FACULTY OF MEDICINE AND HEALTH SCIENCES
DEPARTMENT OF BIOMEDICAL INFORMATICS AND
BIOMEDICAL ENGINEERING
GROUP MEMBERS
Surname
Name
Registration number
CHATIZA
MUTAMBARA
RUPIYA
RUTENDO
VANESSA
RAYMOND
R203205V
R206941V
R206727P
PROJECT TITLE
DESIGN A LONGLASTING INTERNAL GLUCOMETER THAT
CONTINUOUSLY MEASURES AND MONITOR BLOOD GLUCOSE LEVEL.
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Table of Contents
ABBREVIATIONS LIST ..................................................................................................................................... 3
Section 1: INTRODUCTION ............................................................................................................................ 4
1.1: BACKGROUND ........................................................................................................................................ 5
1.1.2: Implantable Sensors............................................................................................................................ 9
1.2: PROBLEM STATEMENT ...................................................................................................................... 10
1.3: AIM .................................................................................................................................................... 10
1.4: OBJECTIVES ........................................................................................................................................ 10
1.5:
JUSTIFICATION .................................................................................................................................. 10
Section 2: LITERATURE REVIEW ................................................................................................................. 16
2.1:
INTRODUCTION ................................................................................................................................ 16
2.2: BLOOD GLUCOSE MEASURING ............................................................................................................ 16
2.2.1: Subcutaneous Continuous Blood Glucose Monitoring ..................................................................... 17
Section 2.3:
DISEASES RELATED TO USE OF BGL DEVICES ....................................................................... 18
2.4: CURRENT BGL MONITORING DEVICES ................................................................................................. 18
2.4.1:The Dexcom 6 CGM ........................................................................................................................... 18
2.4.2: Abbott Freestyle Libre 2 System ...................................................................................................... 19
2.4.3: Medtronic Guardian 3 CGM System ................................................................................................ 20
2.4.4: Eversense ........................................................................................................................................ 20
2.5: ARTIFICIAL INTELLIGENCE AND ROBOTICS USED IN BLOOD GLUCOSE MEASURING........................... 21
Section2.6: CASE STUDIES ........................................................................................................................... 25
2.7: RECOMMENDED GADGETS BY W.H.O ................................................................................................ 29
Section 3: Materials and Methodology....................................................................................................... 29
3.1 Introduction .......................................................................................................................................... 29
3.2 Materials ....................................................................................................................................... 30
4.0 Results and discussion .......................................................................................................................... 34
4.1 The Setup .......................................................................................................................................... 34
REFERENCE LIST .......................................................................................................................................... 36
2
ABBREVIATIONS LIST
BGL – Blood Glucose Level
CGMS – Continuous Glucose Monitoring System
EVS – Eversence
ISF – Interstitial Fluid
AI – Artificial Intelligence
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Section 1: INTRODUCTION
Diabetes is a chronic health condition that affects how the body turns food into energy. Most of
the food that is taken in is broken into sugar and is then released into the bloodstream. When the
blood sugar goes up, it signals the pancreas to release insulin. According to the World Health
Organization (WHO,2020), Diabetes Mellitus, is a chronic metabolic disease that presents itself
as an increased blood glucose level (blood sugar ) above the normal range. This disease can result
in serious damage of major organs such as the heart, blood vessels, the eyes, the kidneys and the
nerves after a prolonged period of time especially without treatment. These complications often
share similar risk factors, and one complication might worsen others. People with diabetes often
also have high blood pressure which worsens eyes and can lead to kidney diseases. Diabetes can
lower High Density Lipoprotein (HDL) cholesterol and raise triglycerides as well as Low-density
Lipoprotein cholesterol. These changes can increase the risk for heart disease and stroke. Damage
to blood vessels and nerves can also lead to amputation of affected limbs. If someone is diabetic,
his/her body does not make enough insulin or is unable to use the insulin it makes as well as it
should. When cells stop responding to insulin, too much blood sugar stays in the bloodstream. It
has two occurrences type 1 and type 2, the latter being the most common. Type 1 diabetes
previously known as juvenile or insulin dependent diabetes is an autoimmune disease that results
in the pancreas’s inability to produce sufficient levels of insulin. The insulin producing beta cells
in the pancreas are attached and destroyed by the immune system. The symptoms of type 1 often
develop quickly and it is usually diagnosed in children and also young adults. Type 1 patients have
to take in insulin every day. Type 2 results when the body becomes resistant to insulin, sugar builds
up in the body and the body is unable to keep blood sugar at normal levels. It is usually diagnosed
in adults but can occur at any age. It can be prevented or delayed with health lifestyle changes,
such as eating health food, being active and losing weight. Blood Glucose Level –B.G.L (GLYCAEMIA) is the concentration of glucose within blood. The normal blood glucose level for
non-diabetics is in the range 3.9 to 7.1 mmol/L (70 to 130 mg/dL). The global average fasting
plasma blood glucose level is around 5.5 mmol/L (100 mg/dL), (Roe.J, 2014). Hypoglycemia (low
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blood sugar ) and hyperglycemia (high blood sugar ) can lead to issues such as excess sweating,
excessive hunger, fainting, fatigue, gastrointestinal problems like nausea, cognitive problems like
confusion or unresponsiveness and can also result in a coma. Monitoring blood sugar is done by
the central nervous system (CNS) namely the Hypothalamus and brain stem. The two types of cells
in the hypothalamus: Glucose excited (GE - increases glucose concentration) and Glucose
inhibited (GI- decreases glucose concentration) are key in glucose homeostasis, (Raman, 2017).
This is an automatic process in the body however for diabetics they need to manually monitor their
blood sugar by use of monitors. A glucose meter (glucometer), is a medical device that
approximates the concentration of glucose in the blood. (Merriam-Webster, 2021). There are two
types of monitors that present the traditional glucometer and recently there have been
developments of non-invasive continuous glucose meters. The most common glucose meter
requires users to prick their finger to draw blood used to measure the BGL however this can only
be done a handful of times per day which is barely enough to keep up with the constant fluctuations
in BGL. However there are continuous glucose monitoring devices (CGMs). These CGMs are
quite expensive and need constant replacement of sensors. The goal of the proposal is to present a
design of an improved glucometer. The glucose meter will be in the form of an implant that can
ideally last for a long period of time without any complications, an external transmitter that can
pick up data from the implant in 5 minute intervals continuously and a mobile application that is
able to record, display, graph and analyze the data from the sensor while also being able to alert
user if their blood sugar has spiked or dipped. This version will also be able to allow patients to
limit visits to the clinic in a post COVID-19 era and allow physicians and nurses to keep up with
then health statuses of their patients virtually. Diabetics will also be able to have an artificial
autonomic nervous system allowing them to live a more normal life with lower risk of
complications and less anxiety.
1.1: BACKGROUND
1.1.1 History of Glucometers
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Fig. 1.1 Evolution of glucometers
The earliest glucose monitoring system was invented in1500 B.C where ants were used to check
glucose level in blood. In the mid-19th century urine was used to quantify glucose level which is
the foundation of modern diabetes care. In 1908 Benedict developed a copper reagent that was
used for urine testing and was used for more than 50 years until the Clinitest which used heating
was developed in 1945 with a modified copper reagent. Glucose was oxidized, and the amount of
glycosuria was proportional to the color of the heated solution. In 1965 Ames developed the first
glucose Test strips, the Dextrostix using glucose oxidase. This worked in such a way that a large
drop of blood was placed on the strip and, after 60 seconds it was washed away. The generated
color was then compared to a chart on the bottle for a semi-quantitative assessment of blood
glucose.
The first blood glucose meter was developed by Anton Clemens at the Ames Research Division,
Miles Laboratories, in Elkhart, Indiana, USA.
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Fig, 1.2 the first glucometer
It was used in the 1970s with the Dextrostix, but its precision and accuracy were poor and also it
was not for home use. By 1980, the Dextrometer was created, the meter used the Dextrostix and
had a digital display. During the 1980s, meters and strips requiring less blood became available,
all at a cheaper price. From the late 80s through to the early 2000s the technology improved. The
blood removal step was removed, electrochemical strips were developed, smaller amounts of blood
were needed, wider ranges of hematocrit were allowed and new enzymatic tests were used. The
Lancets were also optimized. By 2010 this glucometer was relatively painless and patients were
encouraged to use them. More developments were later made which included glucose meters that
required less blood and had digital displays that were affordable and also electrochemical strips
came into use. At this time around the Process of Self-monitoring of blood glucose levels became
standard of care. The introduction of the Continuous glucose monitoring systems was then
introduced around the year 2004. However nowadays current models of glucometers are more
efficient and easier to use compared to the first ones exist.
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Fig 1.3 Continuous glucose monitoring models
The working principle of these is such that an individual has to insert a test strip into the meter and
then prick the side of a fingertip with the needle (lancet) provided with the test kit and then he or
she has to touch and hold the edge of the test strip to the drop of blood. The meter will then display
the blood sugar level on a screen after a few seconds.
Current models
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Fig. 1.4 Current BGL monitoring devices
1.1.2: Implantable Sensors
Eversense (EVS) is the only implantable sensor available at the present moment. The sensor is
inserted in the subcutaneous tissue of the upper arm by medical experts through a small incision
and using a special dissector. Implantable sensors are key components of CGMSs. They are
implanted under the skin where they are inserted in the subcutaneous tissue of the upper arm by
medical experts through a small incision and using a special dissector and continuously monitor
subcutaneous interstitial fluid (ISF) glucose and they use the invasive concept. The implantable
glucometer set consists of an implantable fluorescence based sensor, a removable external
transmitter and a mobile medical application that displays glucose data. The transmitter, sitting
over the sensor and secured to the skin with an adhesive, stores glucose data and transfers the data
via an application using IoT to the patient’s smartphone that displays glucose values, trends and
alerts.
Fig. 1.5: An implantable continuous glucose monitor using IoT
The system alerts the subject during rapid glucose changes and when glucose values exceed or
were predicted to exceed a selected threshold. In the absence of a smartphone, subjects could be
alerted by the transmitter through a vibration.
The implantable device must be biocompatible. Biocompatibility of invasive sensors results in less
sensor failure, long lifespan, high accuracy and good utility. Implanted sensors can reduce skin
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irritation, tissue damage and severe foreign body reaction. Highly biocompatible CGM sensors
lower critical barriers to CGMS use. To increase biocompatibility, the shape, size, material, texture
and properties of bio-interface of CGM sensors can be optimized and they determine the degree
of biocompatibility, for example the small sized devices give less damage to the subcutaneous
tissues and reduces the foreign body response.
1.2: PROBLEM STATEMENT
Diabetic patients are finding it very difficult to monitor their blood glucose levels using CGMs as
they face challenges such as pain and discomfort, sensor change requirements/impermanence of
the instruments used, irritations of the skin and cost.
1.3: AIM
To come up with an implantable glucometer that is inserted in the subcutaneous to monitor BGL,
lasts longer that the current ones and cost less.
1.4: OBJECTIVES
a) Design an intelligent implant that is inserted in the subcutaneous layer for monitoring BGL
and which lasts for 9months.
b) The implant should be 18.3mm in length and 3.5mm in diameter to avoid injury and
discomfort to the patient.
c) It should be made locally with available materials and cost less
1.5:
JUSTIFICATION
Diabetes Mellitus is a great issue that is affecting the planet. A statement from the World Health
Organization concerning diabetes follows that, “About 422 million people worldwide have
diabetes, the majority living in low-and middle-income countries, and 1.5 million deaths are
directly attributed to diabetes each year. Both the number of cases and the prevalence of diabetes
have been steadily increasing over the past few decades.”Diabetes is one of the most common
lifestyle diseases in the world.
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Fig. 1.6 Global distribution of diabetes
In the SADC region due to the changing lifestyles and standards of living the disease landscape
is changing. The WHO has stated that, “In African nations, non-communicable diseases are
rising rapidly and are projected to exceed Communicable, maternal, perinatal, and nutritional
diseases as the most common causes of death by 2030”. Diabetes is becoming a greater
problem in the Southern Africa with our neighboring country South Africa being in the top 5
countries affected by diabetes in the International Diabetes Federation (IDF).
The President of the Diabetic Association of Zimbabwe, Mrs. Tendai Gutu, in an interview
stated that, “some people suffer from the disease (diabetes) unknowingly or in silence at home,
hence the need for increased advocacy, awareness, testing and information to the people. With
an increased prevalence of diabetes as a disease in Zimbabwe, there are increased
complications like kidney failure, blindness and amputations with an increased mortality”.
The statistics in this country show that an estimated 10 out every 100 people have diabetes,
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with these statistics currently representing over 100 000 visits or consultations at outpatients
departments per year. Mrs Gutu also encouraged diabetic patients to control their blood sugar
levels.
Fig. 1.7 Diabetes percentage in Zimbabwe
These numbers show that on a daily basis a significant portion of our population has to live
with diabetes and maintain their blood sugar levels.
On top of this the majority of diabetes patients are part of the older population. Type 2 diabetes
is more common in adults’ especially geriatric adults. The geriatric population is usually
dependant on assistive care for example people with Parkinson’s disease would not be able to
take their own blood glucose by pricking their finger and if they do so there are high chances
of getting false results. Finger pricking method is not an efficient method for children to
measure their BGL for themselves so the use of an implantable glucometer is better and more
efficient. Disabled people without upper limbs are not able to use finger pricking method so
they need an implantable glucometer to monitor their BGL. The use of implants avoids long
queues at hospitals and clinics.
In addition the cost of living with diabetes is very high in this nation. According to several
researches that were made, there is increasing dependence on foreign donors for the majority
of drug supply in the country. Around 90% of donor expenditure is used for communicable
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diseases and reproductive health and family planning programs. In Sub-Saharan Africa,
Zimbabwe has the third highest total cost of diabetes care per year for persons aged 20–79
years, after South Africa and Kenya. “Between 2001 and 2010 in Zimbabwe, the share of
household out-of-pocket cost consumed by health expenditure in Zimbabwe increased from
36% to 52%, which is significantly higher than other countries in SSA” as written in this
journal. Poor treatment and management of diabetes lead to intensified health care utilization
and increased medical care costs. Medical expenditure also comes from indirect treatment
through treatment of complications such as cardiovascular (CVD), cerebrovascular (CVA),
and renal diseases. Chronic complications require expensive medical procedures, such as limb
amputation or renal dialysis, and significantly increase hospitalization costs and length stay.
The tables below shows data from the study:
Patient
T2DM diagnosis
Hypertension diagnosis
characteristics
Discharged
Died
Discharged
Died
Mean cost
Mean
Mean cost
Mean
(95% CI)
cost
(95% CI)
cost
(95% CI)
(%
(% died)
discharged)
(95% CI)
(%
(% died)
discharged)
Women
<65 years
of age
1467 (1177,
1308
1124 (961,
1002 (813,
1828)
(1011,
1314)
1234)
1691)
>65 years
of age
18 (69.2)
8 (30.8)
79 (80.6)
19 (19.4)
1037 (797,
924 (695,
794 (676,
708 (580,
1349)
1230)
933)
864)
9 (60)
6 (40)
53 (66.3)
27 (33.7)
Men
13
<65 years
of age
1443 (1116,
1286
1105 (910,
985 (797,
1866)
(984,
1343)
1218)
1681)
>65 years
of age
11 (61.1)
7 (38.9)
21 (47.7)
23 (52.3)
1020 (761,
909 (679,
781 (643,
696 (571,
1366)
1217)
949)
849)
2 (40)
3 (60)
36 (62.1)
22 (37.9)
1114 (899,
868
865 (770,
674 (571,
1380)
(677,
972)
797)
Procedures
No
procedure
1113)
Dialysis
31 (64.6)
17 (35.4)
137 (69.2)
61 (30.8)
1347 (1095,
1050
1046 (929,
816 (700,
1657)
(833,
1178)
950)
1325)
Physiotherapy
1 (50)
1 (50)
8 (53.3)
7 (46.7)
1630 (1303,
1270
1266 (1081,
987 (832,
2038)
(1003,
1482)
1170)
1610)
1 (50)
1 (50)
41 (70.7)
17 (29.3)
—
1537
—
1193
Amputation
Blood
—
(1183,
(965,
1996)
1475)
2 (100)
2 (100)
1859
transfusion
14
—
1443
(1375,
(1106,
2512)
1884)
2 (100)
2 (100)
Wound
care
2884 (2004,
2248
2239 (1589,
1746
4149)
(1582,
3156)
(1259,
3194)
6 (85.7)
1 (14.3)
2422)
3 (60)
2 (40)
Table 1.1
GLM: estimated costs from GLM model for hospitalization costs adjusted for age, for sex, for
outcome, and then separately for procedures. Costs in USD, (United States dollars).
“Household average monthly income increased from US$33 in 2020 to US$75 in 2021, the
report by Zimbabwe Vulnerability Assessment Committee (ZimVac) said, across all provinces,
incomes have increased from a range of between US$27 and US$45, to a range of between
US$63 and US$102. About 67 percent of Zimbabweans live in the rural areas, with farming
and labor remaining the major source of livelihoods”, according to the Herald.
On average a glucose meter costs $40 to $60 and the strips that come with is cost $100/month
which is about $1200 a year to check blood glucose regularly. The glucometer will be able to
help decrease the number of deaths due to diabetes and health complications related to the
disease. As the president of the Association urged it is essential for patients to be able to control
their blood glucose levels. By continually monitoring blood glucose levels the patients will be
able to keep better records and effectively change their diet plans. This reduces the risk of
potential complications that lead to expensive hospital visits and procedures as stated above.
The glucose meter would ideally cut out the expense of buying strips continually as the sensor
will be in the body for a long duration. Having a somewhat permanent implant that continually
monitors the blood glucose level allows patients freedom of worrying about their blood glucose
throughout the day. Having a meter within the body would also help the members of society
that are dependent. The application would allow care takers to help their patients without
having to prick their finger and manually check. Teachers with diabetic students would also be
able to keep up with blood sugar fluctuations and prevent students from getting ill during
school time. Disabled individuals will be able to live a more independent life as they only have
to check their devices to tell their blood sugar. In a post covid19 world having the ability to
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send data virtually about your illness to healthcare professionals instead of going in person is
preferable
Section 2: LITERATURE REVIEW
2.1:
INTRODUCTION
Ongoing advances in technology and clinical research have addressed progressively more accurate
and precise, small, comfortable, reasonably unobtrusive and user friendly implantable devices that
offer great help in the monitoring of blood glucose levels thus providing benefits to many people
with diabetes. These devices are for continuous glucose monitoring (CGM) and these (CGM)
sensors have led a paradigm shift to painless, continuous, zero-finger pricking measurement in
blood glucose monitoring. They have the capacity to provide information by instantaneous realtime display of glucose level and rate of change of glucose. They also alert a diabetic patient or
the care giver responsible in taking care of the patient of impending hypoglycemic/hyperglycemic
events and thereby enabling the patient to avoid extreme hypoglycemic/hyperglycemic excursions
as well as minimizing deviations outside the normal glucose range, thus preventing both lifethreatening events and the debilitating complications associated with diabetes (Santhisagar
Vaddiraju, 2010). Due to the reason that some CGM devices penetrates/breaks the skin and/or the
sample is measured extra corporeally, these devices can be termed as totally invasive, minimally
invasive and noninvasive. In addition, CGM devices can be classified according to the transduction
mechanisms used for glucose sensing (i.e., electrochemical, optical, and piezoelectric). However,
on a sad note, most of these technologies are plagued by a variety of issues that are affecting their
accuracy and long-term performance.
2.2: BLOOD GLUCOSE MEASURING
. As we already know that the complications arising from diabetes can be reduced and even
prevented through careful management that includes regular checking or measuring of glucose
levels. It is recommended that a Type 1 diabetic patient should check his/ her glucose levels at
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least four times per day, while a Type 2 diabetic patient should check his/her glucose levels at least
two times per day. The patient may need to test before meals and snacks, before and after exercise.
In this case continuous glucose monitoring (CGM) then becomes a highly desirable proposition as
these devices will enable the identification of glucose trends.
A CGM device usually consists of a glucose sensor that continuously measures physiological
(blood or interstitial fluid [ISF]) glucose levels, an electronic processing unit that is wired or
wireless with the glucose sensor and a data display unit. These data may then be used to interpret
the glucose levels of the patient thus whether the patient requires insulin. In simpler terms when
measuring blood glucose a tiny sensor is inserted under your skin usually on the belly or arm. ISF‘s
glucose level is detected by the sensor and thus represents the glucose found between the cells.
The results then transmitted to a monitor (either an insulin pump, smart phone or watch).
2.2.1: Subcutaneous Continuous Blood Glucose Monitoring
An implantable sensor is a key component of a CGMS. The subcutaneous sensors that are
implanted under the skin and continuously monitor subcutaneous interstitial fluid (ISF) glucose.
Subcutaneous ISF glucose is a well-known alternative sample to blood glucose as it shows good
correlation with time lag of (5–30) minutes. Subcutaneous glucose sensors needs calibration of
sensor signal-to-glucose concentration (in vitro) and glucose concentration-to-blood glucose (in
vivo). Electrical current of amperometric sensors or fluorescence intensity of fluorescent sensors
should be converted into information of glucose concentration. If we assume a linear relationship
between sensor reading and blood glucose, blood glucose can be calculated from sensor readings.
Thus, sensor sensitivity means the calibration factor. After in vitro calibration, in vivo calibration
should be followed. Since subcutaneous glucose sensing is indirect sensing for blood glucose, we
should have calibration factor to convert glucose concentration in subcutaneous interstitial fluid to
blood glucose concentration. Thus this indirect sensing inevitably needs in vivo calibration (Yun
Jung Hoe, 2019).
CGMS sensor technology of Abbot Freestyle Libre and Dexcom G6 use factory calibration (the
calibration process is part of the sensor manufacturing process and performed under controlled
laboratory conditions). Factory-calibrated CGM sensors remove burden of user in vivo calibration
and include calibration-factor preprogramming into the sensor electronics during sensor
manufacturing process (Yun Jung Hoe, 2019).
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Section 2.3:
DISEASES RELATED TO USE OF BGL DEVICES
Some diseases are a major drawback when it comes to using these Blood Glucose Measuring
Devices. Thus they act as barriers from using CGMs. Due to use of blood glucose monitoring
devices there is a very high risk of transmitting diseases such as hepatitis B virus (HBV) and other
infectious diseases during assisted glucose (blood sugar) monitoring and insulin administration.
Also we cannot use the current blood glucose monitoring devices to a psychiatric patient as these
patients might not be able to maintain or even remove them
2.4: CURRENT BGL MONITORING DEVICES
The current BGL monitoring devices are classified by their placement of the glucose sensor thus
whether it penetrates the skin or not and its communication with the electronic processing unit
defines the invasiveness of a CGM device. These CGM devices are then classified into three
categories which are invasive (totally implantable) sensors, minimally invasive sensors, and
noninvasive sensors.
There are four newest versions that are currently on the market in the US. These include the
Dexcom G6, The Freestyle Libre system from Abbott and the Medtronic’s Guardian Sensor 3 and
the Eversense.
2.4.1:The Dexcom 6 CGM
Fig 2.4.1: Dexcom 6
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It is the current CGM system from Dexcom. The sensor is applied to the skin every 10 days with
an easy to use automatic applicator. It checks your sugar levels regularly and wirelessly. The
glucose readings are send to a receiver such as your smart phone or watch. Also alarms are present
to alert in the presence of abnormalities. The Dexcom is factory calibrated and therefore no manual
blood glucose checks are needed during the day to calibrate the sensor. Furthermore, the glucose
readings from the Dexcom are FDA-allowed to be used for diabetes treatment decisions without a
confirmatory finger stick. It is for patients of ages 2 and above.
2.4.2: Abbott Freestyle Libre 2 System
Fig 2.4.2: Abbott Freestyle Libre 2 System
It is the latest CGM from Abbott that display blood sugar readings when the patient is placed next
to the sensor. Readings can be transmitted to a smart phone. The sensor can lasts for about 14 days
and is easily inserted with an applicator. It measures sugar levels for every few minutes and records
the glucose levels for 8hours.Alarms are present for notifications.
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2.4.3: Medtronic Guardian 3 CGM System
Figure 2.4.3: Medtronic Guardian 3 CGM System
The Guardian 3 is the current CGM for Medtronic. It last for 7 days. Alarms are also present in
this CGM .Also transmission of sugar levels to smart phones is present as well. The Guardian 3
needs to be calibrated twice daily with a manual blood glucose check. It is approved for patients
who are 3 years old and above.
2.4.4: Eversense
Fig 2.4.4: Eversence
It is from Senseonics. It is the only implantable sensor as of now .The sensor is as small as a small
twig sensor is implantable under the skin of the back of the upper arm. It is removable or you can
take it off when you want to take a break from wearing the device or when you want to do activities
like swimming or any formal even. Due to this, it makes it easier to charge the transmitter. The
Eversence requires a visit to the doctor after every 6months to change the sensor. This makes it
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the most recent CGM that lasts for a longer period and offers great flexibility and freedom
compared to the others. It’s also the only CGM to provide on-body vibration alerts of any
abnormalities even if your smartphone is not with you. Thus the Eversence is the long lasting CGM
sensor available with two sensor insertion and removal procedures per year. Also the device uses
novel, proprietary light based technology to measure glucose levels.
2.5: ARTIFICIAL INTELLIGENCE AND ROBOTICS USED IN BLOOD
GLUCOSE MEASURING
Artificial intelligence methods in combination with the latest technologies, including medical
devices, mobile computing, and sensor technologies, have the potential to enable the creation and
delivery of better management services to deal with chronic diseases. In this particular paper, AI
is referred to as a branch of computer science that aims to create systems or methods that analyze
information and allow the handling of complexity in a wide range of applications in this case,
diabetes management.
Over the past decade, development of the Artificial Pancreas has been intensively pursued. An
Artificial Pancreas consists of an automated system that mimics islet physiology, including a
glucose sensor, a closed-loop control algorithm, and an insulin infusion device. Artificial Pancreas
system aim to improve overall diabetes management and reduce the frequency of life-threatening
events associated with Type 1 Diabetes. The algorithms used by the Artificial Pancreas to calculate
insulin dosage have been intensively investigated, either using data from diabetic patients or
computer-simulated patients, commonly named virtual patients (VP). The major candidate
algorithms are derived from traditional control engineering theory but AI has become more
established over the past few years and could in the future provide better candidates to meet the
challenges of an Artificial Pancreas.
The ability to anticipate Blood glucose fluctuations could provide early warnings regarding
ineffective or poor treatments. Thus, information collected from new technologies for diabetes
management, such as the CGM devices, could lead to real-time predictions of future glucose levels.
Prediction of BG levels is challenging due to the number of physiological factors involved, such
as delays associated with absorption of food and insulin, and the lag associated to measurements
in the interstitial tissue. Errors of the CGM also increase the difficulty of predicting Blood Glucose
values.
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The most common insulin therapies for diabetics, continuous subcutaneous insulin infusion (CSII)
and multiple daily insulin injections (MDI) operate according to similar principles. Both utilize
basal insulin (injection of long-acting basal insulin and infusion at a constant basal rate,
respectively) and bolus insulin (injection of quick-acting bolus insulin and meal boluses,
respectively) to cover meals or snacks. The calculation of correct insulin doses and the estimation
of the amount of carbohydrates is a regular task in the daily life of many insulin-dependent patients.
Bolus advisors are based on previous insulin doses, Blood Glucose measurements, planned
carbohydrate estimates, and other patient-specific parameters, including insulin-to-carbohydrate
ratio and insulin sensitivity. Manually calculating bolus doses and counting carbohydrates can be
complex and challenging because individuals must consider multiple parameters to achieve
satisfactory glucose control, and miscalculation of these values may result in persistent glycemic
episodes. To support carbohydrate estimation and determination of insulin doses by patients, tools
for providing bolus advice and carbohydrate estimates are increasingly being adopted. These tools
seek to increase the accuracy of mealtime and correction boluses. AI has been used to provide sets
of tools to improve the accuracy of carbohydrate estimates and to calculate the optimal insulin
bolus for the ingested meal.
Research groups at the Imperial College London performed an extensive study of an insulin bolus
calculator based on case-based reasoning methodology. Their approach, which managed various
dynamically optimized diabetes scenarios, was proven in a clinical trial to be a safe decision
support tool. Additionally, this approach was demonstrated to improve glycemic control in
diabetes management when it was combined with an Artificial Pancreas system. The Center for
Biomedical Engineering Research at the University of Bern performed several important and
extensive studies investigating the GoCARB system, which provides dietary advice to diabetic
patients based on automatic carbohydrate counting. (Contreras and Vehi, 2018)
Hypoglycemia affects the electrophysiology of the heart, and because it has slightly different
effects on each individual’s heart, an AI system makes it possible to monitor glucose levels in a
highly personalized way. In the recent pilot study, the team used AI to automatically detect
nocturnal hypoglycemia from just a few heartbeat signals recorded by a wearable device. The study
included healthy individuals, whom the scientists monitored for 24 hours a day for 14 consecutive
days. This study was unique because the scientists monitored the participants’ glucose levels
22
individually, whereas previous trials had analyzed results from the participants as a group. The
authors believe that their new approach captures the considerable diversity in ECG signals among
individuals, which previous trials could not accurately incorporate. (Nall, 2020)
There are Semi-invasive CGMs that rely on a sensor placed on the arm or stomach, with micro
needles piercing the skin. Their objective is to sense the glucose levels in the interstitial fluid that
runs between body cells in the tissues below the skin. The drawback is that the glucose levels are
not real-time: there is a lag of approximately 15 minutes between the glucose levels in the blood
and in the interstitial fluid, but results are still considered acceptable. The components of such
devices usually are a disposable sensor and a reader/display. The sensor communicates glucose
readings continuously to the reader via Bluetooth or other technology. Recently trend is the reader
is replaced with the user’s smartphone, which can also allow for sharing of the readings and
associated trend reports directly with doctors.
The leading companies in this space are Dexcom and Abbott, with Medtronic following closely.
Senseonics has a newly approved device with a 90-day sensor (whereas other sensors last for 5 to
7 days). Implant-based CGMs are a little trickier: they have an implant in the body in the
subcutaneous layer but these sensors are touted to last for a year or two. In essence, they require a
minor procedure for implantation versus the semi-invasive ones that can be placed by a general
physician, a dialectologist or, in some cases, the patient at home. The eversense implant CGM is
an example of this type. (Blood, 2018)
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Fig 2.5.1: (Blood, 2018)
Robotics involves the, design, construction, and use of machines (robots) to perform tasks done
traditionally by human beings. Recently there has been the development of the robot called Robin.
Robin is a cognitively and motivationally autonomous affective robot toddler with “robot diabetes”
that has been developed to support perceived self-efficacy and emotional wellbeing in children
with diabetes. Robin provides children with positive mastery experiences of diabetes management
in a playful but realistic and natural interaction context. Underlying the design of Robin is an
“Embodied” (formerly also known as “New”) Artificial Intelligence (AI) approach to robotics.
Robin’s decision-making architecture follows principles of Embodied AI, also known as “New
AI”. The robot is built around a “physiology” of homeostatically controlled “survival-related”
variables that Robin needs to keep within permissible values. The robot has a simple model of
Type 1 diabetes, comprising an internal blood glucose level that increases upon “eating” toy food,
and decreases with “insulin”. Robin chooses how to behave as a function of these internal needs
and the stimulation it gets from the environment. Elements of the environment are detected using
vision (e.g., foods, faces) and tactile contact (e.g., collisions, strokes, hugs). Internal needs and
environmental cues are mathematically combined in what is called motivations. Motivations lead
Robin to autonomously select behaviors from its repertoire (e.g., walking, looking for a person,
eating, resting) that best satisfy its needs (e.g., social contact, nutrition, resting, playing) in the
present circumstances. For this reason, Robin is a motivationally and cognitively autonomous
robot.
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Fig 2.5.2 (Canamero and Lewis, 2016)
Robin has a simple simulated glucose physiology that tracks what food has been eaten and has
been digested (gradually raising the blood glucose), what insulin doses have been given (gradually
being released into the blood, lowering blood glucose), and the amount of physical activity, which
is determined by the current to the joint motors (which acts to slightly lower blood glucose). Hypoand hyper-glycaemia have associated symptoms, such as increased tiredness resulting in a change
in behavior, alerting the children of the potential presence of a problem. Using a Bluetooth
“glucometer” device, the children can measure Robin’s glucose levels and provide insulin to lower
its glucose (correcting hyper-glycaemia). They can feed Robin high-glucose food to raise its
glucose (correcting hypo-glycaemia). (Canamero and Lewis, 2016)
Section2.6: CASE STUDIES
Function of an Implanted Tissue Glucose Sensor for More than One Year in Animals
An implantable sensor capable of long-term monitoring of tissue glucose concentrations by
wireless telemetry was developed for eventual application in people with diabetes. The sensortelemetry system functioned continuously while implanted in subcutaneous tissues of two pigs for
a total of 222 days and 520 days respectively, with each animal in both non-diabetic and diabetic
states. The sensor detected glucose via an enzyme electrode principle that is based on differential
electrochemical oxygen detection, which reduces the sensitivity of the sensor to encapsulation by
the body, variations in local micro vascular perfusion, limited availability of tissue oxygen, and
inactivation of the enzymes. After an initial two-week stabilization period, the implanted sensors
maintained stability of calibration for extended periods. Long-term tissue glucose monitoring with
a sensor-telemetry system implanted in subcutaneous tissues of pigs was achieved. Monitoring
was carried out while pigs were non-diabetic (for three weeks in one animal and nearly one year
in the other) and, after the pigs had been converted to insulin-dependent diabetic by administration
of streptozotocin (STZ), monitoring continued in each animal for an additional six months, with
diabetes managed by insulin injection and diet control.
The implant consisted of eight 300-µm platinum working electrodes, with associated platinum
counter electrodes and Ag/AgCl potential reference electrodes are arranged as four sensor pairs on
the surface of a 1.2-cm diameter alumina disc. A thin electrolyte layer, a protective layer of medical
25
grade polydimethylsiloxane (PDMS), covered the electrodes and a membrane of PDMS with wells
for the immobilized enzymes located over certain electrodes. The enzymes were immobilized in
the wells by crosslinking with albumin using glutaraldehyde, and the resulting gel is rinsed
extensively to remove unbound material. The alumina disc is fused into a hermetically sealed
titanium housing containing a potentiostat and signal conditioning circuitry for each sensor, a
wireless telemetry system, and a battery having a minimum 1-year lifetime. The implant was
sterilized with a chemical sterilant using a procedure that has been validated according to standard
methods .The implant was 3.4 cm in diameter and 1.5 cm thick. The top surface of the implant
includes two polyester velour patches for tissue adhesion.
Procedure
Two series of implant studies were conducted. In the first series, intended to aid design
optimization and component reliability verification, 30 individual sensor-telemetry units were
implanted in six non-diabetic pigs to refine the surgical technique, evaluate device tolerance and
biocompatibility, test the electronic circuitry and telemetry, and identify factors that affect the
lifetime of the sensor. The devices in this series were explanted and analyzed according to pre-set
protocol schedules at periods ranging from one to eighteen months after implantation. Results from
this foundational research included verification of acceptable long-term biocompatibility, assessed
following 18-month implant period , immobilized enzyme life exceeding one year, battery life
exceeding one year , electronic circuitry reliability and telemetry performance, sensor mechanical
robustness including long-term maintenance of hermeticity, stability of the electrochemical
detector structure and acceptability and tolerance of the animals to the implanted device. Results
were also obtained from this series related to the effects of tissue permeability and tissue
remodeling, and are reported below.
In the second series, which involved evaluation under diabetic conditions and which is described
in detail here, two devices were implanted in each of two pigs (4 devices total), and first operated
for 352 days (Subject #1) and 16 days (Subject #2) respectively, with the animals in the nondiabetic state. The animals were then made diabetic by administration of STZ, and the devices
continued to operate for an additional 168 days in Subject #1 to a total of 520 days, and for an
additional 206 days in Subject #2 to a total of 222 days. Individual experiments were terminated
based on consideration of the resources required to maintain diabetic animals.
26
No adverse medical events (infection, erosion, migration, etc.) were encountered with any implant
in either test series. Together, the test series represent a collective 31 total device-years of implant
experience, with 17 of the devices remaining implanted and functional for more than one year.
That study showed that, with appropriate design, an implanted glucose sensor could potentially
operate effectively for long periods in the body. These experimental results and the understanding
of the sensor function derived from animal studies provide a foundation for translation to human
clinical investigation. (Gough et al, 2010)
Implantable and transcutaneous continuous glucose monitoring system: a randomized cross
over trial comparing accuracy, efficacy and acceptance
In a randomized crossover trial 12 weeks with Eversense implantable sensor (EVS) and 12 weeks
with Dexcom G5 transcutaneous sensor (DG5) were compared in terms of accuracy, evaluated as
Mean Absolute Relative Difference (MARD) vs capillary glucose (SMBG), time of Continuous
Glucose Meter use, adverse events, efficacy (as HbA1c, time in range, time above and below
range) and psychological outcomes evaluated with Diabetes Treatment Satisfaction Questionnaire
(DTSQ), Glucose Monitoring Satisfaction Survey (GMSS), Hypoglycemia Fear Survey (HFS2),
Diabetes Distress Scale (DDS).Continuous glucose monitoring (CGM) improves glycemic control
in subjects with type 1 diabetes (T1D), decreasing HbA1c (HbA1c is a blood test that is used to
help diagnose and monitor people with diabetes. It is also sometimes called a hemoglobin A1C,
glycated hemoglobin or glycosylated hemoglobin. HbA1c refers to glucose and hemoglobin joined
together (the hemoglobin is 'glycated') and reducing hypoglycemic events.
The study found that CGM is still used less than expected, especially by adolescents. In addition,
CGM systems are often used intermittently. Misuse may be connected with problems with
reimbursement, physical discomfort, problems with sensor insertion and holding on the skin,
concerns about the accuracy of data, interference with sports and daily activities, skin reactions.
Most CGM systems sample glucose concentration in the interstitial of the subcutaneous tissue
through a transcutaneous needle-sensor connected to a transmitter that sends data to a receiver or
a mobile app. They have a lifetime of 7–14 days and the short life span affects adversely patient
adherence. An implantable sensor has been developed, with a lifetime of up to 180 days. The
sensor, inserted subcutaneously in the upper arm, sends data to a removable transmitter worn over
27
the implant and then to the system’s mobile application via Bluetooth. Sensor implantation and
removal require a minor surgical procedure, unlike transcutaneous CGM systems, which are selfinserted by the patients. Clinical studies have shown that both transcutaneous and implantable
systems are safe, well-tolerated and effective, reducing HbA1c and hypoglycemic events and
improving the quality of life.
Procedure
In this study Participants chosen were 18 years or older, with a diagnosis of Type 1Diabetes from
at least one year (World Health Organization criteria), treated with CSII or MDI, with
HbA1c < 10% (86 mmol/mol). Exclusion criteria were episodes of hypoglycemia in the previous
12 months, pregnancy, lactation, medications (apart insulin) affecting glucose metabolism,
inability to comply with study procedures, allergy to skin patches or disinfectants. During the
study, participants omitted drugs interfering with sensor-related measurement of glucose like
acetaminophen. Participants were randomly assigned to 12 weeks with DG5 as first system and
then to 12 weeks with Eversence (arm AB) or vice versa (arm BA) in a 1:1 ratio. Randomization
was performed using a computer-generated random assignment list.
Results
Five premature Eversence transmitter failures occurred: one in a participant during the first
4 weeks of use (with loss of 2 days of data until the transmitter was replaced), two in two different
participants during the 4–8 weeks period (with a loss of data of 13 days/patient) and two failures
in the same participant (with a loss of data of 9 days).
One premature Eversence sensor failure was registered in a participant after 79 days of use.
One premature Dexcom G5 transmitter failure occurred in one participant after 75 days.
In real life Eversence was to have a greater overall accuracy than Dexcom G5, in particular in the
euglycemic range, a fact not observed in a previous study lasting 7 days, probably due to the short
duration of the study. The superior performance of Eversence was assumed to be related to the fact
that it does not allow patients to insert a glucose calibration value if it is too different from values
registered by the sensor at the moment of calibration if it is lower than 2.2 mmol/l (40 mg/dl) or
28
higher than 22.2 mmol/l (400 mg/dl). The calibration mode of Eversence was seen as negative by
only 28% of patients.
It was found that when using the implantable sensor, the mass of available data and the percentage
of sensor use were smaller compared to Dexcom G5. The difference was assumed to be related to
the need to recharge the transmitter every other day for 15 min, to intercurrent transmitter removal
or to problems with the connection between Continuous Glucose Monitoring components. The
transmitter failures were more frequent with Eversence, but this did not translate into decreased
confidence by patients, possibly for the quick substitution of malfunctioning Eversence devices
assured during the study.
In this study, the use of Eversence was associated with better metabolic control, more time in
target, lower mean glucose, smaller standard deviation and a shorter time in hyperglycemia. Thus
showing how implanted glucose meters are better overall. (Boscari et al, 2022)
2.7: RECOMMENDED GADGETS BY W.H.O

Dexcom G6 CGM

Abbott Freestyle Libre System 2

Eversence CGM
Section 3: Materials and Methodology
3.1 Introduction
This section contains relevant methods that were employed as part of solving the aforementioned
problem as well as to achieve the aim and objectives stated in the preceding chapters. We manage to
produce a design of the CGM which is explained by the block diagram, flow chart and code. We stated
and analyzed all the components necessary to make the CGM. We came up with a block diagram showing
the principal parts that are used in coming up with the CGM and also which shows the relationship of the
blocks. A flow chart was also generated which showed the workflow o this CGM and how the code
operates. Lastly the setup is shown and how each component is connected on the Arduino Uno board.
29
3.2 Materials
Arduino Uno Board, BGL sensor module , LCD, piezo Buzzer, 10kΩ resister potentiometer, Breadboard,
Several jumper wires, USB cable for programming, A computer for programming only, 7, 330-ohm
resistors, Red, yellow and green LED, Transmitter (IR 940nm)
Calibration
Blood
Sample
Signal
Conditioner
Sensor
Algorithm /
Code
Output
LCD Display
LED
ALARM
30
ADC
Microcontroll
er Unit
(Arduino
Uno)
3.3.2 Flowchart Showing How the CGM Operates
Start
Initialize all
components
Fetch Blood
Sample Glucose
Level Data
Sensor data
processing
Is BGL
Normal
(7099mg/dl)?
High or
Very high
BGL?
YES
NO
Green LED
turns on
Low or
Very Low
BGL?
Yellow LED Turns On
Red LED Turns On
Display the BGL in
LCD
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Alarm sounds
LCD display and
Save
3.3.3 The Code
Int led1 = 13;
Int led2 = 12;
Int led3 = 11;
Int led4 = 10;
Int led5 = 9;
Int led6 = 8;
Int ledState = LOW; //set the LED before it starts
Int x;
Int xDoubled;
Void setup() {
// put your code here, to run once:
Serial.begin (9600); // connection between arduino and computer????
pinMode (led1, OUTPUT);
pinMode (led2, OUTPUT);
pinMode (led3, OUTPUT);
pinMode (led4, OUTPUT);
pinMode (led5, OUTPUT);
pinMode (led6, OUTPUT);
// all the LEDs respond to a certain amount of voltage created by the blood
}
Void loop() {
// put your main code here, to run repeatedly:
x = analogRead (A0); // this is where 2 get the connection from the blood to the arduino
xDoubled = x*2;
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if ( xDoubled <= 170 ) { // If the voltage is under 2mV you are very low in glucose
digitalWrite (led6, HIGH); turn on LED6
delay (20000); how long it stays on
digitalWrite (led6, LOW); // turn off LED6
}
if ( 300 > xDoubled < 500 ) { // between 2.8mV and 6.5 mV you are low in glucose
digitalWrite (led5, HIGH); // turn on LED5
delay (20000); // how long it stays on
digitalWrite (led5, LOW); // turn off LED5
} // if value is between 2.8 mV and 3.8 mV
if ( 510 > xDoubled < 690 ) { // between 7mV and 11mV you have a normal glucose level
digitalWrite (led4, HIGH);
digitalWrite (led3, HIGH); // turn on LED4 and LED3
delay (20000); // how long it stays on
digitalWrite (led4, LOW);
digitalWrite (led3, LOW0; // turn off LED4 and LED3
} // if value is between 3.8 mV and 7.4mV
if ( 750 > xDoubled < 860 ) { // between 12mV and 18Mv you have a high glucose level
digitalWrite (led2, HIGH);
delay (20000);
digitalWrite (led2, LOW);
}
if ( xDoubled < 900 ) { // if the voltage is above 20 mV, the blood glucose level is very high
digitalWrite (led1, HIGH);
delay (20000);
digitalWrite (led1, LOW);
33
}
}
4.0 Results and discussion
4.1 The Setup
When the we run the code
Results obtained from the Fuzzy Controller system that was proposed.
34
Target
Levels Upon waking
by Type
Before
meals At least 90 minutes after
(pre prandial)
meals
(post prandial)
Non-diabetic*
4.0 to 5.9 mmol/L under 7.8 mmol/L
Type 2 diabetes
4 to 7 mmol/L
under 8.5 mmol/L
5 to 7 mmol/L
4 to 7 mmol/L
5 to 9 mmol/L
Children w/ type 1 4 to 7 mmol/L
4 to 7 mmol/L
5 to 9 mmol/L
Type 1 diabetes
diabetes
Using the table above as the Data set , the optimal range of blood glucose level is normally
between 4 to 9 mmol/L . Our fuzzy logic system design aims to alert the patient when their Blood
Glucose Level spikes or dips . Most doctors recommend that the patient should try to keep their
Blood Glucose Level at 4.5 mmol/L . With more time and testing the design will include more
values and more varibles such that the Blood Glucose Level can be maintained .
The graph below accurately shows which blood glucose level ranges result in alerting the patient
with anything above 9 mmol/L being a high Glucose Level and anything below 4 mmol/L being a
low Glucose level . The graph also shows that between 4 and 9 mmol/L is the safe range where
the patient is not in danger . This system helps the Patient maintain their Blood Glucose Level
through out the day . Obviously the Fuzzy Logic System needs to be more meticulous in future
adding more output functions that can aid the patient more efficiently .
35
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Boscari, F., Vettoretti, M., Cavallin, F., Amato, A.M.L., Uliana, A., Vallone, V., Avogaro, A.,
Facchinetti, A. and Bruttomesso, D. (2022). Implantable and transcutaneous continuous glucose
monitoring system: a randomized cross over trial comparing accuracy, efficacy and acceptance.
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Contreras, I. and Vehi, J. (2018). Artificial Intelligence for Diabetes Management and Decision
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Gough, D.A., Kumosa, L.S., Routh, T.L., Lin, J.T. and Lucisano, J.Y. (2010). Function of an
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