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Design, fabrication, and feasibility
analysis of a colorimetric detection
system with a smartphone for self-
monitoring blood glucose
Hung-Chih Wang
Fuh-Yu Chang
Tung-Meng Tsai
Chieh-Hsiao Chen
Yen-Yu Chen
Hung-Chih Wang, Fuh-Yu Chang, Tung-Meng Tsai, Chieh-Hsiao Chen, Yen-Yu Chen, “Design, fabrication,
and feasibility analysis of a colorimetric detection system with a smartphone for self-monitoring
blood glucose,”J. Biomed. Opt. 24(2), 027002 (2019), doi: 10.1117/1.JBO.24.2.027002.
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Design, fabrication, and feasibility analysis of
a colorimetric detection system with a smartphone
for self-monitoring blood glucose
Hung-Chih Wang,aFuh-Yu Chang,a,*Tung-Meng Tsai,bChieh-Hsiao Chen,b,c and Yen-Yu Chenb
aNational Taiwan University of Science and Technology, Department of Mechanical Engineering Taipei, Taiwan
biXensor Co. Ltd., Taipei, Taiwan
cChina Medical University and Beigang Hospital, Taichung, Taiwan
Abstract. Maintaining appropriate insulin levels is very important for diabetes patients. Effective monitoring of
blood glucose can aid in maintaining the body’s insulin level, and thus reduce disease severities, secondary
complications, and related mortalities. However, existing blood glucose measurement devices are inconvenient
to carry and involve complex procedures, reducing the willingness of diabetes patients to regularly measure
blood glucose. We aim to provide a rapid, convenient, and portable meter for diabetes patients. We introduce
an integrated blood glucose detection device (IBGDD) that has no electronic component and uses the optical
sensing module of a smartphone to inspect colorimetric blood strips. To demonstrate accuracy conformance of
the developed device to the ISO 15197:2013 standard for blood glucose measurement, 20 diabetes mellitus
patients used the IBGDD with smartphones to measure their blood glucose level. The measurement results
revealed an accuracy of 100%, completely satisfying the requirements of the ISO 15197:2013 standard.
Overall, our specially designed IBGDD with a smartphone could achieve high accuracy and convenient
usage for the measurement of blood glucose concentration. Furthermore, the device is highly portable and
simple to operate. This contributes toward achieving self-monitoring of blood glucose by diabetes patients and
improved mobile health in the future. ©The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.
Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JBO.24.2
.027002]
Keywords: blood glucose; colorimetric blood strip; diabetes; smartphone.
Paper 180557R received Sep. 30, 2018; accepted for publication Jan. 22, 2019; published online Feb. 21, 2019.
1 Introduction
Rapid development of the economy and subsequent changes in
lifestyle have led to the prevalence of diabetes, affecting over
382 million people in 2013. This number is expected to rise
to 592 million by 2035, which will impose a large and increas-
ing global health burden.1Some studies pointed out that
frequent monitoring of blood glucose changes and effective con-
trol of blood glucose levels can help reduce the incidence of
diabetic complications.2–8However, current devices for blood
glucose monitoring are inconvenient to carry or their testing pro-
cedures are complicated. Therefore, the willingness of patients
to detect blood glucose frequently is reduced. This could
seriously impact the health of diabetic patients with high blood
glucose levels.
In general, glucose oxidase (GOx), glucose dehydrogenase
(GDH), and hexokinase are used in existing glucose measure-
ment techniques, which are based on the principles of enzymatic
reactions.9Each enzyme has characteristic advantages and
limitations, including redox potentials, cofactors, turnover
rates, and selectivity for glucose.10 Glucose biosensors for self-
monitoring of blood glucose (SMBG) are usually based on the
two enzyme families, GOx and GDH. GOx is the standard
enzyme for biosensors. It is easy to obtain, cheap, can withstand
greater extremes of pH, ionic strength, and temperature, and
has a relatively higher selectivity for glucose than many other
enzymes. Thus, the manufacture of GOx involves less stringent
conditions than other enzymes.10,11 The majority of current
glucose biosensors are of the electrochemical type because of
their higher sensitivity, reproducibility, and easy maintenance.
Electrochemical sensors may be divided into potentiometric,
amperometric, or conductometric types.12–14 However, electro-
chemical sensors have two disadvantages: one is interferences
with nonspecific electroactive particles and the other is compli-
cations related to signal conversion.15,16
In the colorimetric method for SMBG, blood glucose test
strips with special enzymes are used to produce chemical
reactions with blood glucose, and changes in the glucose con-
centration are monitored by evaluating changes in the color of
the strips. The conventional colorimetric method employs visual
comparison to determine the value of blood glucose. Although
this method has the advantages of low cost without an additional
blood glucose meter, it has a serious drawback of low accuracy,
especially due to differences in the personal visual evaluation.
To overcome this issue, many researchers are currently attempt-
ing to apply image analysis software and hardware, such as
a computer with a scanner or camera, to analyze the color
value of blood glucose strips.17,18 These works provide a
means to improve the accuracy of detection, but the significant
and outstanding disadvantages lie in that they require additional
and unportable hardware and complex operation with nonuser-
friendly steps. To simplify the complex operation, smartphones
have been used in several studies to detect the concentration
*Address all correspondence to Fuh-Yu Chang, E-mail: fychang@mail.ntust
.edu.tw
Journal of Biomedical Optics 027002-1 February 2019 •Vol. 24(2)
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of whole blood glucose.19–22 However, in these experiments, the
measurement of the concentration of blood glucose was time
consuming. Therefore, the development of a portable device
with simple procedure is of high significance for regular SMBG.
In this study, a convenient and portable colorimetric SMBG
system is introduced. The system combines a designed inte-
grated blood glucose detection device (IBGDD) with an auto-
matic glucose concentration analysis software installed in a
smartphone. The IBGDD consists of a blood glucose test site
(BGTS), a disposable lancet, a cover and baseplate set, and a
light guide channel. The IBGDD is designed to have a minimum
feature size and can be connected to the smartphone easily. In
addition, the IBGDD does not have any electronic component
and does not require any extra-light source during the detection
of blood glucose concentration. Light from the smartphone’s
liquid crystal display (LCD) is reflected and guided to the
detection area, as the required light source for capturing and
analyzing color changes of blood glucose test strips. An optical
simulation is performed to guide the light channel for achieving
proper illuminance and uniform illumination on the BGTS. The
smartphone’s camera is used to capture the image of the strip,
and then the software installed in the smartphone analyzes the
image and provides accurate values of blood glucose concentra-
tion. For developing the automatic glucose concentration analy-
sis software, whole blood samples with 10 different blood
glucose concentrations, from 50 to 500 mg/dL, were collected
and a normalized algorithm and a line process were applied to
establish reference mainlines for calculating the blood glucose
concentration values. Finally, blood samples were collected
from 20 diabetes mellitus patients and measured for blood glu-
cose using the developed SMBG system with smartphone and
a standard biochemical blood glucose analyzer. The measured
results were compared and analyzed to confirm the accuracy and
stability of the proposed SMBG system.
2 Design and Simulation
2.1 IBGDD Design
The major components of the IBGDD are the BGTS, a cover and
baseplate set, a disposable lancet, and a light guide channel, as
shown in Fig. 1. In our study, the smartphone’s LCD was used
as the light source to capture the BGTS image. In order to guide
the light to the BGTS area efficiently, after the IBGDD 3-D
model was built using the computer-aided design software,
an optical simulation using Tracepro (Lambda Research
Corporation) was performed to find the optimal design for
the IBGDD, especially the reflector’s angle.
2.1.1 Blood glucose test site
The main function of the BGTS is to observe changes in
the color of test strip and measure the concentration of blood
glucose. It is divided into two components: a test strip and
a substrate. The substrate has a 2-mm hole, through which
changes in the color of test strip after reaction can be observed.
The diameter of the test strip is 2.5 mm and it is placed on the
substrate with the same center as the through hole. To effectively
observe and evaluate the color change in the observation area,
optical simulation was performed in this study to determine the
final design of the IBGDD with the optimal illuminance and
illumination uniformity of the BGTS.
2.1.2 Cover and baseplate set
The main functions of the cover and baseplate are to lead the
blood to the BGTS and block the ambient light source,
which affects the image signal of BGTS. A black-colored blood
guide hole made of hydrophilic acrylic plastic was designed on
the cover. The hydrophilic property can effectively assist to lead
the blood to the BGTS, and the black feature can completely
isolate the ambient light.
2.1.3 Light guide channel
Because the direction of LCD light emission is parallel to the
vertical direction of the BGTS, a light guide channel is required
to redirect the light on the BGTS. According to the law of
the light reflection theory, a reflect angle (RA) structure was
designed in the guide light channel. This RA has an important
effect on the brightness and illumination uniformity of the
BGTS. The simulation experiment was aimed at determining
an optimal RA of the light-guiding channel.
Fig. 1 Smartphone and IBGDD, comprising the BGTS, cover, baseplate, light guide channel, and
disposable lancet. The red arrows indicate the light path in the IBGDD.
Journal of Biomedical Optics 027002-2 February 2019 •Vol. 24(2)
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2.1.4 Disposable lancet
The disposable lancet is mostly used to obtain blood from the
fingers because it is currently the safest and easiest method. In
this study, a special and small disposable lancet was designed
such that it can work with our IBGDD and is convenient for
users to carry. The lancet functions according to a spring mecha-
nism, and the total size is only 6 mm in diameter and 37.9 mm in
length. When the lancet is pushed against a finger, a hidden
needle rapidly and gently pierces the finger. Then, the blood
flows from the finger around the fingertip, and it is absorbed
by the BGTS through capillary action when the finger touches
the blood guide hole on the cover.
2.2 Simulation and Measurement Method for
Achieving Uniform Illumination of
the BGTS Area
The light source plays an important role in image analysis. To
find an optimized RA that can guide light into the BGTS area, in
this simulation experiment, the RA of the light guide channel
was set from 30 deg to 70 deg with 5 deg intervals as the analysis
conditions. In this manner, the most optimal angle for the best
illuminance and illumination uniformity on the observation area
of the BGTS could be determined. The actual illumination of
the smartphone’s LCD was measured to be the light source
illuminance value in the simulation. First, the screen was set to
be white, and the screen brightness was raised to the maximum,
and then a T10 illuminance meter (Konica Minolta, Japan) was
used to measure the actual illumination of the screen. The
average illumination is 230 lux. According to this measurement,
the light source area was set to be 7.5 mm ×6.3 mm in the
simulation. The light wavelength was set by the general colori-
metric method, in which the 550- nm wavelength is the most
commonly used. The number of simulated traces was set to
be 3 million.
The nine-point uniformity method was used to analyze the
method of achieving uniform illumination in this study. The
BGTS observation area was divided into nine symmetrical sam-
pling areas, P1 to P9, as shown in Fig. 2. The sampling square
size was 0.4 mm ×0.4 mm. The nine-area uniformity is defined
by the minimum illuminance measured at the nine sampling
areas divided by the maximum illuminance measured at the
nine sampling squares, as
EQ-TARGET;temp:intralink-;e001;326;609U¼MinðP1∼P9Þ
MaxðP1∼P9Þ;(1)
where Uis the BGTS uniformity.
2.3 Simulation Result
Our analysis of the simulation result indicates that the angle of
reflection of the light guide channel has a significant effect on
the illuminance and illumination uniformity of the BGTS obser-
vation area. At an angle of 30 deg, the illuminance and illumi-
nation uniformity are 50.43 lux and 90.43%, respectively. When
the angle increases, both illuminance and illumination uniform-
ity continuously increase. At 50 deg, the illuminance and illu-
mination uniformity reach the highest values of 53.67 lux and
95.47%, respectively. With continued increase in the reflection
angle, the illumination uniformity begins to decline. When the
Fig. 2 Illuminance analysis of the BGTS area using the optical simulation program TracePro, with the
BGTS area divided into nine symmetrical squares with sizes of 0.4mm ×0.4mm.
Journal of Biomedical Optics 027002-3 February 2019 •Vol. 24(2)
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angle increases to 70 deg, the illumination uniformity declines
to 90.68%, as shown in Fig. 3. It is concluded that when the RA
of the light guide channel is too low, it cannot effectively reflect
the light to the BGTS, and therefore both illuminance and illu-
mination uniformity are low. However, when the angle is higher
than 50 deg, the light intensity gradually decreases toward the
BGTS observation area, and therefore the illumination and illu-
minance uniformity of the observed area will decline. According
to the simulation, the final design of RA of light guide channel
was selected to be 50 deg to achieve the optimal illuminance and
illumination uniformity.
3 Experiment
3.1 Fabrication and Integration of the IBGDD
All IBGDD components were fabricated by plastic injection
molding, because the process allows components with consis-
tent characteristics at reasonable production cost. After fabrica-
tion, all components were cleaned by ultrasonic cleaning and
then assembled together. The mold of the reflector channel in
the reflective area was polished, which can ensure a mirror-like
surface roughness and enhance the light reflection efficiency of
the reflector.
In this experiment, generally marketed products of colori-
metric blood glucose strip were selected as the BGTS material,
which had concentrations ranging from 40 to 500 mg/dL. Since
general strip dimension cannot meet IBGDD size a knife tool
was used to cut the strip to the size 7.5 mm ×6.3 mm.
3.2 Verification of the Illuminance and Illumination
Uniformity of the BGTS Area
The illuminance measuring instrument used is Konica Minolta
T10 illuminance meter. In order to verify the basic illuminance
and illumination uniformity, a standard card of 4 mm diameter
was used. The standard card has Pantone White and completely
uniform color characteristics. After inserting the standard card
into the IBGDD, the glucose measurement program was started
and the smartphone’s LCD was switched on and guided to the
BGTS area by the light guide channel, after which white balance
locking and ISO value adjustment were performed. The program
divided the BGTS area image into nine symmetrical sampling
areas of square frame with four pixels each, and red, green, and
blue (RGB) data from each rectangle image were collected to
measure the minimum and maximum values. The measured
data with the standard card were used to adjust the glucose
measurement program for setting the basic illuminance and illu-
mination uniformity. This operation was repeated five times to
ensure the repeatability of the measurement.
3.3 Preparation of Blood Glucose Samples with
Different Concentrations
In this experiment, a Yellow Springs Instrument (YSI) bio-
chemical blood glucose analyzer equipment (YSI-2300) was
used to establish the different concentrations of blood glucose
from the collected whole blood samples.
Sterile heparin tubes were used to collect 120 c.c. venous
blood samples from each patient at environmental temperature
of 22°C. After 24 h of standing, the blood glucose of
the samples degraded to 0 mg/dL, and then proper amounts of
glucose solutions were added to the samples without blood
glucose according to the required blood glucose concentration.
After complete mixing with a rotor equipment, the YSI-2300
blood glucose meter was used to measure the blood glucose
concentration.
3.4 Colorimetric Enzyme Reaction Analysis and
Establishing the Signal Reference Mainline
The color of the colorimetric enzyme strip after reaction may
vary with different blood glucose concentrations and reaction
time. The first target of this experiment was to determine the
signal of RGB specific and sensitive to the colorimetric enzyme
strip and the time required to clearly distinguish the blood glu-
cose concentration value of the samples. To avoid variations of
blood glucose concentration caused by differences in tempera-
ture, the environmental temperature was set to 22°C. After
inserting the strip into the IBGDD, the glucose measurement
program was started and the image of the BGTS area was cap-
tured by the smartphone’s front camera (iPhone 5s, Apple Inc.)
and the digital image of color change was separated into R, G,
and B signals using the ColorAssist (FTLapps, Inc.) app
according to the blood glucose concentration and reaction time.
The concentration was adjusted from 50 to 500 mg/dL with an
Fig. 3 Simulation results of illuminance and illuminance uniformity.
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interval of 50 mg/dL. The experiment was repeated three times
for each of the 10 different blood samples. Subsequently, the
normalized algorithm was applied to obtain 10 different concen-
tration curves of RGB signals, which could assist in determining
the signal most suitable for the colorimetric strip, and the deter-
mined signal was used to establish linear equations with the
parametric regression analysis method. Normalization was per-
formed by dividing the reaction signal value by the unreacted
signal. Given that the normalized signal values for each concen-
tration were too low so as to easily distinguish them, the range of
the normalized signal values for each concentration was rescaled
up to 400 times. Finally, the linear equations were used as the
signal reference mainline to convert the imaged strip colors to
the blood glucose concentration values.
3.5 Verification Plan for the Developed SMBG
System
In 2013, ISO published a new standard ISO 15197:2013 and
tightened the accuracy acceptance criteria.23 The standard stip-
ulates that to satisfy the minimum acceptable accuracy for a glu-
cose monitoring system, 95% of measurement results must fall
within 15 mg∕dL of the reference measured glucose concen-
tration < 100 mg∕dL or within 15% of the reference measured
glucose concentration ≥100 mg∕dL. To verify the accuracy of
the new SMBG system within the criteria of ISO 15197:2013,
a verification test was performed with venous blood from
20 diabetic patients. The diabetic patients in this verification
plan covered diabetes types 1 and 2. The collected venous blood
specimens were measured not only by the SMBG system but
also by the YSI-2300 analyzer for accuracy comparison.
4 Results and Discussion
4.1 Illuminance and Illuminance Uniformity of
the BGTS Area
The T10 illuminance meter was used to measure the illuminance
of the BGTS area and the average illuminance was 54.6 lux from
five times of measurement. The nine-point uniformity method
was used to measure and analyze the RGB signals with the stan-
dard card in the BGTS area. The measured uniformity values for
RGB signals from five times of measurement were all higher
than 94.5%. The values of G signal uniformity were signifi-
cantly higher than those of the R and B signals, with an average
value of 97.4%. The coefficient of variation (Cv) value of
the RGB signals, ratio of the standard deviation to the mean
value, was lower than 0.82%. The Cv of the G signal reached
as low as 0.27%. This indicates that the designed reflector angle
and reflection channel can provide uniform and stable light to
the BGTS area. Uniformity and illuminance stability of the light
source are extremely important factors affecting the accuracy
and stability of the subsequent blood glucose measurement.
The measured illuminance and uniformity were also com-
pared with the simulation results, and the comparison results
proved the reliability of the simulation model. The simulation
model will be used to improve the SMBG design in the future.
4.2 Selection of the RGB Signal and Reference
Mainline
The developed SMBG system was applied to record the RGB
signal values every 0.2 s for 50 s, and each concentration sample
was measured three times. The normalized algorithm was
applied and 10 different concentration curves of RGB were
obtained, as shown in Fig. 4. The figures show that the normal-
ized curve of the R signal cannot be distinguished when the
concentration is over 300 mg/dL [Fig. 4(a)], and the normalized
curve of the B signal requires more than 30 s to distinguish
each concentration curve [Fig. 4(b)]. In contrast, the 10 different
concentration curves of the G signal could be distinguished
completely after 10 s, as shown in Fig. 4(b). Therefore, the
normalized curves of the G signal were selected to establish
the signal reference mainline, and normalized curve values of
each concentration at 10 s were collected. Subsequently, the
parametric regression analysis method was used to obtain
a linear equation y¼−0.3247xþ219.25, and the coefficient
of determination R2was higher than 0.98, as shown in
Fig. 5. The linear equation was used as the reference mainline
to convert the imaged strip colors to the blood glucose
concentration.
Fig. 4 Normalized curve from the measurement of 10 different con-
centration blood samples over 50 s. Each curve represents the aver-
age of three measurements. (a) The R signal cannot be distinguished
when the concentration reaches over 300 mg/dL. (b) The 10 different
concentration curves of the G signal can be clearly separated after
10 s. (c) More than 30 s is required to distinguish the concentration
curves of the B signal.
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4.3 Accuracy and Stability Tests for the Signal
Reference Mainline
To verify the accuracy and stability of the signal reference main-
line, six different blood samples with glucose concentrations
adjusted at 50, 100, 200, 300, 400, and 500 mg/dL were tested
and each sample was measured 10 times. The mean values of the
samples from 10 times of measurement with the developed
SMBG system were 49.0, 99.0, 198.3, 292.6, 397.0, and
488.7 md/dL, respectively; their standard deviations were 1.2,
2.9, 5.0, 9.6, 13.1, and 15.4, respectively; and their Cv values
were 2.4%, 2.9%, 2.5%, 3.3%, 3.3%, and 3.1%, respectively.
Compared with the adjusted glucose concentration of the sam-
ples, the accuracy of the measurements with the developed
SMBG system was found to be 97.4%, 95.9%, 95.7%,
98.3%, 97.4%, and 95.8%, respectively. As the experimental
results show accuracy values over 95% for all tested samples
and all Cv values under 3.8%, the developed SMBG system
with the established signal reference mainlines meet the stan-
dard criteria and clinical trial requirement for measuring blood
glucose concentration with high accuracy and stability. The
seven samples were also measured by the YSI-2300 analyzer
for comparison. The concentration values were 50.4, 103.3,
207.3, 297.6, 407.8, and 510.2 mg/dL, respectively. Comparing
these values, the developed system may measure the blood
glucose concentration with the same level of accuracy and
stability as the gold standard equipment.
4.4 Verification Result for the Developed SMBG
System
In the verification with venous blood collected from 20 diabetes
patients, 4 were diabetes type 1 patients and 16 were diabetes
type 2 patients. The age distribution is as follows: 5% under
the age of 20, 15% between 21 and 30, 20% between 31 and
40, 25% between 51 and 60, 10% between 61 and 70, and
5% over the age of 71. According to the analysis of data from
the 20 patients, measurement results of the developed SMBG
system and YSI-2300 analyzer were compared, and the data
were found to completely satisfy the 15 mg∕dL or 15%
criteria, as shown in Fig. 5. The accuracy acceptance percentage
is 100% and meets the ISO 15197:2013 (E) criteria successfully.
The parametric regression analysis of the data revealed a
coefficient of determination (R2) value of 0.9848, as shown
in Fig. 6(b). These analyses show that the developed system
measured the blood glucose concentration with the same
level of accuracy and stability as the gold standard equipment.
The design of the experiment and human subject involvement
were approved by China Medical University Hospital.
5 Conclusions
In this study, a convenient and portable self-monitoring blood
glucose system, comprising a special IBGDD and a smartphone,
was developed and tested. As no additional light source and
electronic part are required in the IBGDD, the device is as
small as a pen cap or a key chain. An optical simulation was
performed to optimize the design of the IBGDD in order to
guide the light from the smartphone’s LCD to the colorimetric
strip efficiently. The smartphone’s camera was used to capture
the image of the colorimetric strip, and then the developed
blood glucose concentration analysis program, installed in the
smartphone, was used to obtain the measured value of blood
glucose concentration. To confirm the accuracy and stability
of the developed system, data from blood samples of 20 diabetes
patients were used to measure their blood glucose and the
results were compared with measured data from a commercial
YSI-2300 blood glucose analyzer. The analysis results showed
that all measured data were within the 15 mg∕dL or 15%
Fig. 6 ISO 15197:2013 and parametric regression analysis results. (a) Comparison of the measured
data from the developed SMBG system and the YSI-2300 analyzer for 20 samples. The plot also
gives the superimposed tolerance bands according to the ISO 15197:2013 criteria for accuracy.
(b) Parametric regression analysis plot comparing results for the SMBG and YSI-2300 analyzer.
Fig. 5 Signal reference mainline obtained based on the normalized
value of the G signal with the parametric regression analysis method.
Glucose concentrations of 50 ∼500 mg∕dL were recorded with the
IBGDD and G signal and the results were read with a smartphone.
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criteria. The accuracy acceptance percentage was 100%. The
results prove that the developed system can completely meet
the requirements of the ISO 15197:2013 standard for blood
glucose measurement.
In the next phase plan, a clinical trial will be conducted with a
hospital. It will enroll 120 subjects (type 1 and type 2 diabetes
patients). These subjects will use the IBGDD with smartphones
to measure their blood glucose levels. The measured data will be
compared with those of the YSI-2300 analyzer to verify whether
the clinical trial results meet the ISO 15197:2013 accuracy
requirements.
In the future, our goal is to design a personal health manage-
ment system that combines glycosylated hemoglobin (HbA1C),
cholesterol, triglyceride, and blood glucose data. The personal
health system data will be used to analyze physiological infor-
mation, which will be sent to the cloud system of medical insti-
tutions via the internet. In this manner, concerned physicians can
quickly grasp and judge the health information and transmit the
related medical information to the patient. Through this
personal health management system, patients can effectively
employ digital technology to achieve physical health.
Disclosures
T. M. Tsai, C. H. Chen, and Y. Y. Chen are cofounders of
iXensor Co., Ltd., and H. C. Wang was an employee of
iXensor Co., Ltd. F. Y. Chang has no relevant conflict of inter-
ests to declare.
Acknowledgments
This research was supported by iXensor Co., Ltd., and approved
by IRB of China Medical University Hospital (CMUH102-
REC2-050). Ministry of Science and Technology, Taiwan,
Republic of China under Grant MOST 106-2221-E-011-
071-MY2.
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Hung-Chih Wang is a PhD candidate at the National Taiwan
University Science and Technology, Taiwan. He received his BS
and MS degrees in mechanical engineering from the National
Taiwan University Science and Technology, Taiwan. He is experi-
enced in mechanical product design, optical and biomedical device
development for over 20 years.
Fuh-Yu Chang received his PhD in mechanical engineering, Leeds
University, United Kingdom. Currently, he is working as an associate
professor in the Mechanical Engineering Department of National
Taiwan University Science and Technology, Taiwan. He has over
10 years of experience in biomedical device development. He is
also interested in the semiconductor industry, electronics, and related
optical design.
Tung-Meng Tsai received his PhD in chemistry engineering from the
National Chung Hsing University, Taiwan. Currently, he is working in
iXensor as chief executive officer and cofounder. He has more than
20 years of experience in biomedical device development.
Chieh-Hsiao Chen received his MD degree from KMU and PhD in
biomedical engineering from NCKU, Taiwan. He specializes in
nanotechnologies for cancer treatment, biosignal processing, and
entrepreneurship. He works at iXensor as chief medical officer
and cofounder. He also participates in clinical strategy, algorithms,
and user experiences. He teaches biodesign and entrepreneurship
at CMU, KMU, and NCKU. He is also the director of urology at
CMUH, Beigang, and CEO of Brain Navi Ltd.
Yen-Yu Chen received his PhD in electrical engineering from
National Taiwan University, Taiwan. Afterward, he worked as a visit-
ing researcher at Massachusetts Institute of Technology, California
Institute of Technology, and Stanford University, to develop advanced
optical imaging systems for biomedical research. He is now the CTO
of iXensor and leads the company’s technology and strategy develop-
ment. His fields of interest include medical devices, optical instru-
ments, and digital health.
Journal of Biomedical Optics 027002-7 February 2019 •Vol. 24(2)
Wang et al.: Design, fabrication, and feasibility analysis of a colorimetric detection system with a smartphone for self-monitoring blood glucose
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