Remote sensing of mechanical properties of materials using a novel ultrasound transducer and signal processing
ABSTRACT An ultrasound-based remote sensing method to evaluate the mechanical properties of materials is presented. This method consists of a disk-shaped, piezoelectric transducer, operating at its resonance frequency, and a phase-shifted, feedback circuit. Mechanical parameters are derived by analyzing the signal contained in the phaseshifted values of the reflected signal. It is concluded that, using this novel transducer system and signal processing, remote mechanical measurements can be made. Such measurements obviate the need to apply the force-deformation approach and may be used to enable stiffness imaging.
- [Show abstract] [Hide abstract]
ABSTRACT: This study describes the development of Remote-Type Haptic Catheter Sensor System which enables the mechanical property evaluation of a blood vessel. This system consists of a feedback circuit and a piezoelectric ultrasound transducer, and is operated based on a phase shift method so that the entire system oscillates at its inherent resonance frequency. Ultrasound reflected by the blood vessel makes a phase shift of the resonance system depending on the acoustic impedance of the reflector. The phase shift is then measured as a change in resonance frequency of the system; therefore, the detection resolution is highly improved. The correlation between the acoustic impedance and the resonance frequency change of the sensor system was demonstrated using silicone rubbers, metals and actual blood vessels from a pig. The performance of the sensor was also examined using vessel shaped phantom model. Finally, the discussion surveys a possibility of the novel sensor system in an application for intra vascular diagnosis.IEEJ Transactions on Sensors and Micromachines 01/2007; 127(12):546-552.
- [Show abstract] [Hide abstract]
ABSTRACT: in neurosurgery, delineation of tumor boundaries during resection of brain tumors is of substantial relevance. During operation distinction between tumor and healthy tissue rely on the abilities of the surgeon based on visual and tactile differentiation. In this paper a high sensitivity actuator-sensor system using a piezoelectric bimorph is presented. Frequency shift and transfer function of the bimorph’s voltages are detected and evaluated. Sensor’s sensitivity is evaluated using two frequency controls strategies: A phase-locked loop (PLL) and a self-oscillating circuit. Results of measurements conducted on gel-phantoms are presented and discussed.Frequency Control Symposium, 2008 IEEE International; 06/2008
- [Show abstract] [Hide abstract]
ABSTRACT: Minimally invasive surgery minimizes trauma to the patient, however, the loss of tactile feedback impedes the surgeon's ability to locate tumors among healthy tissue. In this paper, a resonance-based instrument to measure the stiffness, damping, and effective mass of a soft material such as biological tissue is presented. It was designed to be small yet has two natural frequencies below 100 Hz so that the effective mass of the tissue would not impact the determination of the stiffness. A state-space model was used to develop a fast and accurate method of extracting the tissue parameters by measuring the natural frequencies and the bandwidth at the first natural frequency. A fast and robust phased-locked-loop-based feedback system is described, which was used to measure the required frequencies. Simulations showed that the system was robust, while subjected to disturbances including hand tremor, tissue parameter variation, and preload. A prototype system showed that the instrument could accurately predict the stiffness, damping, and mass with an average error of 6%, 6%, and 7%, respectively. Experiments on a simulated tissue phantom showed the ability of the instrument to detect a tumour while it was stationary and in motion.IEEE/ASME Transactions on Mechatronics 01/2013; 18(3):973-980. · 3.14 Impact Factor
ieee transactions on ultrasonics, ferroelectrics, and frequency control, vol. 52, no. 3, march 2005 439
Remote Sensing of Mechanical Properties of
Materials Using a Novel Ultrasound
Transducer and Signal Processing
Yoshinobu Murayama, Member, IEEE, Christos E. Constantinou, Member, IEEE,
and Sadao Omata, Member, IEEE
Abstract—An ultrasound-based remote sensing method
to evaluate the mechanical properties of materials is pre-
sented. This method consists of a disk-shaped, piezoelec-
tric transducer, operating at its resonance frequency, and
a phase-shifted, feedback circuit. Mechanical parameters
are derived by analyzing the signal contained in the phase-
shifted values of the reflected signal. It is concluded that,
using this novel transducer system and signal processing,
remote mechanical measurements can be made. Such mea-
surements obviate the need to apply the force-deformation
approach and may be used to enable stiffness imaging.
ment of mechanical properties of materials. In previous
studies  we reported on the use of a sensor system to
measure the mechanical properties of both hard and soft
materials by applying the principles of resonant vibration
technology and the phase-shift analysis. The results of a
number of practical applications using this type of sensor
were previously reported –, characterizing the elas-
tic properties of a number of tissues and glands. In these
applications, it is required that the sensor makes contact
with the object to be evaluated, monitoring the consequent
change in frequency, which is linearly related with acoustic
impedance. The ability to measure the elastic properties
of biological samples is of practical importance because it
provides a descriptor of its inherent anatomical integrity
and is a useful indicator of pathology. As such, tissue stiff-
ness is a significant parameter in clinical diagnosis because
it contributes to the differentiation between the healthy
and pathological organs. Indeed, in the case of many fre-
quently detected tumors, such as those of prostate and
breast cancer, stiffness as revealed by palpation of nod-
ules constitutes the first level of diagnosis. Although the
value of the “felt” hardness of the digital examination for
prostate cancer and palpation of the breast is a valuable
diagnostic test for malignancy, it is still a subjective mea-
sure. In the context of this background, there is a need
riginated by Kleesattel and Gladwell , ultrasonic
hardness testing has been developed for the measure-
Manuscript received April 17, 2003; accepted September 21, 2004.
Y. Murayama and S. Omata are with the College of Engineering,
Nihon University, Koriyama, Fukushima 963-8642, Japan (e-mail:
C. E. Constantinou is with the Department of Urology, Stanford
University Medical School, Stanford, CA 94305.
to develop and test a sensor system to evaluate the elas-
tic properties of various grades and stages of cancer. The
ability to objectively detect tissue stiffness using ultra-
sound would be of particular clinical significance because
the sensitivity and range of detection potentially would be
increased to incorporate structures well below the surface
of the patient.
In this paper, we extend the application of the earlier,
contact-based stiffness detection, to that of remote stiff-
ness detection. This was done using an ultrasound-based
sensor system that incorporates in the analysis of the re-
flected signal the phase-shift information. Our approach
differs from previously reported methodologies of stiffness
measurements, in that the object to be evaluated is not
compressed or vibrated , .
Furthermore, in this paper we provide an analysis of
the comparative differences between contact and remote
sensing, given that the principle of operation for both ap-
proaches is fundamentally based in the evaluation of fre-
quency shift. Particular emphasis is given in considera-
tions of the characteristics of the phase-shift circuit that
compensates for the phase shift of the sensor element by
adjusting the resonance frequency.
The approach used forthwith potentially fills the gap
in the determination of remote stiffness characterization,
given that echogenicity alone is not sufficient to define the
mechanical attributes of the reflecting object and is based
on the assumption that plane waves propagate and stand-
ing waves occur between target and sensor in the near
field. As such, the remote sensing method outlined may
find application in determining the biomechanical charac-
teristics of tissues and cells in which the transducer may
approximate, but not make contact.
II. Principle of Detection
The basic components of the ultrasonic system, illus-
trated by Fig. 1, consist of the sensor and the associated
circuitry with feedback. As shown in Fig. 1(a), the sen-
sor consists of two fused, disk-type piezoelectric (PZT)
ceramic transducers, made of lead zirconate titanate. One
face of the sensor is the ultrasonic transducer and the other
is designed to be used as the vibration pickup.
0885–3010/$20.00 c ? 2005 IEEE
440 ieee transactions on ultrasonics, ferroelectrics, and frequency control, vol. 52, no. 3, march 2005
Fig. 1. (a) Two fused, disk-type PZT, and (b) schematic diagram illustrating method for measurement.
A. Contact Sensing
Principle of contact sensing is based on the contact com-
pliance method  and phase-shift method . In previous
work , we showed that acoustic impedance measured by
these methods is highly correlated with stiffness. Briefly,
when an alternating voltage is applied across electrodes of
the driving PZT element, it is able to vibrate freely. The
PZT element transmits the longitudinal wave. If the fre-
quency of the vibration is equal to the inherent resonance
frequency of the sensor element, a standing wave occurs
within the sensor element. Then, the system is loaded when
the end of the sensor makes contact with the unknown
impedance Zx, at position l on its length. The change in
resonance frequency (∆f0) between the unloaded and the
loaded conditions can be written as:
where V0 is an equivalent velocity, and Z0 is an equiv-
alent impedance of the sensor. β is the reactance of the
impedance of the unknown object, Zx, which may be ex-
pressed in the form:
Zx= α + jβ,
where α is a resistance and j is standard complex number.
According to , β may be adequately written as:
β = ωmx−
10(1 − ν)·
S·1 − ν2
where ρ = density, ν = Poisson’s ratio, E = Young’s
modulus, S is the contact surface area, and = πr2with
r = radius of contact area. mx is the inertia term and
Cxis the surface compliance, so that the stiffness is kx=
1/Cx. As is shown in (4) and (5), the inertia term, mx,
can be expressed as the third power of the radius r, and
the stiffness kx, as the first power of r. Generally, because
the contact area of the tactile sensor is r < 1.0, the inertia
term, mx, will be larger than the stiffness, kxand may be
neglected. Then the change in resonance frequency caused
by the stiffness loading effect may be written as:
Accordingly, the resonance curve of the sensor shifts and,
consequently, the phase-difference curve as a function of
driving frequency also shift.
B. Remote Sensing
Basic principle of remote sensing is almost the same as
that of contact sensing except that ultrasound is transmit-
ted through water, substituting for a probe of the contact
sensing. The vibration pickup detects vibration of the ul-
trasonic transducer and sound pressure of standing wave
that arise between the sensor and the object. Phase dif-
ference between input (ultrasonic transducer) and output
(vibration pickup) alternative voltage is dependent on the
acoustic impedance of the object; and this phenomenon
can be explained using simple forced vibration resonance
model as follows:
dt+ k0· δ = Fv· sin(ωt) + Fs· sin(ωt + φ),
where r0 is the equivalent resistance of the equivalent
impedance of the sensor system. ω is a driving angular
frequency. Fv is the amplitude of driving force of the ul-
trasonic transducer and, namely, Fvsin(ωt) represents the
input voltage. Fs is the amplitude of the other driving
force and Fssin(ωt + φ) represents the sound pressure of
the standing wave just in front of the sensor. Fsis a func-
tion of an acoustic impedance of the object and a distance
between the sensor and the object. φ is a function of the
distance. Response of this resonance system δ can be easily
solved as follows:
δ = A · sin(ωt − θ),
murayama et al.: analysis of comparative differences in contact and remote sensing441
Fig. 2. Phase-shift circuit.
θ = tan−1r0ω −?k0− m0ω2?µ
Fv+ Fs· cosφ.
Consequently, Fswhich is a function of acoustic impedance
can be calculated by measuring phase difference θ at a
given angular frequency ω.
(k0− m0ω2) + r0ωµ,
C. Phase-Shift Method
The phase characteristics depend on the physical prop-
erties of loaded objects, affecting the resonant frequency
of a sensor. Because the value of the phase is very im-
portant, we developed a novel phase-shift circuit (Fig. 2).
Phase differences expressed in (10) can be measured with
high signal-to-noise ratio by applying this novel phase-shift
The vibration pickup detects the vibration and con-
verts it to an electrical voltage that is fed back to the
driving PZT element through an amplifier and a phase-
shift circuit. The phase shift of the alternating voltage
through the sensor element is represented at θ1. In the
same way, θ2 is the phase shift through the phase-shift
circuit. As the amplification is increased in this system,
the phase-shift circuit drives the sensor element at its res-
onance frequency (f0) when θ1+ θ2 = 0 (Fig. 3). If the
sensor is loaded, the resonance curve shifts, depending on
the acoustic impedance of the object. The sensor then res-
onates with the new frequency (fc = f0− ∆fc) where
θ1+ ∆θ1= −(θ2+ ∆θ2). Specifically, the circuit compen-
sates for the phase shift of the sensor element by adjusting
the resonance frequency. In such a system, because the
feedback system is operated as the velocity resonance sys-
tem by combining the mechanical vibration of the PZT
transducer and the resonance of an electrical circuit, the
signal-to-noise ratio of the sensor increases remarkably.
III. Experimental Setup
Evaluation of transducer characteristics was accom-
plished with a precision mechanical manipulator system
Fig. 3. Influence of load on phase/frequency characteristics. Reso-
nance frequency shifts following the phase-shift principle. Black line:
phase curve of the phase shift circuit, gray line: phase curve of the
using positioners and a testing platform whose basic con-
struction is illustrated by Fig. 1(b). Contact and remote
stiffness measurements were made within a tank of water
in which the object to be evaluated was totally immersed
together with the sensor system. As indicated by Fig. 1,
with this setup the sensor can be moved using the three-
dimensional (3-D) micromanipulator (CAT-ED, Chuo In-
dustrial Co., Ltd., Tokyo, Japan), the object remaining
Fig. 1(b) shows a schematic block diagram of the elec-
trical system used to guide the micromanipulator/sensor
assembly. The direction and speed of displacement pro-
duced is computer controlled via RS-232C and synchro-
nized with data collection. In addition Fig. 1(b) shows the
interconnections of the amplifier and feedback circuit for
the sensor together with the frequency counter. With this
circuit, changes in resonance frequency are measured with
the frequency counter (TR5822, Advantest Corporation,
Tokyo, Japan) and recorded in a computer via GP-IB.
Measurements were carried out with the ultrasonic sensor
oscillating at the resonance frequency of about 458 kHz.
C. Measurement Protocol
Measurements were carried out using iron, copper, alu-
minum, and silicon objects. Fig. 4(a) shows the setup used
to investigate the basic performance of the ultrasonic stiff-
ness sensor. Using this setup, the change in the resonance
frequency of the sensor was measured as a function of dis-
tance between sensor and object. The sensor was incre-
mentally moved away from the object over a distance of
0 to 7 mm, and the change in resonance frequency was
simultaneously recorded every 10 µm. The stiffness of the
four objects defined above was evaluated with the sensor
in contact with the object, and by remote sensing. Statis-
tical evaluation between ∆f and acoustic impedance for
442ieee transactions on ultrasonics, ferroelectrics, and frequency control, vol. 52, no. 3, march 2005
Fig. 4. (a) Measurement setup, and (b) basic performance of the
remote sensing was measured using the Pearson correla-
tion coefficient method.
IV. Experimental Results
A. Contact Sensing
The results obtained using the measurement setup
shown above is given by Fig. 4 for contact and remote
sensing of stiffness. As indicated, by Fig. 4(b), upon con-
tact, ∆f = −6786, −5892, −3541, and 825 Hz (at dis-
tance = 0.0 mm). The relationship between ∆f and the
standard and well-known acoustic impedance (ZA) for sil-
icon, aluminum, copper, and iron,  is given by Fig. 5.
As indicated, the linear regression thorough these points is:
∆f (Hz) = −5058·logZA(106·N·s·m−3)+2221. (Pearson’s
correlation coefficient; R = −0.99). Because contact usage
of the sensor is the same as that of a conventional tactile
sensor, as demonstrated on the basis of our previous mea-
surements  and analysis, it is assumed that a change in
resonance frequency and acoustic impedance has a linear
relationship. Therefore, these results show that the sen-
sor has the same characteristics with a tactile sensor, and
stiffness is linearly correlated with ∆f when the sensor is
in contact with the object.
B. Remote Sensing
Fig. 4(b) shows the change in resonance frequency at
distances from >0.0 to 7 mm, showing the relationship
between the ∆f and distance for iron, copper, aluminum,
Fig. 5. Calibration curve of the ultrasonic sensor in contact manner.
Fig. 6. Pearson’s correlation coefficient between change in resonance
frequency and acoustic impedance.
and silicon objects placed in the path of the beam. As in-
dicated, the resonance frequency of the sensor changes in
a manner that is consistent with a standing wave having
characteristics that are specific to the objects evaluated. In
particular, Fig. 4(b) shows that, at antinodes, the values
for ∆f are such that ∆fi > ∆fc > ∆fa ? ∆fs, repre-
senting iron, copper, aluminum, and silicon, respectively.
Notice that the sign of ∆fs is opposite from others.
Additionally, Fig. 4(b) shows that, at the nodes of the
standing wave, there is convergence for all objects tested
where ∆f = 0 at integral multiples of λ/4 (assuming a res-
onance frequency of 458 kHz, and a velocity of sound in wa-
ter is 1498 m/s). As indicated, the exception to this occurs
at boundary when object and sensor are within 0–2 mm.
Fig. 6 shows the variation in the correlation coefficient be-
tween resonance frequency and acoustic impedance at each
measurement location from the test object. As indicated,
there is a very high degree of correlation at distances corre-
sponding to the nodes illustrated earlier by Fig. 4(b). Lin-
ear relationship between resonance frequency and acous-
tic impedance was demonstrated in remote conditions. On
murayama et al.: analysis of comparative differences in contact and remote sensing443
Fig. 7. Distance dependent constant a (a), and b (b) in the calibration curve equation.
this basis, the calibration curve within the range of 0–7 mm
is given as:
∆f = a · logZA+ b.
Fig. 7 shows the variation in the numerical value of con-
stant a and b at each measurement location. These results
are the first evidence indicating that this sensor configura-
tion and analysis can be used to accurately determine the
stiffness of the test of objects used under the experimental
conditions of this study.
This work presents a method of evaluating the mechan-
ical properties of materials using an ultrasound-based re-
mote sensing approach. In the conventional tactile sensor
use, sensor information relating to the mechanical proper-
ties of materials is obtained by evaluating the change in
resonance frequency obtained upon contact after touching
the sensor to objects; the change in resonance frequency is
linearly related on, a semi-logarithmic, with the acoustic
impedance. In this experiment, the sensor we used is not
different in its basic composition at all so that the sensor
should have the same basic performance with a conven-
tional tactile sensor. Clearly, although the formula of phase
as a function of stiffness is practically useful, it should be
viewed at this stage as an approximate simplification be-
cause phase and signal frequency are related by an integral
equation. Thus, although the experimental results repre-
sent reasonably consistent values for the materials tested,
generalization of this approach on a theoretical basis can-
not be made. To establish the significance of these con-
siderations, it is essential that the consideration be made
of the density and Young’s modulus of the materials to
be tested. In considering remote testing, it also is appro-
priate to consider that, theoretically, this method makes
the assumption that plane waves propagate and stand-
ing waves occur between the target sample and disk PZT
transducer. Given that plane waves are generated in the
near field, these are both dependent on the frequency and
PZT transducer parameters. In view of these constraints
the geometry of the transducer needs to be specifically
designed to accommodate not only the geometry of the
object to be evaluated but also its surface characteristics.
Practically remote sensing of biologically derived target
tissues or cells may be scanned in the 3-D to optimize the
resolution of the near field and provide a stiffness image.
Furthermore stiffness imaging can be used to enhance con-
trast and identify tissues having different shear modulus
of elasticity, or remotely identify embedded materials.
The physical principles used by the present approach
can be extended to develop stiffness imaging, thereby
adding to conventional ultrasound a new parameter by
making possible the identification of the mechanical prop-
erties of materials. This is particularly important in medi-
cal imaging because it enables the differentiation of struc-
tures based on their hardness. However, low signal-to-noise
ratio in remote sensing, as compared to contact sensing,
requires improvement. Furthermore, in an attempt to use
this method practically with biological tissues, an impor-
tant training step is necessary. This could be accomplished
with an improved electrical design and with recently de-
veloped digital signal processing technologies.
In view of this background, the use of an ultrasound-
based methodology to measure the elasticity of cancer is
of particular importance, partly on the basis of the devel-
opment of sensor technology for cancer identification and
partly to better understand the impact of pathology on
the biomechanical properties of this tissue. As such, tis-
sue elasticity measurements using ultrasound may become
of interest in clinical diagnosis as its objective is to distin-
guish the state of health between the normal and patholog-
ical. In this context, the fundamental question arises as to
whether the observed nodular changes in elasticity are due
to the tissue architecture of the cancer in vivo or whether
they are an inherent quality of the individual cancer cells.
To answer this question, it is necessary to develop a means
of measuring the elastic properties of soft tissues. Experi-
mental studies are beginning to show that tissue elasticity
is modulated by hormonal manipulation at the organ level,
and emerging studies at the cellular level strongly suggest
that one of the early events of cancer onset is the collapse
of the cytoskeleton , . Furthermore, these obser-
vations emphasize that premalignant Pap smear cells will
stretch far more than their innocuous counterparts, and
444 ieee transactions on ultrasonics, ferroelectrics, and frequency control, vol. 52, no. 3, march 2005
they emphasize the additional importance of developing a
new means of evaluating tissue characteristics.
This approach has distinct advantages over X-ray, CT,
or conventional ultrasound imaging in that stiffness is a
physical parameter that is intuitively easy to comprehend
as an analogue to palpation using the finger. This is be-
cause, for an X-ray density of tissues varies by approxi-
mately a factor of 2–3, and for ultrasound the bulk mod-
ulus by a factor of 15–20 while the bulk modulus of elas-
ticity varies by many orders of magnitude . For this
reason technological development upon the principles of
stiffness imaging defined in the present study can enable
tissue characterization in a variety of medical applications.
 C. Kleesattel and G. M. L. Gladwell, “The contact-impedance
meter-1,” Ultrasonics, vol. 6, pp. 175–180, July 1968.
 S. Omata and Y. Terunuma, “New tactile sensor like the human
hand and its applications,” Sens. Actuators A, vol. 3, pp. 9–15,
 S. Omata and C. E. Constantinou, “Modeling of micturi-
tion characteristics based on prostatic stiffness modulation in-
duced using hormones and adrenergic antagonists,” IEEE Trans.
Biomed. Eng., vol. 42, no. 8, pp. 843–848, 1995.
 C. E. Constantinou and S. Omata, “Analysis of the relative
biomechanical effects of alpha 1 and alpha 2 antagonists in modi-
fying the compliance of the prostate and micturition parameters
of the hormonally manipulated male rat,” Neurour. Urodyn.,
vol. 15, no. 1, pp. 85–101, 1996.
 D. G. Hatzichristou, S. Omata, and C. E. Constantinou, “New
method for direct stiffness measurement of the corpora caver-
nosa,” Int. J. Impot. Res., vol. 7, pp. 221–231, Dec. 1995.
 J. Z. Lee, S. Omata, B. Tillig, I. Perkash, and C. E. Constanti-
nou, “Chronology and urodynamic characterization of micturi-
tion in neurohormonally induced experimental prostate growth
in the rat,” Neurourol. Urodyn., vol. 17, no. 1, pp. 55–69, 1998.
 O. Lindahl, S. Omata, and K. A.˚ Angquist, “A tactile sensor for
detection of physical properties of human skin in vivo,” J. Med.
Eng. Technol., vol. 22, pp. 147–153, 1998.
 T. Watanabe, S. Omata, J. Z. Lee, and C. E. Constantinou,
“Comparative analysis of bladder wall compliance based on cys-
tometry and biosensor measurements during the micturition cy-
cle of the rat,” Neurour. Urodyn., vol. 16, no. 6, pp. 567–581,
 J. Ophir, I. Cespedes, H. L. Ponnekantii, Y. Yazdi, and X. Li,
“Elastography: A quantitative method for imaging the elasticity
of biological tissues,” Ultrason. Imag., vol. 13, pp. 111–117, 1991.
 K. J. Parker and R. M. Lerner, “Sonoelasticity of organs: Sheer
waves ring a bell,” J. Ultrasound Med., vol. 11, pp. 387–392,
 N. Kaifu,Ed.National Astronomical
Rika Nenpyo (Chronological Scientific Tables). Tokyo, Japan:
Maruzen Co., Ltd., 2002. (in Japanese)
 J. Guck, R. Ananthakrishnan, H. Mahmood, T. J. Moon, C.
Cunningham, and K. Kas, “The optical stretcher: A novel laser
tool to micromanipulate cells,” Biophys. J., vol. 81, pp. 767–784,
 A. P. Sarvazyan, A. R. Skovoroda, S. Y. Emilianov, J. B.
Fowlkes, J. G. Pipe, R. S. Adler, R. B. Buxton, and P. L. Carson,
Biophysical Bases of Elasticity Imaging in Acoustical Imaging.
New York: Plenum, 1995, pp. 223–240.
 M. Sato, K. Nagayama, N. Kataoka, M. Sasaki, and K. Hane,
“Local mechanical properties measured by atomic force mi-
croscopy for cultured bovine endothelial cells exposed to shear
stress,” J. Biomechan., vol. 33, pp. 127–135, 2000.
Yoshinobu Murayama (M’04) was born in
Fukushima, Japan on June 16, 1976. He re-
ceived the B.E. and M.S. degrees in engi-
neering from The University of Osaka, Osaka,
Japan in 1999 and 2001, respectively. He is
currently a Research Assistant at the College
of Engineering, Nihon University, Fukushima,
Japan. His research interests are stiffness mea-
surement and related physiological activities
of living tissues.
He is a member of the IEEE, the Japan
Society of Mechanical Engineers, and the
Japanese Society for Medical and Biological Engineering.
born in Limassol, Cyprus, on July 21, 1939.
He received both his M.S. in Engineering Sci-
ence in 1968 and Ph.D. degree in Biomedi-
cal Engineering in 1973 from Stanford Uni-
versity, Stanford, CA. From 1977 to 1984 he
was Assistant Professor, and from 1984 to the
present, Associate Professor of Urology at the
Stanford University Medical School. His re-
search involves both the clinical and basic
sciences with interests involving the physio-
logical mechanisms of urine transport, phar-
macology, and imaging of the upper urinary tract where he con-
tributed to the understanding of the pacemaker system of the kid-
ney. His clinical contributions were concentrated in the development
of the biomedical engineering technology of urodynamics in evaluat-
ing and modeling urinary tract function. He is currently focused on
the development of novel technologies and biosensors for the clini-
cal evaluation of the biomechanical properties of tissues in general
and the pelvic floor in particular. He promoted many collaborative
projects within Europe and Japan having direct clinical application
of biomedical engineering technology. Dr. Constantinou is a member
of the Institute of Electrical Engineers, Physiological Society, Inter-
national Continence Society and American Urological Association.
Constantinou (M’69) was
Sadao Omata (M’90) was born in Fukushima,
Japan on October 16, 1947. He received the
B.E., M.S., and Ph.D. degrees in engineering
from Nihon University, Fukushima, Japan in
1972, 1974, and 1981, respectively. He was a
Lecturer and an Associate Professor at the
College of Engineering, Nihon University from
1979 to 1993 and 1993 to 1994, respectively.
Since 1994, he has been a Professor at the
same college. During this period, he was a Vis-
iting Research Associate at Umea University,
Medical School, Umea, Sweden, and Stanford
University, Medical School, Stanford, CA, in 1992 and 1993. Since
2000, he has concurrently been a president of the Towser Laboratory
Corporation Co., Ltd., Koriyama, Fukushima, Japan. His research
interests are in sensing technology for medical engineering, ultra-
sonic sensing technology, optics sensors, robotics, virtual reality, and
next generation medical diagnosis.
He is a member of the IEEE, the Japan Society of Mechanical En-
gineers, the Japanese Society for Medical and Biological Engineering,
and the Acoustical Society of Japan.