wireless network supports the
unique needs of low-cost, low-power sensor networks, and operates within the
unlicensed 2.4GHz band. The transceiver module is completely turned off for most of
the time, and it is turned on to transmit data in extremely short intervals. The timing of
the system is shown in Figure 2-4. When the sensor module is turned on, it is
programmed to power up for the first 30 s. Following the initialization process, the
31
detection circuit is periodically powered down for 5 s and powered up again for another
1 s, achieving a 16.67% duty cycle. The ZigBee transceiver is enabled for only 5.5ms to
transmit the data at the end of every cycle. This gives a RF duty cycle of only 0.09%
and significantly saves the power consumption.
Figure 2-5. Sequence of transceiver module operation.
2.3.4 Wireless Sensor Network Monitoring Software
The hydrogen sensor network software was developed using NET Framework
v3.5. The software can be installed and launched on any Windows-based operating
system. It performs the functions of communication port setting, emergency alarm, data
collection, and real-time data plot viewing. The software also defines the monitoring
states, transitions, and actions. In addition, a remote hydrogen sensing system was
developed to present the data plot to Internet users, regardless of the user locations.
The general control interface and the graphical data view of the software are presented
in Figure 2-5a and b.
The data channeled from the Zigbee receiver contains sensor ID, sensed currents,
and sensed voltage. The software uses these data to calculate the density of the
hydrogen gas. Based on the calculation the monitoring state will either transit or stay.
And the corresponding action will be performed. The data are transferred in the same
32
format to the remote web site through the Internet. Internet data transfer employs the
data package technology for safety purposes. The data packages are stored and
analyzed again at the web server. A web page is constructed for displaying sensor
information. The web page is presented in Figure 2-6. Users can select different time
windows from real time to 6 days to display the sensor data.
2.3.5 Monitoring States, Transitions, and Actions
The state diagram of the hydrogen sensor network software is illustrated in Figure
2-7. The monitoring states include: initialize, collect data, analyze data, emergency, and
sleep. The state machine runs through each state until a possible emergency hydrogen
density is detected and sustained for 20 s. The emergency threshold was set at a level
that hydrogen concentration would be high enough to pose any danger. In case of an
emergency, the software will trigger the alarm and make phone calls to the numbers
listed in the “Emergency Calls” list (a modem and phone line connected to the server
computer is required). The Internet data transfer and storage is performed in the
“Collect Data” state.
2.3.6 Packages
The sensor module is fully integrated on an FR4 PC board as shown in Figure 2-
8a. The FR4 PC board has a thickness of 0.062" and is measured 2.75" x 1.52". The
circuit board is enclosed in a plastic package as shown in Figure 2-8b, which has a
sensor guard to protect the sensor device from being damaged by an external object.
The circuit board is powered by the AC power and backed up by a 9-V battery. A power
sensing chip is used to sense the voltage from the wall plug adapter. In the case of
power failure, the power management circuits will switch to 9-V battery. The base
33
station consisting of a wireless receiver and computer are presented in Figure 2-9. A bill
of material of the transceiver is listed in Table 2-1.
Table 2-1. Wireless hydrogen sensor board bill of material
Block Vendor Specification
ZigBee RF Module
Digi
2.4 GHz operating frequency, -92 dBm receiver sensitivity, 90 m outdoor range, 250 Kbps data rate, 0 dBm output power
MSP430 Microcontroller
TI 8 MIPS, 1.8–3.6 V operating voltage, up to 60 KB FLASH, 12-bit SAR ADC
Crystal ABRACON 32.768 KHz operating frequency, 12.5 pF load capacitance, through hole mounting, ± 20 ppm frequency tolerance, -20°C to +70°C operating temperature
Linear Regulator
Maxim 1.8V, 2.5V, 3.3V, and 5V fixed output voltage, 2.5V to 12V Input Voltage Range, 200 mA max Output Current
Power Supervisory Circuits
Maxim 5.0V, 3.3V, 3.0V, and 2.5V power-supply monitoring, 1.2V operating supply voltage
Operational Amplifier
Maxim 1 V to 5.5 V voltage operation, 9 μA supply current consumption, rail-to-rail output swing
Switch Switchcraft
DPDT contact configuration, raised slide actuator, 125 V maximum contact voltage, 3 A maximum contact current
PCB Goldphoenix 2 layers, 0.062" board thickness, 1 oz copper thickness, FR4-TG130, two side silk, 2.75" x 1.52" board size
Sensor Enclosure Box Enclosures
Plastic, 1.5" x 2.75" x 4.6" box size, 9 V Battery
34
Figure 2-6. Images of wireless sensor network monitoring software (a) An image of general control interface including monitoring status, data file, and emergency call functions. (b) An image of graphical data view interface including data view and curve view.
35
Figure 2-7. An image of the hydrogen sensing website showing the real-time responses of the hydrogen sensors.
Figure 2-8. State flow diagram of the hydrogen sensor network software monitoring mechanism.
36
Figure 2-9. Individual hydrogen sensor package. (a) A photograph of differential hydrogen sensor PC board including differential hydrogen sensor, detection circuits, microcontroller, and wireless transmitter. (b) A photograph of sensor node package with sensor guard.
Figure 2-10. A photograph of base station including wireless receiver and computer. The picture is taken at the Greenway Ford dealership, Orlando, Florida.
2.4 Field Test
Field tests have been conducted both at the University of Florida and at Greenway
Ford in Orlando, FL. The outdoor tests at the University of Florida have been conducted
37
several times for a period of 2 weeks, to test a range of possible real world conditions in
a more controlled setting. Hydrogen leakage was successfully detected for hydrogen
concentrations in a range from1% to 100% at the point of the leak and heights ranging
from 1 to 10 ft in an outdoor environment. The setup at Greenway Ford was aimed to
test the stability of the sensor hardware and the server software under the actual
operating environment. The test was started on the 30th of August 2006 and has run
until the time of this report. Six sensor modules and the server have been functioning
since then. A web site was also developed to share the collected sensor data via the
Internet (http://ren.che.ufl.edu/app/default.aspx), as shown in Figure 2-6. This figure
illustrates the current level of each sensor on the network and data for real time and the
choices of past 85min, 15 h, or 6 days can be viewed on the web site. If any of the
sensor’s current increases to a level that indicates a potential hydrogen leakage, the
alarm is triggered. The server program for the wireless sensor network could also report
a hydrogen leakage emergency through phone lines using the computer’s modem to
send a message to cell phones, beepers, fire department, and so forth.
2.5 Summary
In conclusion, a wireless sensor network which uses the IEEE 802.15.4 standards
has been constructed to transmit data from a number of hydrogen sensors to a base
station. A user-friendly program has been developed to share the data collected by
base station to Internet, so that the data can be analyzed and monitored from anywhere
with an Internet connection. A cell phone alarm has been implemented to report any
potential hydrogen leakage to responsible personnel. The entire system has been
tested for functionality and stability both at the University of Florida and at Greenway
38
Ford in Orlando. Field tests show that the low-power hydrogen sensor can work stably
and react quickly to possible hydrogen leakage.
39
CHAPTER 3 MULTIPLE DOPPLER RADAR SENSOR PLATFORM FOR TWO-DIMENSIONAL
HIGH-SENSITIVITY HUMAN PHYSIOLOGICAL MOVEMENT DETECTION
3.1 Challenges of Body Movements
The noncontact vital sign detection system [26][27] is designed based on the
Doppler phase modulation effect in microwave frequency bands. The radar transmits an
ultra-low-power un-modulated electromagnetic wave toward the human body, where it is
reflected and phase-modulated by the periodic physiological movement, i.e. the
respiration and heartbeat. By down-conversion and proper signal processing of the
reflected signal, the vital signs can be extracted.
To achieve accurate and robust performance, researchers working on noncontact
vital sign detection have spent great efforts for more than two decades on several
technical challenges. Among these challenges, noise has always been a main concern.
One of the main challenges, the influence of clutter noise and phase noise, has been
solved by the range-correlation effect by means of applying the same transmitted signal
to the receiver as the reference signal [33]. In order to understand the overall noise
performance of vital sign detectors, the investigation of the signal-to-noise ratio (SNR)
of the detectors in quadrature direct conversion architecture [33][42] clarifies the effect
of SNR on vital sign detections [74]. In addition to the inherent noises from the
electronic circuits, the noise from the random movement of the human body presents
even severer distortion to the vital sign information. To solve the random body
movement challenge, researchers introduced the double-detector technique to cancel
the body movement in single direction [42].
As the above efforts kept on pushing the non-contact vital sign detection closer to
daily applications, we needed to give special attention to a few challenges before we
40
could accomplish a practical vital sign detection system. Since most of the human body
under test conditions has random body movement in at least two dimensions, e.g. a
seated person randomly moving in two horizontal dimensions, the cancellation
techniques need to be expanded to multiple dimensions. In addition, the resultant vital
sign signal trajectories from the multiple detectors should be compared carefully in the
constellation graph and compensated with certain DC offset in real time to ensure a
correct recovering of vital sign information, i.e. the respiration rate and heart rate.
In this chapter we report a two-dimensional noncontact vital sign detection system
with Doppler radar array for the accurate and body movement calibrated operation. The
system consists of four noncontact vital sign detectors placed at the four sides of the
human body. Each noncontact vital sign detector includes a radio frequency transceiver,
a baseband analog circuit, and a power management circuit. The baseband signals
from the multiple detectors are channeled to a computer for spectrum analysis. Details
of sensitivity enhancement and DC offset compensation algorithm will be discussed.
3.2 Principle of Noncontact Vital Sign Detection
Figure 3-1(a) shows a block diagram of the quadrature direct conversion vital sign
detection system. Figure 3-2(b) shows a continuous-wave (CW) Doppler-radar vital sign
detection experiment setup. The vital sign detector consists of an RF transceiver front-
end, a baseband amplifier, and a built-in analog-to-digital converter (ADC) of the digital
signal processor.
For vital sign detection, the radar transmits a continuous-wave un-modulated RF
signal toward the human subject. The transmitted signal can be represented as
tfttT 2cos)( (3-1)
41
(a)
(b)
Figure 3-1. (a) Block diagram of the vital sign detection system. (b) Setup of the vital sign detection experiment
The RF signal is reflected on the surface of the human body. The reflected signal
is modulated by the physiological movement x(t) and received at Node 1. The received
RF signal can be represented as
c
dt
txdfttR 00 244
2cos)(
(3-2)
42
where f is the carrier frequency, λ=c/f is the wavelength, d0 is the distance between the
vital sign detector and the subject, x(t) is the time-varying displacement of the subject,
and φ is the phase noise of the received signal.
The received signal is then amplified and fed to the mixer at node 2. When the
signal at node 2 is mixed with the LO signal derived from the transmitted signal, the
down-converted signal at node 3 can be represented as
txtxtB rh 44
cos (3-3)
where xh(t)=mhsin(ωht) and xr(t)=mrsin(ωrt) represent the heartbeat and respiration
movement, and Δφ is the residual phase noise. The down-converted signal is amplified
by a baseband amplifier and the amplified baseband signal at node 4 is sampled and
digitized by the ADC. A digital signal processor or a computer can be used to analyze
and calculate the magnitude of each frequency component within the digitized signal.
Figure 3-2 shows an example of the baseband time domain signal and frequency
domain spectrum using a Doppler radar on a CMOS chip. The radar chip uses the
homodyne quadrature architecture and has two baseband output channels (I/Q). Since
the same transmitted signal is used as the LO to down-convert the received signal
which is phase-modulated by the physiological movement, there is no frequency offset
in the baseband. The timing delay does not affect the detection either. Therefore, no
synchronization mechanism is needed for the system.
With the transmitted signal as LO for down-conversion, the range-correlation effect
[33] minimizes the distortion of the baseband from LO phase noise. Without the range-
43
correlation effect, the Δφ term in the baseband signal B(t) will change over time and
distort the detection of the phase modulation by the physiological movement.
In order to study the spectrum of the baseband signal, the sinusoidal function in
Equation 3-3 can be expanded using Fourier series. The Fourier series representation
of the phase modulated signal in Equation 3-3 is:
l
tjlhl
k
tjkrk ee
mJe
mJtB hr
44Re
tltkm
Jm
J hrr
k
l
hl
k
cos44
(3-4)
Figure 3-2. Baseband I/Q signals: time domain signal and frequency domain spectrum. From [57].
44
From Equation 3-4, we can observe that the signal strength of the vital sign signals
are determined by the harmonics, as well as the intermodulation tones of the heart beat
and respiration signals. For example, the detected heart beat signal is determined by
the (l = ±1, k = 0) terms, and its signal strength J±1(4πmh/λ) J0(4πmr/λ) is dependent on
both mr and mh.
3.3 Two-Dimensional Random Body Movement Cancellation
The block diagram of the two-dimensional vital sign detection system with Doppler
radar array is shown in Figure 3-3. The measurement is performed from the four sides
of the human body. When the human body roams randomly in the horizontal plane, the
body movement generates a significant noise spectrum component in the frequency
domain of the output signal in every individual detector. By combining the random
frequency shifts caused by body movement in the multiple detectors, the noise can be
extracted and canceled in spectrum analysis.
With the random body movement in presence, the baseband signal detected by I
and Q channel of the radar array can be modeled by complex time series as:
1
11
1
1
11
444exp
txtxtxjtS bhr
(3-5.a)
2
22
2
2
22
444exp
txtxtxjtS bhr
(3-5.b)
3
33
3
3
33
444exp
tytxtxjtS bhr
(3-5.c)
4
44
4
4
44
444exp
tytxtxjtS bhr
(3-5.d)
45
Figure 3-3. Block diagram of the vital sign detection system with Doppler radar array.
where xrk = mrksin(ωrt) and xhk = mhksin(ωht), k = 1,2,3,4 are the respiration-induced and
heartbeat-induced physiological movement amplitudes on the front chest wall, back, left
side and right side of the human body; ωr and ωh are the angular frequency of the
respiration and heartbeat; λk for k = 1,2,3,4 are the wavelengths of the radar carrier
signals (near 5.8 GHz in this paper); xb(t) = Vxt and yb(t) = Vyt are the x- and y-axis
components of the planar body displacement Db(t) = xb(t)x + yb(t)y. The body movement
resembles a random walk in a two-dimensional space. The random variable Vx = Vxx +
Vyy, that is the speed during a movement period, is approximated by the discrete
46
random variable series uniformly distributed between –4 mm/s and 4 mm/s. The
modeled random walk of the human body is shown in Figure 3-4(a) as the inset.
Figure 3-4. Simulation of 2-D random body movement cancellation. (a) Baseband
spectra obtained from individual detectors when planar random walk of human body is present. The planar random walk of body is shown in the inset. (b) Baseband spectra recovered by two-dimensional random body movement cancellation using radar array, showing respiration at 21 beats/min and heartbeat at 72 beats/min.
The pairs of physiological movements on opposite sides of the body, e.g. xr1 and
xr2, move in the same direction relative to their respective detecting radar. On the other
hand, when the body is drifting toward one of the radars, it is moving away from the
opposite one. The signs of the body displacements in each dimension are opposite
because the movement directions are opposite relative to the pair of detectors. Since
the baseband output signals in the radar array are in phase but the body movement
47
components are 180 degree out of phase, by multiplying the four vectors Sk(t), the noise
from the two-dimensional random walk of human body can be eliminated. Note that the
different amplitudes mrk and mhk of respiration induced movement xrk and heart beat
induced movement xhk are summarized together in Equation 3-6, thus will not affect the
cancellation technique.
The processed time series of the baseband signal with pure respiration and
heartbeat information is:
tStStStStS 4321
4
1
4
1
4
1
sin4sin4
expk
k
h
k
hkr
k
rk tmtm
j
(3-6)
The simulated baseband spectra from the multiple detectors are shown in Figure
3-4(a), and the recovered baseband spectrum is shown in Figure 3-4(b). The simulation
result verified the theory.
3.4 Sensitivity Improvement Using Doppler Radar Array
In addition to cancelling the planar random walk of the human body, the Doppler
radar array approach of vital sign detection also effectively strengthens the vital sign
components within the frequency domain of the signal. Harmonic analysis using Fourier
expansion [82] on the recovered baseband signal in Equation 3-6 gives
j
hr etCtCjtS sinsin2 0110
j
hr etCtC 2cos2cos2 0220 (3-7)
48
and
4
1
4
1
44k
hkj
k
rkiij mJmJC (3-8)
where Jn is the Bessel function of the first kind. Since the magnitude of ejΦ is 1 and is
independent of the value of Φ, the sensitivity on respiration and heartbeat detection are
determined by the value of the 1st Fourier coefficients at frequency ωr and ωh in
Equation 3-7, i.e. 2C10 and 2C01.
1.5 2 2.5-0.2
0
0.2
0.4
0.6
0.8
1
Respiration Movement Summation (mm)
Am
plit
ude
log(J1r
/J1r-single
)
0.3 0.4 0.5 0.6
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Heartbeat Movement Summation (mm)
Am
plit
ude
log(J0h
/J0h-single
)
(a) (b)
1.5 2 2.5
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Respiration Movement Summation (mm)
Am
plit
ude
log(J0r
/J0r-single
)
0.3 0.4 0.5 0.6
0
0.2
0.4
0.6
0.8
1
1.2
Heartbeat Movement Summation (mm)
Am
plit
ude
log(J1h
/J1h-single
)
(c) (d)
Figure 3-5. Amplitude of Bessel functions: (a) J1(4πΣkmrk/λ); (b) J0(4πΣkmhk/λ); (c) J0(4πΣkmrk/λ); (d) J1(4πΣkmhk/λ)
49
The behavior of the function J0(4πΣkmhk/λ) and J1(4πΣkmrk/λ) is presented in
Figure 3-5 (a) and (b). The magnitude of human heartbeat-induced movement mh is in
the range of 0.1 mm, which leads to a very small Bessel function parameter 4πΣkmhk/λ
at the frequency of 5.8 GHz (e.g., for Σkmhk = 0.6 mm and λ = 51.7 mm, 4πΣkmhk/λ =
0.15), thus J0(4πΣkmhk/λ) is close to 1. In the case of respiration detection, therefore, the
coefficient C10 can be approximated by J1(4πΣkmrk/λ). When 4πΣkmrk/λ is small,
J1(4πΣkmrk/λ) increases rapidly as 4πΣkmrk/λ increases. The combination of the
baseband signals of multiple detectors described in Section II increases the value of
4πΣkmrk/λ and thus improves the respiration sensitivity which depends on C10. In our
simulation (for f =5.8 GHz and Σkmrk goes from 1.2 mm to 2.6 mm), the multiple
detectors approach versus the single detection from the front chest wall almost doubles
the value of C10 from 0.14 to 0.30. The increase of C10 as the number of detectors
increases is shown in Figure 3-6.
1 2 3 40
0.1
0.2
0.3
0.4
Number of Detectors
Am
plit
ude o
f F
requency C
om
ponents
Respiration sensitivity: C10
Heartbeat sensitivity: C01
Figure 3-6. Respiration and heartbeat sensitivity improves as the number of detectors
increases.
50
In the case of heartbeat detection, the detection sensitivity is related to the
coefficient C01. The behavior of the function J0(4πΣkmrk/λ) and J1(4πΣkmhk/λ) is
presented in Figure 3-5 (c) and (d). In the range of the vital sign movement amplitude,
typically from 0.1 mm to 3 mm, as mr and mh increase, J0(4πΣkmrk/λ) decreases and
J1(4πΣkmhk/λ) increases. Since the change in J1 is faster than J0, C01 increases as mr
and mh increase. In effect, the signal strength at heartbeat frequency is also improved
by the multiple detector approach. In our simulation (for f =5.8 GHz, Σkmrk goes from 1.2
mm to 2.6 mm, and Σkmhk goes from 0.1 mm to 0.6 mm), the combination of multiple
baseband signals increases the value of C01 from 0.029 to 0.066. The increase of C01
as the number of detectors increases is shown in Figure 3-6.
3.5 DC Offset Compensation
The Doppler radar array approach for noncontact vital sign detection is not
immune from the disturbance of DC offset. Figure 3-7 shows the distortion of heart beat
information while DC offset with amplitudes of 20%, 40%, and 60% of the signal
amplitude is present in different number of detectors. In fact, based on the simulation,
as the number of detectors increases, DC offset will introduce more noise to the
spectrum. In order to guarantee an accurate recovery of the vital sign spectrum, a DC
offset compensation algorithm is developed to cancel the unwanted DC offset.
Figure 3-8(a) shows the baseband signal trajectory with unwanted DC offset in the
constellation graph. Based on Equations 3-5.a - 3-5.b, the signal trajectory of the
baseband signal without DC offset will be an arc on the unit circle. When unwanted DC
offset is introduced by down-conversion of the radar carrier wave reflected on a still
object and the leakage from transmitting antenna to receiving antenna, the arc is shifted
in the constellation graph. The compensation algorithm calculates the shift of the arc
51
center and adds corresponding DC values to I and Q signals to move the arc back to
the unit circle. Figure 3-8(a) also shows the calibrated signal trajectory. Figure 3-8(b)
shows the baseband spectrum before and after the DC offset compensation.
70 75 800
0.1
0.2
Beats/Min (a)
No
rma
lize
d S
pe
ctr
um
20%
40%
60%
70 75 800
0.1
0.2
Beats/Min (b)
No
rma
lize
d S
pe
ctr
um
20%
40%
60%
70 75 800
0.1
0.2
Beats/Min (c)
No
rma
lize
d S
pe
ctr
um
20%
40%
60%
70 75 800
0.1
0.2
Beats/Min (d)
No
rma
lize
d S
pe
ctr
um
20%
40%
60%
Figure 3-7. Heartbeat spectra when DC offset is present in (a) one detector, (b) two detectors, (c) three detectors, and (d) four detectors. Vertical dash line marks the correct heartbeat frequency.
Figure 3-8. Illustration of DC offset compensation algorithm. (a) Baseband signal trajectory before and after the DC offset compensation. (b) Recovered baseband spectrum before and after the DC offset compensation. Vertical dash line marks the correct heartbeat frequency.
52
3.6 Experiments
The two-dimensional noncontact vital sign detection system with Doppler radar
array was tested in the laboratory environment. The system consists of four individual
noncontact detectors. Each detector consists of a transceiver as the radio front-end, a
baseband amplifier as an interface to amplify and level-shift the transceiver output, a
data acquisition module to sample and digitize the baseband output signal, and a
computer to perform the spectral analysis. All of the detectors are operating at the 5.8
GHz ISM band. Note that the gains of the detectors are not necessary to be the same. If
assuming the receivers are operating in the linear region, the difference in receiver
gains will only introduce a scalar factor to the time series of the processed signal, thus
will not affect the normalized spectrum of the recovered vital sign signal. A photograph
of the noncontact vital sign detection system is shown in Figure 3-9.
Figure 3-9. Photograph of the RF radar array and TX/RX antennas. The antennas of opposite facing transceivers use orthogonal polarization to prevent one unit from saturating and interfering of the other unit.
53
In the experiment, the human subject was seated in the middle of the detection
system setup, 0.5 m away from each of the vital sign detectors. A sampling frequency of
20 Hz is used. The spectra obtained by the individual detectors are shown in Figure 3-
9(a). The spectrum of the recovered vital sign signal is shown in Figure 3-9(b).
0 20 40 60 80 100 1200
0.5
1
Beats/Min (a)
No
rma
lize
d S
pe
ctr
um
Front
Back
Left
Right
0 20 40 60 80 100 1200
0.5
1
Beats/Min (b)
No
rma
lize
d S
pe
ctr
um
Figure 3-10. Two dimensional random body movement cancellation using multiple detectors array: (a) spectra measured from the front, back, left and right side detectors; (b) Recovered spectrum by the Doppler radar array, the heartbeat information is successfully recovered.
In the figures, the magnitude of the spectrum was normalized for reading
convenience. The unit of the horizontal axis is beats per minute. The spectrum of the
human vital sign shows the subject’s respiration rate at 24 beats/min and the heartbeat
rate at around 80 beats/min. From Figure 3-10(a), the main Doppler frequency shift of
the random body movement can be seen at 17 beats/min. By applying the proposed
54
algorithms, the main spectral component of the planar body movement is eliminated by
the radar array approach, as shown in Figure 3-10(b).
3.7 Limitation of Sensitivity Improvement
In Section 3.4, the sensitivity improvement feature of the four-sensor Doppler
radar array is demonstrated. By using four radar sensors, the detection sensitivities of
both the respiration and heart beat are at least doubled compared to the sensitivity
when using only one sensor. However, the increase in sensitivity using multiple sensors
is not limitless. By using more Doppler radar sensors than the four sensors in this
dissertation, the value of Bessel function J0 and J1 will be pushed to a point to produce
diminishing detection sensitivity.
In the case of respiration detection, the sensitivity is dependent on the behavior of
Bessel function J1(4πΣkmrk/λ) and J0(4πΣkmhk/λ) which is presented in Figure 3-11(a).
The Bessel function J0(4πΣkmhk/λ) at the frequency of 5.8 GHz is very close to 1 even
with many operating sensors since the heart beat movement amplitude is very small
(e.g., with 20 sensors, Σkmhk = 2.4 mm and λ = 51.7 mm, J0(4πΣkmhk/λ) = 0.92).
Therefore, the respiration signal strength C10 can still be approximated by J1(4πΣkmrk/λ).
When the number of sensors is less than a marginal number, J1(4πΣkmrk/λ) increases
rapidly as 4πΣkmrk/λ increases. This Bessel function behavior produces the
improvement of sensitivity discussed in Section 3.4. However, when the number of
sensors is larger than a marginal number, J1(4πΣkmrk/λ) starts to decrease, thus the
respiration sensitivity starts to deteriorate. In our simulation (for f =5.8 GHz and Σkmrk
goes from 0.6 mm to 12 mm), the respiration amplitude peaks when there are 12
sensors in operation. As a result, the maximum sensitivity that a multiple radar system
55
can achieve is 7.7 times that of the single detection from the front chest. The saturation
of C10 as the number of detectors increases is shown in Figure 3-12.
5 10 15 20-2
-1
0
1
2
3
Number of Detectors
Am
plit
ude o
f B
essel F
unction
J1r
/J1r-single
J0h
/J0h-single
(a)
5 10 15 20-3
-2
-1
0
1
2
3
Number of Detectors
Am
plit
ude o
f B
essel F
unction
J0r
/J0r-single
J1h
/J1h-single
(b)
Figure 3-11. Amplitude of Bessel functions: (a) J1(4πΣkmrk/λ) and J0(4πΣkmhk/λ); (b) J0(4πΣkmrk/λ) and J1(4πΣkmhk/λ).
56
5 10 15 200
0.2
0.4
0.6
0.8
1
Number of Detectors
Am
plit
ude o
f F
requency C
om
ponents
Respiration sensitivity: C10
Heartbeat sensitivity: C01
Figure 3-12. Respiration and heartbeat sensitivity peaks at 12 sensors and 8 sensors,
respectively.
In the case of heartbeat detection, the detection sensitivity is related to the
behavior of the function J0(4πΣkmrk/λ) and J1(4πΣkmhk/λ) which is presented in Figure 3-
11(b). In the range of the vital sign movement amplitude, typically from 0.1 mm to 3 mm,
as mr and mh increase, J0(4πΣkmrk/λ) decreases and J1(4πΣkmhk/λ) increases. When the
number of detectors is less than a marginal number, the change in J1 is faster than J0.
When the number of detectors is larger than a marginal number, the change in J1 is
slower than J0. In effect, the signal strength at heartbeat frequency is peaked at a
marginal number of detectors. In our simulation (for f =5.8 GHz, Σkmrk goes from 0.6 mm
to 12 mm, and Σkmhk goes from 0.12 mm to 2.4 mm), the combination of multiple
57
baseband signals results in a maximum sensitivity improvement at 8 detectors. The
maximum value of C01 of the multiple detector approach is 5.5 times that of the single
detector approach. The increase of C01 as the number of detectors increases is shown
in Figure 3-12.
3.8 Limitation of Real-time Large Body Movement Cancellation
By using the multiple Doppler radar array discussed in this dissertation,
cancellation of body movements with amplitude less than 1 cm and speed less than 12
mm/s is achieved. However, real-time cancellation of large body movement still remains
a challenge. When the large body movement is present, the baseband amplifier of the
high-sensitivity vital sign detectors is usually saturated. The saturation of the baseband
signal results in the failure of the body movement cancellation algorithm. In order to
prevent the saturation, the receiver gain and sensitivity need to be decreased in real
time. Therefore, there is a trade-off between the detection sensitivity of the vital signs
and the cancellation capabilities of the Doppler radar array. An intelligent software-
controlled amplifier should be implemented to balance these two factors in real time and
accomplish a real-time large body movement cancellation system.
3.9 Summary
A Doppler radar array is proposed in this chapter to cancel the two-dimensional
human body movement in vital sign detections. By using the radar array, the detecting
sensitivity for respiration and heartbeat is improved. An algorithm for compensating DC
offset is introduced to ensure the proper operation of the two-dimensional body
movement cancellation. Experiments on human subject are performed to verify these
techniques for two-dimensional vital sign detection. The limitation of sensitivity
58
improvement and body movement cancellation are demonstrated with simulation
results.
59
CHAPTER 4 SYSTEM LEVEL INTEGRATION OF HANDHELD WIRELESS NONCONTACT VITAL
SIGN SENSOR RADAR
4.1 Challenges of Portable Applications
In daily applications of the multiple sensor platforms for noncontact vital sign
detection, many of the applications such as sleep apnea monitoring and baby monitor
require integration of the entire system in small portable packages. The realization of
the detection in compact portable system becomes a new focus of interest. Although the
integrations of the radio frequency front-end have been reported in both board level [39]
and chip level [45], most of the previously reported systems rely on computers for real-
time processing or post processing of the signals. In this chapter we report a noncontact
vital sign detector for handheld applications without the need of computers. The radio
frequency transceiver, the baseband analog circuit, and the power management circuit
are integrated on a single printed circuit board. The baseband signal processing board
includes an ARM7TDMI microprocessor and its peripherals. The spectrum of the
baseband signal can be channeled through the PIO controller to a commercial LCD
display. All of the above components can be potentially integrated together in an easy-
to-carry package.
A method to evaluate the overall noise performance of the handheld system will be
reported in this chapter. The overall noise performance evaluation will provide
quantitative guidelines on system architecture choice, components design, and
detection accuracy optimization. We will perform a thorough noise analysis on the
quadrature direct-conversion vital sign detector. A key design choice of mixer is derived
based on this noise analysis. The requirement of output SNR is added as a design
60
guideline for vital sign detectors. Simulations and experiments related to the guideline
are performed and the results will be discussed.
4.2 Vital Sign Detection System Architecture
The block diagram of the vital sign detection system is shown in Figure 4-1.
Typically, a noncontact vital sign detection system consists of a transceiver as the radio
frequency front-end, a baseband amplifier as an interface to amplify and level-shift the
transceiver output, a digital signal processor for spectrum analysis, and a display unit.
The quadrature transceiver, the two-stage baseband amplifier, and the power
management circuit are integrated on a single Rogers printed circuit board as the vital
sign detection radar. The size of the radar is 6.8 cm × 7.5 cm, which is suitable for
handheld applications. Due to the range correlation effect [33], a free-running voltage
controlled oscillator (VCO) can be used to generate the radio frequency signal. As
demonstrated in Li’s experiment [39], due to the non-linear phase modulation effect,
there is an optimal carrier frequency for a subject with certain physiological movement
amplitude. This optimal frequency varies from several GHz to the lower region of Ka-
band. Considering the cost for the handheld radar, the system was designed to have a
carrier frequency from 4-7 GHz. Four VCOs covering different frequency ranges within
the same package can be implemented onto the board. The VCOs guarantee the phase
noise to be always lower than -101 dBc/Hz at 100 kHz offset, and the maximum output
power is more than 2 dBm over the entire frequency tuning range. After the Wilkinson
power divider, one half of the power is transmitted through the transmitting antenna (TA)
and the other half of the power is further amplified and used to drive the mixer in the
receiver chain. The receiver chain contains the receiving antenna (RA), a 3.5 – 7 GHz
low noise amplifier (LNA), two stages of adjustable gain block, and the down-conversion
61
mixer, which is a compact I/Q mixer utilizing two standard double balanced mixer cells.
The radio frequency part of the receiver chain has an adjustable 30 dB gain control
range. The down-converted baseband quadrature signals are amplified by a two-
channel two-stage amplifier, which is realized in a space-saving package with four unit-
gain-stable operational amplifiers.
090
Pow
er
Managem
ent
SAR ADC10-bit
Bit ReverseAlgorithm
FFTAnalysis
AT91SAM7S64MCU
USBDevice
PowerSupply
5 V
3.3 V
18.432 MHzCrystal
User LED
As ADC Indicator
Parallel I/OController
PIO
Pins
To LCD DisplayTA
RA
Figure 4-1. Block diagram of the vital sign detection system.
Except for the VCO and the passive I/Q mixer, all the other components have a
single supply voltage of 5 V. The VCO is 3 V supplied and requires a 0 to 10 V tuning
voltage. Therefore, 5 V and 3 V fixed output voltage regulators are implemented, and an
adjustable output regulator with up to 11 V output voltage is used to tune the carrier
frequency. Either a 6 – 9 V wall plug or a 9 V battery can be used to power up the radar.
A photograph of the complete RF transceiver board and signal processor board is
shown in Figure 4-2. A detailed block diagram of the transceiver board is shown in
Figure 4-3. A bill of material of the transceiver is listed in Table 4-1. The amplified
baseband IQ signals are sampled by the AT91SAM7S64 microprocessor. The on-chip
62
10-bit Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC)
converts the sampled baseband signal to a digital format. The sampling rate can be set
to 2 - 32 Hz to guarantee sufficient headroom over the Nyquist frequency of common
vital signs (respiration and heart beat). A 256-point radix-2 fixed-point Fast Fourier
Transform (FFT) is implemented on the AT91SAM7S64 microprocessor to analyze the
magnitude of each frequency within the vital sign signal. The choice of window size is
optimized to provide maximum frequency resolution and minimum execution time.
Spectrum results can be channeled through Parallel Input/Output (PIO) Controller to a
LCD display such as DisplayTech 64128H LCD to show the measurement result. The
detail of this baseband signal processor design and spectrum analysis algorithm will be
presented in the Section 4.3.
Figure 4-2. Photograph of the RF transceiver board and signal processor board
63
Figure 4-3. Block Diagram of the RF transceiver board
Table 4-1. RF transceiver board bill of material
Block Vendor Specification
VCO1
Hittite
4.46-5.0 GHz, -105dBc/Hz @100 kHz phase noise, 4 dBm output power
VCO2 Hittite 5.0-5.5 GHz, -103dBc/Hz @100 kHz phase noise, 2 dBm output power
VCO3 Hittite 5.6-6.1 GHz, -102dBc/Hz @100 kHz phase noise, 2 dBm output power
VCO4
Hittite 6.1-6.72 GHz, -101dBc/Hz @100 kHz phase noise, 4.5 dBm output power
Switch Hittite DC-8 GHz, 40 dB isolation @6 GHz, 1.8 dB insertion loss @6 GHz, SP4T
Gain Block RFMD DC-8 GHz, 15.5 dB maximum gain, 14.5 dBm P1dB @6Ghz
Mixer Hittite 4-8.5 GHz, 50 dB LO to RF isolation, 40 dB image rejection
LNA Hittite 3.6-7.0 GHz, 16 dB gain, 2.5 dB NF
64
4.3 Baseband Signal Processor Design
The baseband signal processor is implemented mainly with an Atmel
AT91SAM7S64 microprocessor. The embedded FLASH holds the spectrum analysis
code and the on-chip RAM stores the data before and after the signal processing. The
down-converted baseband signal from the preceding radar receiver stage is fed into the
analog input channels AD6 and AD7 of the ADC. This input signal is in the range of 0.1
– 3 V. Therefore, the reference voltage of the ADC is set to 3.3 V to cover the dynamic
range of the signal. The ADC sample and hold time is set to 600 ns which is minimal
and necessary for the ADC to guarantee the best converted final value between two I/Q
channels selection. The conversion resolution is 10 bit which provides 1024
quantization levels.
Conversions of the active analog channels are initiated with a hardware trigger
from the Time Counter channels in the microprocessor. The interval between two
successive triggers is the sampling period. The sampling rate can be set accurately by
configuring the Time Counter. In this application the sampling frequency is within the
range of 2 - 32 Hz. The four most significant digits of the conversion result are shown
with the LEDs for testing purposes. The digitized baseband signal is windowed and
processed by the spectrum analysis code in FLASH. The resultant spectrum is stored in
the RAM and can be channeled to the LCD display through PIO controller of the
microprocessor. A simple power supply circuit is used to stabilize and adjust the voltage
from 5 V to 3.3 V, the input VDD of the AT91SAM7S64 microprocessor. The signal
processing ability of the microprocessor is mostly realized by the spectrum analysis
code and is described in details below.
65
Fast Fourier Transform (FFT) is the core of the spectrum analysis algorithms in
our application. A standard 256-point radix-2 fixed-point FFT is utilized. The algorithm
includes three sub-blocks: sine/cosine lookup table generation, bit reverse of the input
windowed signal, and iterations of butterfly computations. The flow diagram of the
algorithm is shown in Figure 4-4.
GenerateCoefficientLookup Table
ADConversion
User LEDIndication
Write InputRegister
Full?
Window
Bit ReverseInput Buffer toOutput Buffer
DIT FFTIterations
OutputSpectrum
TimerCounter
N
Y
Initialize
Figure 4-4. Flow diagram of the spectrum analysis algorithm
66
The AT91SAM7S64 microprocessor core is running at 40 MHz. This speed is
significantly slower than that of personal computers previously used for signal
processing. Therefore, two adjustments are needed to speed up the FFT calculation for
displaying measurement results real-time. First, the bit reverse algorithm is designed on
the bit manipulation level and takes advantage of the bit-wise operation offered by the
microprocessor. Second, the coefficients in the butterfly computation have a repeated
pattern. Therefore, the coefficients are calculated ahead of time and stored in a lookup
table in the RAM. This will speed up the real-time FFT computation significantly. A
photograph of the signal processor board is shown in Figure 4-5.
Figure 4-5. Photo of the digital signal processor board
67
4.4 Receiver Chain Noise Analysis
The detector is divided into three sub-systems including RF front-end amplifiers,
mixer with LO, and baseband amplifiers. Table 4-2 lists an example of receiver chain
components noise specifications.
Table 4-2. Receiver chain components noise specification
LNA Gain Block Mixer BB Amplifier
Component
Hittite 318MS8G
RFMD NBB-400
Hittite 525LC4
Maxim MAX4478
Gain [dB]
16 15.5 -7.5 43
F [dB]
2.5 4.3 7.5 /
Cumulative F [dB]
2.5 2.6 2.61 /
Vn [nV/√Hz]
/ / / 21
Cumulative Vn [nV/√Hz]
9.51 9.62 9.63 3257
4.4.1 LNA and Gain Block
LNA and gain block make up a cascaded RF system. Their function is to amplify
and scale the received signal to a level that will be acceptable by the mixer. Stationary
noise propagates in these two 50-ohm terminated components and can be measured by
noise figure. The cumulative noise figure of these two components can be calculated
based on the cascaded noise figure equation and is listed in Table 4-2.
4.4.2 Mixer with LO Input
In Doppler radar detection of human vital signs, the baseband signal bandwidth is
typically less than a few Hertz. Conventional Gilbert-type active mixers contain several
noise sources: the transconductor noise, the LO noise, and the noise from the switching
transistors. These noise sources establish an unacceptable noise figure in the
68
interested baseband spectrum. Therefore, Gilbert-type active mixers are not suitable for
the vital sign detection receiver. Passive mixers avoid the transconductor stage in the
active mixers and have no dc bias current. This feature minimizes the flicker noise at
the mixer output. Therefore, a passive mixer was chosen for direct conversion vital sign
detection. Figure 4-6 is a noise figure comparison between a Gilbert active mixer and a
passive mixer designed for a 5.8 GHz radar receiver chip in 0.13 µm CMOS [43][48].
100
102
104
106
108
0
20
40
60
80
100
Frequency (Hz)
Nois
e F
igure
(dB
)
Passive mixer
Active mixer
Figure 4-6. Noise figure of active mixer and passive mixer in 0.13 um CMOS. The
difference between active mixer and passive mixer noise figure at 1 Hz is 64.5 dB.
In order to minimize the flicker noise of the passive mixer, the gate–source voltage
of the switching transistor should be close to Vth. This break-before-make bias
technique will minimize the dc bias current in the switching transistors. For the switching
transistor size, there is a tradeoff between noise figure and capacitive load to the
preamplifier stage. To provide appropriate noise figure in the interested vital sign
69
bandwidth, large switching transistors are used and they produce relatively large
capacitive loads to the preamplifier. As a result, there is a large capacitive load to the
preamplifier stage. This is the reason that a source follower buffer was used at the
preamplifier to drive the mixer in voltage-driven mode.
4.4.3 Baseband Amplifier
The baseband amplifier is an interface that amplifies the transceiver output to a
level that will be acceptable by the ADC. Similar to mixer, an important noise source
disturbing the vital sign information in this sub-system is the flicker noise from the
amplifier and is measured by noise voltage spectral density Vn,BB. In order to minimize
the noise, low noise operational amplifiers should be used. In the example used for
study, an op-amp with input-referred noise voltage spectral density Vn,BB of 21 nV/√Hz
at 10 Hz is used. Another important noise source is the thermal noise of the external
resistor in the feedback loop of the amplifier. The feedback resistor has a value of 140
KΩ and contributes an input-referred noise voltage spectral density of 0.34 nV/√Hz. The
combined noise voltage spectral density of this sub-system is the square root of the
sum of the squared values of the two individual noise voltage spectral densities and can
be calculated to be 21 nV/√Hz.
4.4.4 Complete Noise Performance Evaluation Model
The purpose of the complete noise evaluation is to develop a single figure of merit
that measures the noise in the complete vital sign detector. It is the baseband signal at
Node 4 that is sampled and analyzed to generate the spectrum of vital sign information.
Therefore, the measure of the complete system noise performance is the signal-to-
noise ratio of the signal at this node. Using this figure of merit, one can predict and
70
optimize the overall system noise performance as well as the vital sign detection
accuracy.
The noise in RF front-end of the vital sign detector is measured by noise figure.
The noise in baseband amplifier is measured by noise voltage spectral density. In order
to combine the noise in the two sub-systems, the RF input-referred noise figure can be
converted to noise voltage spectral density at RF output Node 3 using equation
20,
, 102
RFRF GNF
ANTENNAn
RFn
VV
(4-1)
where GRF is the gain of the RF front-end, and Vn,ANTENNA is the RMS value of the noise
voltage spectral density looking into the antenna (50 Ω) [44]. At 25 oC, Vn,ANTENNA equals
to 0.9 nV/√Hz. The RF front-end noise appears as additive noise on the baseband
signal and can be summed with the baseband amplifier noise voltage spectral density
Vn,BB. The combined noise voltage is then amplified and added to the signal at sampler
input Node 4. The noise voltage spectral density at Node 4 can be described by
equation
2
,
2
,4, BBnRFnBBn VVAV (4-2)
where ABB is the baseband amplifier gain of 141. The accumulative noise voltage
spectral densities at 10 Hz are listed in Table 4-1. For the baseband amplifier, the input-
referred noise voltage density adjusted by feedback resistor thermal noise is dependent
on the frequency, especially in the flicker-noise region. The data of this frequency-
dependent relationship can be found in the data sheet of the baseband amplifier. The
total output noise voltage is obtained by integrating the output noise voltage spectral
71
density over the baseband amplifier bandwidth. The output signal-to-noise ratio at Node
4 can be calculated as
B
n
signal
out
dBV
VSNR
0
2
4,
log20 (4-3)
where Vsignal is the baseband signal voltage in RMS, and B is the baseband amplifier
bandwidth and equals to 70.4 kHz. Using the example detector, assuming the signal
voltage being 0.1 V, the overall output SNR is 41.6 dB. If the ADC noise voltage of 5 mV
is included in the noise analysis, the overall output SNR is 26 dB.
4.5 Experiments
The integrated vital sign detector was tested in the laboratory environment. Two
experiments have been performed using the integrated system. First, an actuator
programmed to move in a pattern consisting of a two-tone sinusoidal wave was placed
3 meter away from the detector. The spectrum resulted from this experiment shows the
integrated vital sign detector’s ability to measure accurately the frequency and
amplitude of periodic movements. Second, a human subject was seated at 0.5-m away
and faced the detector. The subject was breathing normally throughout the duration of
the testing. The vital sign detector recorded the vital signal and did the spectrum
analysis on the signal. In the second experiment, we discovered a trade-off between
spectrum sharpness and spectrum response speed, as well as stability. This trade-off
can be used as a guideline in choosing the sampling frequency for different
applications. The transceiver board is configured to run at 5.8 GHz in the experiments.
72
4.5.1 Two-tone Actuator Movement
The diagram illustrating the experiment setup is shown in Figure 4-7. In this
experiment, the actuator was programmed to move in a pattern determined by the
function
tfmtfmtx 2211 2sin2sin)( (4-4)
where m1 = 4 mm, m2 = 2 mm, f1 = 0.1 Hz, and f2 = 0.5 Hz. The baseband signal can be
written as
tfmtfmtB 2211 2sin42sin4
cos)( (4-5)
where λ is the wavelength of the carrier (0.0517m in our case) and is the total residue
phase noise. The actuator is placed 3 meters away from the integrated vital sign
detector. The sampling frequency of the signal processor is set to be 12.8 Hz.
Figure 4-7. Two-tone actuator movement experiment setup
The theoretical and measured baseband signals are shown in Figure 4-8(a) and
(b). The theoretical and measured spectrums generated by the baseband signal
73
processor are shown in Figure 4-8(c) and (d). From the figure, the two tones (0.1 Hz
and 0.5 Hz) can be identified.
4 8 12 16 200
0.2
0.4
0.6
0.8
Time (s)
No
rma
lize
d M
ag
nitu
de
4 8 12 16 200
0.2
0.4
0.6
0.8
Time (s)
No
rma
lize
d M
ag
nitu
de
(a) (b)
0 0.25 0.5 0.75 1 1.25 1.50
0.17
0.33
0.5
0.67
0.83
1
Frequency (Hz)
No
rma
lize
d S
pe
ctr
um
0 0.25 0.5 0.75 1 1.25 1.50
0.14
0.29
0.43
0.57
0.71
0.86
1
Frequency (Hz)
No
rma
lize
d S
pe
ctr
um
(c) (d) Figure 4-8. Theoretical results vs. experimental results of the two-tone actuator
experiment: (a) theoretical baseband signal in two-tone experiment; (b) measured baseband signal in two-tone experiment; (c) theoretical spectrum in two-tone experiment; (d) measured spectrum in two-tone experiment.
4.5.2 Human Respiration and Heart Beat Measurement
The human subject was seated at 0.5-m away from the vital sign detector. In the
experiment, two sampling frequencies were used: 25.6 Hz and 6.4 Hz. The diagram
illustrating the experiment setup is shown in Figure 4-9. The baseband signal and its
spectrum obtained by the baseband signal processor are shown in Figure 4-10. In the
spectrum figure, the magnitude of the spectrum was normalized for reading
convenience. The unit of the horizontal axis is beats per minute. The spectrum of the
74
human vital sign shows the frequency of the subject’s respiration at 20 beats/min and
the heart beat is around 84 beats/min.
Figure 4-9. Human respiration and heart beat measurement setup.
0 2 4 6 8 100
0.2
0.4
0.6
0.8
Time (s)
No
rma
lize
d M
ag
nitu
de
0 12 24 36 48 60 72 84 961080
0.14
0.29
0.43
0.57
0.71
0.86
1
Beats/Min
No
rma
lize
d S
pe
ctr
um
(a) (b)
0 8 16 24 32 400
0.2
0.4
0.6
0.8
Time (s)
No
rma
lize
d M
ag
nitu
de
0 15 30 45 60 75 90 105 1200
0.17
0.33
0.5
0.67
0.83
1
Beats/Min
No
rma
lize
d S
pe
ctr
um
(c) (d)
Figure 4-10. Detected baseband signal and spectra in non-contact vital sign detection. (a) baseband signal detected with a sampling rate of 25.6 Hz; (b) baseband spectrum detected with a sampling rate of 25.6 Hz; (c) baseband signal detected with a sampling rate of 6.4 Hz; (d) baseband spectrum obtained with a sampling rate of 6.4 Hz.
75
4.5.3 Guideline for Selecting the Sampling Frequency
In previously reported vital sign detectors, the signal processing was handled by
computers. The FFT algorithm in Li’s works [39], [42] and [45] used a large window size
of 10240 and a sampling rate of over 20 Hz to achieve a smooth spectrum. However,
the handheld version of a vital sign detector has a limit on the size of the window
because the relatively low-speed microprocessor cannot calculate large windows very
quickly. The handheld vital sign detector in this paper utilizes a 256-point window.
Therefore, the sharpness of the spectrum is now dependent on the sampling rate
selected. As shown in Figure 4-10, the spectrum with the higher sampling rate is not as
sharp as the spectrum with the lower sampling rate. Therefore, if the application needs
sharper spectrum, a low sampling rate should be selected. However, the lower sampling
rate results in a longer measurement period and prolongs the response of the spectrum
to the change in vital sign. Also, any strong interference in this long period will destroy
the spectrum. This dynamic implies that there is a tradeoff between spectrum sharpness
and spectrum response speed, as well as stability for handheld vital sign detectors.
The guideline for selecting the sampling rate is: If the application has a relatively
stationary subject and needs accurate measurement, a low sampling rate is suitable. An
example is the sleep apnea monitoring; if the application needs fast recognition and
quick response time, a higher sampling rate should be used. An example is the search-
and-find rescue mission.
4.5.4 The Effect of Output SNR on Detection Accuracy
The above model was used to simulate the effect of overall output SNR on the
detection of vital signs. A human subject was seated 1 meter away in the experiment.
The subject’s respiration had an amplitude of mr= 0.8 mm and a frequency of 19
76
beats/min. The subject’s heartbeat is simulated to have a strong amplitude of mh = 0.3
mm and a frequency of 72 beats/min. The carrier frequency of the Doppler radar is set
at 5.8 GHz. Figure 4-11 presents the baseband signals and spectrums without noise
and with a low output SNR.
0 5 10 15-0.8
-0.4
0
0.4
0.8
Time (s)
Am
plit
ud
e (
V)
0 30 60 90 1200
0.2
0.4
0.6
0.8
1
Beats/Min
No
rma
lize
d S
pe
ctr
um
(a) (b)
0 5 10 15-0.8
-4
0
0.4
0.8
Time (s)
Am
plit
ud
e (
V)
0 30 60 90 1200
0.2
0.4
0.6
0.8
1
Frequency (Hz)
No
rma
lize
d S
pe
ctr
um
(c) (d)
Figure 4-11. Simulated baseband signal and spectrum in non-contact vital sign detection. (a) baseband signal without noise. (b) baseband spectrum without noise. (c) baseband signal with SNR = 13 dB. (d) baseband signal with SNR = 13 dB.
As shown in Figure 4-11, with a low SNR, the noise floor of the system
overwhelms the heartbeat signal. A low SNR can be caused by either low vital sign
signal strength or high overall noise voltage at the baseband output. The strength of the
77
heartbeat signal is normally 5 to 10 times weaker than that of respiration. Therefore, in
order to guarantee the detection of heartbeat, the received signal level needs to be
roughly 14~20 dB higher than the required signal level for respiration detection only.
4.5.5 The Trade-off between Output SNR and Detection Accuracy
The SNR is related to the detection distance according to a two-way Radar range
equation. A long detection distance results in low signal strength and low detector SNR.
We can verify this effect by conducting an experiment on a short-range low-power
noncontact vital sign sensor node [53] and measuring the vital sign signals in different
distances. In order to calculate the SNR of the measured signal, a band-pass filter and
a band-stop filter is used to separate the signal and noise. The SNR is calculated as the
ratio of the variance of the signal and the variance of the noise. As shown in Figure 4-
12, the measured SNR will decrease by 15.3 dB when the detection distance is
increased by 60.6%.
To raise the SNR of the received vital sign signal, at least one of the following
methods should be used: (1) an increase in transmitter output power; (2) closer
measurement distance; (3) larger physiological movements; or (4) a lower receiver
noise. For a specific measurement setting (human subject and measurement device),
the latter two conditions are relatively fixed. Therefore, based on the first two conditions,
the SNR data should be provided as guidelines for experiment references. Figure 4-13
is a chart showing the simulated result of SNR at the measurement distance from 10 cm
to 40 cm for the short-range low-power vital sign sensor node. The chart shows three
sets of simulation results with three output power settings.
78
Figure 4-12. Detected baseband signal and spectrum in non-contact vital sign detection.
(a) baseband signal detected at 16.5 cm, measured SNR is 26.2 dB; (b) baseband spectrum obtained at 16.5 cm; (c) baseband signal detected at 26.5 cm, measured SNR is 10.9 dB; (d) baseband spectrum obtained at 26.5 cm.
10 20 30 40-10
0
10
20
30
40
50
Distance (cm)
SN
R (
dB
)
5 dBm
0 dBm
-5 dBm
Figure 4-13. Simulated receiver output SNR.
79
4.6 Summary
An integrated noncontact vital sign detector for handheld applications is
demonstrated. A low-cost, low-power, and small-size signal processor is developed to
perform the spectrum analysis task. Noise analysis on the quadrature direct-conversion
vital sign detector is demonstrated. The noise characteristics in the detector sub-
systems are analyzed and are combined to form an overall noise performance
evaluation of the vital sign detection system. This integrated system enables the vital
sign detection to be integrated in handheld devices. Experiments on both human
subject and programmed actuator are performed to verify the accuracy of the detection.
The guideline on selecting the sampling frequency for different application is described.
A key design consideration in selecting mixer for vital sign detection is presented. The
guideline of detector SNR is introduced. The wireless noncontact detection system can
be used widely in applications including medical, search-and-rescue, and military
applications.
80
CHAPTER 5 INTEGRATED VITAL SIGN RADAR SENSOR WITH ON-BOARD ANTENNA
5.1 Integration of Vital Sign Radar and Antennas
Although the radar front-end, the baseband analog circuit, and digital signal
processor (DSP) have been integrated on a single printed circuit board and shown
satisfactory performance, the integration of the antennas on-board is of great interest.
Currently, the antennas used to transmit (TX) and receive (RX) the RF signal are patch
antennas with an operating frequency at 5.8 GHz. Integrating the antennas on-board
requires the designer to solve the challenge of the coupling between the TX and RX
antennas. The methods to minimize the coupling will be discussed in this chapter.
Efforts on coupling minimization will help to reduce the DC offset and prevent the
leakage from saturating the receiver. The orientation of both antennas will be further
investigated to minimize substrate coupling.
5.2 Transmitting and Receiving Antenna Arrays Design
Both the transmitting (TX) and the receiving (RX) antenna are designed to be
patch antenna arrays. A patch or microstrip antenna array is a low profile antenna array
that has a number of advantages over other antennas. It is lightweight, inexpensive, and
easy to integrate with accompanying electronics, thus it makes the perfect candidate to
integrate with the Doppler radar circuits. Figure 5-1 shows a patch antenna array model
designed in Ansoft Designer. The patch antenna array is designed for 5.8 GHz
operation on a Rogers RO4350B substrate with 3.48 dielectric constant and 0.032"
thickness.
The equation to determine the initial setting of the width (W) and length (L) of the
microstrip patch antenna are
81
1
2
2
0
rrf
vW
(5-1)
Lf
Lreffr
22
1
00 (5-2)
Here fr is the resonant frequency, εr is the dielectric constant of the substrate, εreff
is the effective dielectric constant of the substrate, ΔL is the length of feed line, and v0 is
the speed of light.
Figure 5-1. Patch antenna array model used in on-board antenna design.
The patch without the feeding network was simulated in Ansoft HFSS to adjust W
for resonance at 5.8 GHz. The input impedance of the feed lines to the patches was
simulated by placing a 50 Ω transmission line at the patch edge. By changing the trench
length, the input impedance was match to be 50 Ω. The power distribution lines are
simulated to match the impedance change caused by the branching. Finally, a right-
82
angle quarter-wave length transformer was used to match the input impedance of the
first power distribution branch to a 50 Ω terminal. The radiation pattern and S11 of the
finalized patch antenna is shown in Figure 5-2 and Figure 5-3. The final dimensions of
the patch antenna array are listed in Table 5-1.
Figure 5-2. Patch antenna array radiation pattern. Maximum gain 11.5 dB is achieved.
Figure 5-3. S11 of patch antenna array. The antenna resonates at 5.8 GHz.
83
Table 5-1. Dimensions of the patch antenna array.
Design Parameter Parameter Value (mm)
Note
W
16.4
Patch width
L 12.9 Patch length WSlot
3.2
Slot width
LSlot
4.3 Slot length
Dx
34 Patch x-axis separation
Dy
34 Patch y-axis separation
Lf1
4 Patch feed line length
W4
3.3 Quarter-wave length transformer width
lambda4
8.2 Quarter-wave length transformer length
W5
2.3 Right-angle Q-W length transformer width
lambda5
6.7 Right-angle Q-W length transformer length
5.3 Orientation of the TX and RX Antennas
The coupling between TX and RX patch antenna arrays is a function of the
position of one array relative to the other and the relative orientation of them [50]. When
the two patch antenna arrays are placed along the H-plane, the mutual coupling
between the two elements are minimum. An illustration of the H-plane orientation is
presented in Figure 5-4. At microwave frequencies, surface waves along the air-
dielectric interface contribute mainly to the mutual coupling. In the H-plane orientation,
the fields in the space between the elements are primarily TE and there is not a strong
dominant mode surface wave excitation, thus producing less coupling between the
arrays.
84
Figure 5-4. H-plane patch antenna array orientation.
5.4 Simulation of the Coupling between TX and RX Antennas
The mutual coupling between two rectangular microstrip patches in H-plane
orientation can be found to be [50]
cos2cossincos
cos2
sin2
0
3
2
0
0
Z
Wk
W
dL
J
sin21
0
0 (5-3)
where Z is the center-to-center separation between the slots, J0 is the zero-order Bessel
function of the first kind, L is the separation of patches along E-plane, and Z is distance
of patches along the H-plane. According to the equation, the mutual coupling between
the two patches will decrease when the separation is increasing.
85
The mutual coupling between two on-board patch antnnas can be calculated by a
simulation set up in Ansoft HFSS. Figure 5-5 shows the TX and RX antennas model
designed in Ansoft HFSS. The two antennas are in H-plane orientation and separated
by a distance of s. The mutual inductance between the two antennas is simulated with a
separation from 115 mm to 155 mm. The mutual coupling or leakage between the two
antennas S12 over frequency band from 5.5 GHz to 6.1 GHz is presented in Figure 5-6.
By using the simulation result, an estimate of the on-board antenna isolation can be
determined. The on-board antennas on the fabricated integrated radar board are
designed to have a separation of 140 mm. The simulated isolation between the TX and
RX antennas are 39 dB at 5.8 GHz.
Figure 5-5. Mutual coupling simulation model in Ansoft HFSS.
86
110 120 130 140 150 160-50
-45
-40
-35
Separation (mm)
dB
(S(P
ort
1,P
ort
2))
5.5 GHz
5.6 GHz
5.7 GHz
5.8 GHz
5.9 GHz
6.0 GHz
6.1 GHz
Figure 5-6. S12 of the on-board TX and RX patch antenna array.
5.5 System Integration of the Vital Sign Detector with On-board Antenna
The quadrature transceiver, the baseband amplification circuits, the power
management circuit, and the TX and RX antennas are integrated on a single Rogers
RO4350B printed circuit board. The size of the integrated portable radar is 20 cm ×
7cm. The TX and RX antennas are placed at the two sizes of the quadrature transceiver
to further reduce the interference between the antennas. The coupling capacitor
connected between the RF front-end and baseband amplifiers is fine-tuned from 10 µF
to 1 µF. This coupling capacitor combined with the input impedance of the baseband
amplifiers forms a high pass filter. By using the lower capacitance, the cutoff frequency
of the filter is increased from 0.13 Hz to 1.3 Hz, thus it rejects the respiration signal and
enhanced the heart beat signal.
87
The integrated radar draws a current of 0.24 A from the power supply and has a
low power consumption of 2.2 W. The output power of the transmitter is 0 dBm which is
within the limit set by IEEE RF Safety Guideline. The photograph of the integrated vital
sign radar sensor is show in Figure 5-7. A real-time vital sign monitoring software is
designed for the integrated radar. The software uses digital filtering to separate the time
domain signal of the respiration and heart beat signal. A screen shot of the software is
shown in Figure 5-8. Vital sign detection on a human subject is successfully achieved
with the integrated hardware and software. The detection results are shown in Figure 5-
8.
Figure 5-7. Photograph of the integrated vital sign radar sensors with on-board antennas.
88
Figure 5-8. Photograph of the real-time integrated vital sign radar software.
5.6 Low-power Design, Link-budget, and Emission Safety
As shown in Figure 4-3, the power management circuits supply the power
consumption of the radar board. It consists of the power chip Maxim MAX603 and
Maxim MAX604. The power chips convert the 9-V power from the wall plug to 3.3 V and
5 V. The number of power chips is determined by the total power needed and the
maximum power each power chip can supply.
The most power-hungry components on the board are the RF amplifiers. Each RF
amplifier (RFMD NBB-400) consumes a current of 50 mA. The low-power design goal of
the vital sign detection radar requires using the minimum number of RF amplifiers to
achieve sufficient detection sensitivity. In order to determine the minimum number of RF
89
amplifiers, a link-budget analysis is needed to determine the minimum gain needed in
the RF receiver chain.
Table 5-2 is a list of detailed data to calculate the received power using the link
budget method. The equation for received power estimate is
2
244
R
GGP rt
r
(5-4)
where Pr is the received power, Pt is the transmitted power, Gt and Gr are antenna gains
of the TX and RX antennas, λ is the signal wavelength in air, σ is the radar cross
section of the target.
The Radar cross section (RCS) σ is a measure of how detectable an object is with
a radar. A larger RCS indicates that an object is more easily detected. The RCS of the
human vital sign is estimated to be 0.01 m2. It will give a received signal power of 20 dB
lower than that using a RCS of 1. Using the equation, the received signal power is
estimated to be -77 dBm.
Table 5-2. Received RF power estimate for 5.8 GHz integrated vital sign sensor.
Value
Frequency
5.8 GHz
Transmitting Power
0 dBm
Antenna Gain
10 dB
Radar Cross Section
0.01 m2
Distance
3 m
Received Power
-77 dBm
90
In order to guarantee a successful detection of vital sign signals, especially the
heart beat signal, sufficient RF front-end gain needs to be present in the receiver chain.
Normally the input of the baseband amplifier should be larger than 2 mV (-41 dBm).
From the result of the link-budget analysis, the RF gain of the receiver should be larger
than 36 dB. To reach this minimum gain requirement, two receiver RF amplifiers are
used in the receiver chain of the integrated vital sign radar sensor. The total power
consumed by the individual radar sensor is 2.1 W. Two MAX 604 and one MAX 603 are
used to provide the power.
The radiation power of the sensor should meet the IEEE safety standard. Shown
in Figure 5-9 is the IEEE RF safety guideline [86]. The maximum power density at
5.8GHz in uncontrolled environments should be lower than 1 mW/cm2. Shown in Figure
5-10 is the power density of the integrated noncontact vital sign detector at distances
from 10 cm to 200 cm. At any detection distance, the radiation of the integrated detector
is lower than 1 mW/cm2, thus it complies with the IEEE RF safety standard.
Figure 5-9. IEEE RF safety standard C95.1-2005.
91
0 50 100 150 2000
2
4
6
8x 10
-3
Detection Distance (cm)
Pow
er
Density (
mW
/cm
2)
Doppler Radar Power Density
Figure 5-10. Power density of the integrated noncontact vital sign detector.
5.7 Summary
An integrated noncontact vital sign sensor with on-board antenna is demonstrated.
A 5.8 GHz patch antenna array is designed for functioning as the individual antenna on
radar board. The theory of mutual coupling between the TX and RX patch antennas are
discussed. The consideration of the relative orientation of the antennas is discussed. A
pair of H-plane aligned patch antenna arrays is modeled and the simulation results are
used as guidelines in designing the on-board antennas. This integrated system is
fabricated on a single PCB. Accompanying software is programmed in Labview.
Experiments on a human subject are performed to verify the functionality of the sensor.
92
CHAPTER 6 CONCLUSIONS
The theory and implementation of multiple sensor platform techniques in hydrogen
sensing and two-dimensional noncontact vital sign detection are presented in this
dissertation. The implemented hydrogen sensing system can detect and display the
detected hydrogen density from the six sensors in real time. A user-friendly program
has been developed to share the data collected by base station to Internet, so that the
data can be analyzed and monitored from anywhere with an Internet connection. Field
tests show that the low-power hydrogen sensor can work stably and react quickly to
possible hydrogen leakage.
The implemented noncontact vital sign detection system can detect and display
the detected vital sign signals from the four sensors. The multiple radar system can
cancel the two-dimensional human body movement in vital sign detections. By using the
radar array, the detecting sensitivity for respiration and heartbeat is improved. An
algorithm for compensating DC offset is introduced to ensure the proper operation of the
two-dimensional body movement cancellation. An integrated noncontact vital sign
detector for handheld applications is demonstrated. The noise characteristics in the
detector sub-systems are analyzed and are combined to form an overall noise
performance evaluation of the vital sign detection system.
The noncontact vital sign detector is further integrated with the transmitting and
receiving antennas. The mutual coupling between TX and RX antennas are studied.
The integrated noncontact vital sign detector is presented. The detector’s power
consumption and radiation is studied. A real-time vital sign detection software
programmed in Labview is demonstrated.
93
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BIOGRAPHICAL SKETCH
Xiaogang Yu received the B.S. degree in physics from Nanjing University, Nanjing,
China, in 2004, the M.S. degree in electrical and computer engineering from the
University of Florida, Gainesville, in 2007, and the Ph.D. degree in electrical and
computer engineering at the University of Florida, in 2011.
His research interests include wireless sensors, biomedical applications of
microwave/RF systems, and microwave/millimeter-wave circuits.
Mr. Yu is a student member of the IEEE Microwave Theory and Techniques
Society (IEEE MTT-S). He was a finalist in the 2009 IEEE Radio and Wireless
Symposium Student Paper Competition.