# Everything You Wanted to Know About Noninvasive Glucose Measurement and
Control

Prateek Jain Amit M. Joshi Saraju P. Mohanty Dept. of ECE Dept. of ECE
Computer Science and Engineering MNIT, Jaipur, India MNIT, Jaipur, India
University of North Texas, USA<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Diabetes is a chronicle disease where the body of a human is irregular to
dissolve the blood glucose properly. The diabetes is due to lack of insulin in
human body. The continuous monitoring of blood glucose is main important
aspect for health care. Most of the successful glucose monitoring devices is
based on methodology of pricking of blood. However, such kind of approach may
not be advisable for frequent measurement. The paper presents the extensive
review of glucose measurement techniques. The paper covers various non-
invasive glucose methods and its control with smart healthcare technology. To
fulfill the imperatives for non-invasive blood glucose monitoring system,
there is a need to configure an accurate measurement device. Noninvasive
glucose-level monitoring device for clinical test overcomes the problem of
frequent pricking for blood samples. There is requirement to develop the
Internet-Medical-Things (IoMT) integrated Healthcare Cyber-Physical System
(H-CPS) based Smart Healthcare framework for glucose measurement with purpose
of continuous health monitoring. The paper also covers selective consumer
products along with selected state of art glucose measurement approaches. The
paper has also listed several challenges and open problems for glucose
measurement.

###### Index Terms:

Smart Healthcare, Internet-of-Medical-Things (IoMT), Healthcare Cyber-Physical
System (H-CPS), Diabetes, Glucose measurement, Non invasive measurement,
Spectroscopy and calibration

##  I Introduction

The glucose is considered as important source of energy for the human body.
The body requires blood glucose of normal range (80 to 150 mg/dl) in order to
perform the daily activities [1]. However, the higher or lower value of
glucose would lead to various complication inside the body. At the same time,
insulin is also crucial hormone generated inside the body from the food
intake. The glucose is produced from the food digestion which enters the blood
cell to supply the energy and also helps in the growth. In case, the insulin
is not properly generated then blood would accumulate the high glucose
concentration. Fig. 1 illustrates the closed-loop of glucose generation and
consumption in human body [2]. A consistently high blood glucose concentration
is possible if the generation of $\alpha$ cells is larger as compared to that
of the $\beta$ cells. Because of this condition, enough insulin is not
secreted in the body for glucose consumption. This condition refers to as the
Diabetes Mellitus. Diabetes is termed as chronic disease which defines high
blood glucose levels inside the human body. The unbalanced glycemic profile is
main reason for the cause of diabetic condition. The rate of prevalence for
Non Communicable Diseases (NCD)/Chronic Disease has increased with many fold
from last several years. There are around 20 million death reported yearly
through cardiovascular disease, for which high blood glucose is significant
predisposing factors. Moreover, people with diabetes are more affected during
the viral pandemic outbreaks [3, 4, 5].

Figure 1: Illustration of the closed loop form of glucose generation and
consumption [2].

There has been exponential growth of diabetes patients over past few years
because of obesity, unhealthy diet plan, old-age population, and inactive
lifestyle. Diabetes is considered as one of the fastest growing health
challenges, with the number of adults living with diabetes having more than
tripled over the past 2 decades (Refer Fig. 2) [6]. The prevalence of diabetes
around the world was 9.3% during 2019 with approximate 463 million people. It
is expected to rise to 578 million by 2030 with 10.2% prevalence rate and the
same would be 10.9% with 700 million population by 2045. It has been observed
that prevalence is quite higher in urban to 10.8% whereas 7.2% in rural
region. Almost half of the diabetes patients unaware about their situation due
to lack of knowledge. The diabetes has indeed global outbreak which has
affected presently almost 1 in 10 people around the world. It is projected
that more than 0.5 billion adults would suffer from the diabetes in the next
decade [7]. As per the report from International Diabetes Federation (IDF),
the death from diabetes has large number than combined death from Malaria
(0.6mio), HIV/AIDS (1.5mio) and tuberculosis (1.5mio) [8]. There are around 8
million new patients are being added to diabetic community every year. This
has grown the demands immensely for the effective diabetic management. It is
important to monitor the blood glucose over time to time for avoiding late-
stage complication from diabetes. This has necessitate the design of various
reliable and robust solutions for efficient diabetes management. The market of
diabetes devices has also grown rapidly with significant requisite for
frequent glucose measurement for better glycemic profile control.

Figure 2: Global trend of Diabetes, Adopted from [6].

Diabetes is one of the major chronic disease which has long-term impact of the
well-being life of a person. Diabetes Mellitus (DM) is considered as
physiological dysfunctions with high blood glucose because of insufficient
insulin, insulin resistance, or excess generation of glucagon [9]. It is the
critical health issue of $21^{st}$ century. Type 2 Diabetes (T2DM) has shown
rapid growth around the world from past few years. Any form of diabetes may
lead to complications in various body parts which increase the possibility of
premature death. The higher value of blood glucose known as hyperglycemia,
would lead to thickening of blood vessels which could resulted in kidneys
damage and loss of sight and some times even to these organs failure. Diabetes
is also associated with limb amputation, peripheral vascular diseases and
myocardial. Contrary, the low blood glucose defined as hypoglycemia may occur
in Type 1 Diabetes Patients (T1DM) for excessive insulin dosage [10]. The most
common symptoms for hypoglycemia pateints are dizziness, sweating and fatigue
and in the worst case it can lead to coma and death. The diabetic patients
would have several common symptoms such as thirsty, tiredness, changes in
vision, consistently hungriness, unexpected weight loss and the excretion of
urine within short durations [11]. If the diabetes remain untreated over the
period of time, it may cause blindness, heart stroke, kidney disease, lower
limb amputation and blindness. It would lead to increase the probability of
death almost 50% higher in comparison of the patients without diabetes. The
diabetes also brings the additional financial burden for the treatment and
point of care. The diabetic patients could also result in loss of productivity
at workplace and may lead to disability. There are several health issues which
may also arise from diabetes like depression, digestive problem, anxiety
disorders, mood disorder and eating habits change. The diabetes could be
controlled with proper diet plan, through some physical exercise, insulin
dosage and medicines. The early stage of diabetes is possible to control with
oral medicines. The diabetes control also helps to reduce the associated risk
of high blood pressure, cardiovascular and amputation.

The rest of the article is organized in the following manner: Section II
briefly presents different types of diabetes while making case for the need of
glucose level monitoring. Section III presents overview of various types of
glucose-level measurement mechanisms. Section IV provides details of available
approaches for noninvasive glucose-level monitoring. Section V has discussions
on various post-processing and calibration techniques for noninvasive glucose-
level monitoring. Section VI briefly discusses various consumer products for
noninvasive glucose level measurement. Section VII presents the approaches for
glucose-level control and corresponding consumer products. Section VIII
provides the Internet-of-Medical-Things (IoMT) perspectives of glucose level
measurements and control in healthcare Cyber-Physical Systems (H-CPS) that
makes smart healthcare possible. Section IX outlines the shortcomings and open
problems of glucose-level measurements and control. Section X summarizes the
learning of this comprehensive review work.

##  II The Health Issue of Diabetes and Need for Glucose-Level Measurement

This Section presents details of different types of diabetes, the health
issues arise due to diabetes, while making case for the need of glucose level
monitoring.

###  II-A Types of Diabetes

The diabetes occurs because of insufficient insulin with respect to glucose
generated inside the body. The insulin from body is either insufficient or not
any which is generated from beta cells of the pancreas. In case of diabetes,
the cells of liver, muscles and fat unable to balance glucose insulin
effectively. The diabetes are classified mainly in three categories: Type 1
diabetes, Type 2 diabetes and gestational diabetes (Refer Fig. 3) [12].

Figure 3: Different types of diabetes and their symptoms.

For diabetes of type-1, the pancreas does not produce insulin inside the body
which is resulted in a weak immune system. This results in a person who is
unable to generate insulin naturally [2, 13]. In case of type 2 diabetes, the
amount of insulin from pancreas is not sufficient to maintain glycemic profile
of the body. Gestational diabetes usually occurs in a pregnant woman at later
stage of the delivery. in the year 2020, total 2 billion adults around the
globe suffers from overweight, and 300 million of them are obese. In addition,
a minimum of 155 million children in the world is overweight or obese. It is
projected that the prevalence of hyperglycemia is 8.0% and expected to
increase to 10% by 2025 [7]. There has been concern for diabetic people
specially in developing countries due to increase in Type 2 Diabetes cases
rapidly at earlier age which have overweight children even before puberty.
Whereas for developed countries, most of people have high blood glucose at age
around 60 years. Most frequently affected are at middle aged between 35 and 64
in developed countries [6]. In 2019, 69.2 millions population in India had
Type-2 diabetes. Approximately 2.35 million adults have Type-1 diabetes. In
general, there are around 5% adults have been considered for Type-1 diabetic
patients while the others 90-95% are of Type-2 diabetic patients. Type-1
diabetic patient must have insulin to control the blood glucose level. Type-2
diabetic patients can control their glucose level by following an optimized
diet with medication and a regular physical exercise schedule.

###  II-B The Health Crisis due to Diabetes

The diabetes mainly occurs due to unbalanced glucose insulin level of the body
where insulin is demolished and muscles and cells are not able to generate
insulin properly [14, 13]. The probability of death would also increase upto
50% in comparison to non-diabetes case. The control action of the diabetes
would be possible using proper precautionary measure after frequent glucose
measurements. Therefore, there is a real need for smart healthcare solution
which would provide instant self measurement of blood glucose with high
accuracy.

Figure 4: Diseases in human body due to diabetes

Hyperglycemia is the major issue which has been considered by several health
organizations at worldwide level [15, 16]. There are several attempts which
have been used for glucose measurement [17]. There have been substitutional
work using various techniques to make the device more familiar with clinicians
and patients [18]. Diabetes is possible in the age group 18 to 80 years
usually [19, 20]. The normal range of glucose is in the range of 70-150 mg/dL
and pathophysiological would be from 40 mg/dL to 550 mg/dL [21]. One of the
emerging issues is to design the glucose measurement device for continuous
health care monitoring [22]. The devices for monitoring the glucose level are
available for last two decades [23].

###  II-C Glucose Measurement: A Brief History

The glucose meter (aka glucometer) is a portable medical device for predicting
the glucose level concentration in the blood [24, 25]. It may also be a strip
based dipped into any substance and determined the glucose profile. It is a
prime device for blood glucose measurement by people with diabetes mellitus or
hypoglycemia. With the objective of glucose monitoring device advancement, the
concept of the biosensor has been proposed earlier in 1962 by Lyons and Clark
from Cincinnati. Clark is known as the “father of biosensors”, and modern-day
glucose sensor which is used daily by millions of diabetics. This glucose
biosensor had been composed with an inner oxygen semipermeable membrane, a
thin layer of GOx, an outer dialysis membrane and an oxygen electrode. Enzymes
could be gravitated at an electrochemical detector to form an enzyme electrode
[26]. However, the main disadvantage of first-generation glucose biosensors
was that there was the requirement of high operation potential of hydrogen
peroxide amperometric measurement for high selectivity. The first-generation
glucose biosensors were replaced by mediated glucose biosensors (second-
generation glucose sensors). The proposed biosensors till present scenario
represent the advancements in terms of portability of device and precision in
measurement. But, due to some environmental and measurement limitations; these
biosensors were not taken for real-time diagnosis. The history of glucose
measurement is shown in Fig. 5 [27].

Figure 5: History of Glucose Measurement. Figure 6: Invasive versus
Noninavsive Glucose Measurement.

###  II-D Glucose Measurement Technique

Presently, the glucose monitoring is carried out either laboratory based
technique or home based monitoring. These both approaches are invasive in
nature which provides discomfort by blood pricking and it only helps to
measure the glucose measurement at that point of time. It is also not very
convenient for the user to take out blood samples multiple times in a day and
many patients are reluctant to opt such type of solution. Therefore,
significant changes of glycemic profile may go unnoticed because of
unanticipated side effects and low compliance from the patients. This could
impact on improper insulin dosage and unknown food ingredient. However, they
are reliable solution due to their good sensitivity and higher accuracy for
glucose measurement [28, 29].

The novel approach for glucose measurement has been explored from past several
years which is based on the principle of physical detection than conventional
chemical based principle. Such non-invasive based method does not require the
blood sample but uses the interstitial fluid (ISF) for glucose molecule
detection. There are several attempts in the same direction for glucose
measurement through sweat, saliva, tears and skin surface [30]. However, the
main challenge is to have precise measurement, good sensitivity and
reliability from such measurement. Such approach could be suitable for
Continuous Glucose Measurement (CGM) and self monitoring purpose. Such CGM
techniques would provide the frequent measurement in a day which would helpful
for better glucose control and also for the necessary preventive actions for
hyperglycemia and hypoglycemia patients. Such kind of techniques would also
support for the dietician and healthcare provider to prepare proper diet plan
according to glucose fluctuation for the patient.

###  II-E The Need for Continuous Glucose Measurement (CGM)

The measurement of glucose could be done through non-invasive, semi (or
minimal) invasive and invasive approach. The frequent measurement may not be
possible using invasive method which can cause trauma. The semi-invasive and
non-invasive could be useful for Continuous Glucose Measurement (CGM) without
any pricking of the blood. However, the non-invasive glucose measurement is
most suitable technique which helps to measure the blood glucose painlessly
[31].

CGM assist to have proper blood glucose level analysis at each prandial mode.
It helps to measure glucose insulin level after insulin secretion,hysical
exercise or subsequent to medication. The frequent glucose reading also
helpful to endocrinologist for providing the proper prescription. It mainly
helps for type 1 diabetic patients to take care of their insulin dosage over
the period of time. The proper diet management could be possible with help of
recurrent glucose monitoring and flow diagram of CGM is shown as Fig. 7 [28,
29]. The CGM is useful for the patients for frequent glucose measurement over
the period of time. This would helpful to identify the average blood glucose
value for the last 90 days, by which glycated haemoglobin (HbA1c) can be
determined.

Figure 7: The objectives of continuous glucose monitoring.

##  III Approaches for Glucose-Level Measurement: A Broad Overview

This Section discusses an overview of various types of glucose-level
measurement mechanisms. In the past, many works has done for the glucose
measurement. They can be invasive, non-invasive, or minimally invasive. A lot
of works has been completed based on the non-invasive technique. They are
technically based on optical and non-optical methods. Some of the optical
techniques used methods based on Raman Spectroscopy, NIR spectroscopy, and PPG
method. A taxonomy of the different methods is provided in Fig. 8 [25, 28, 29,
32].

Figure 8: An overview of the Glucose Measurement Options [32, 28, 29, 25].

###  III-A Invasive Methods

Many commercial continuous blood glucose measurement devices use cost-
effective electrochemical sensors [33]. They are available to respond quickly
for glucose detection in blood [34]. Lancets (for pricking the blood) is used
at the primary stage for blood glucose monitoring for various commercial
devices available in the market [35]. The frequent measurement through the
process is so much panic due to picking the blood sample from the fingertip
more than 3-4 times in a day for frequent monitoring[36]. The low invasive
biosensor for glucose monitoring has been developed with glucose oxidase that
require around 1mm penetration inside the skin for measurement [37]. The
technique of photometric was attempted to detect glucose with help of small
blood volumes [38].

###  III-B Minimally Invasive Methods

The minimally invasive method using prototype sensor was developed to have
frequent monitoring of glucose tissue [39]. The sensor is wearable and is
implanted on membrane which contains the immobilized glucose oxidase. The
glucose monitoring through implantable devices were developed [40]. The semi
or minimal invasive method using biosensors designed for diabetes patient
[41]. The wearable micro system explored for frequent measurement of glucose
[42]. Similarly, there was an attempt of continuous glucose monitoring with
help of microfabricated biosensor through transponder chip [43]. The signal
coming out of transponder chip was used for the calibration for semi invasive
approach of Dexcom sensor [44]. The diabetes control explored by glucose
sensor with artificial pancreas system [45]. The minimal invasive approaches
have limitations mainly accuracy and may have shorter life span for
monitoring.

This is a wearable microsystem for the continuous monitoring of the blood
glucose. It’s a minimally invasive method for the glucose monitoring. The main
idea behind this is that it uses micro-actuator which consists the shape
memory alloy (SMA) for the extraction of the blood sample from human skin
[46]. An upgraded version of SMA is used for the implementation of PCB.
Because of it’s feasibility and performance, it can be considered as the first
wearable device for the glucose monitoring but it is large in size which makes
it inconvenient.

###  III-C Non-invasive Methods

Non-invasive measurement would mitigate all the previous issues and would
provide painless and accurate solutions [47, 48]. The non-invasive glucose
measurement solution for smart healthcare had developed through portable
measurement [31]. A lot of approaches have been introduced for glucose
measurement [49]. The non-invasive measurement are more convenient for
continuous glucose measurement in comparison to invasive method and semi
invasive [47], [48]. The glucose measurement with help of optical method has
observed more reliable and precise in the literature [50]. The popular optical
methods include non-invasive measurement such as Raman spectroscopy, near
infer-red spectroscopy,polarimetric,scattering spectroscopy [51],
photoacoustic spectroscopy [52] etc. For the development of a non-invasive
measurement device, it is considered by the researcher that the device would
be much convenient for the user’s perspective [53, 54]. Calibration of the
blood glucose to interstitial glucose dynamics have been considered for the
accuracy of continuous glucose monitoring system [55, 56]. Several calibration
algorithms have been developed and implemented for portable setup [57]. There
has been several concious efforts towards the development of the self-
monitoring system [58].

Figure 9: NIR Spectroscopy Mechanism of Serum Glucose Measurement.

###  III-D Invasive Versus Non-invasive Glucose Measurements: The Trade-Offs

Recent glucose measurement methods for the ever-increasing the diabetic
patients over the world are invasive, time-consuming, painful and a bunch of
the disposable items which constantly burden for the household budget. The
non-invasive glucose measurement technique overcomes such limitations, for
which this has become significantly researched era. Although, there is
tradeoff between these two methods which is represented in Fig. 10.

Figure 10: Representation of Tradeoffs between Invasive and Non-invasive
Glucose Measurement.

###  III-E Capillary Glucose versus Serum Glucose for Noninvasive Measurement

The serum glucose value is precise which is always close to actual blood
glucose measurement with compare to capillary glucose level. Traditional
approaches able to measure capillary glucose instantly but the serum glucose
measurement identification is difficult. It is observed that the glucose level
of capillary is always higher than serum glucose. The accurate measurement of
blood glucose would help for appropriate control actions. Therefore, it is
really important to measure the serum glucose than the capillary glucose which
is more reliable for medication. Capillary blood glucose measurement has been
used widely than serum glucose estimation for medication purpose. The serum
glucose is not possible for continuous glucose measurement or frequent
measurement for diabetes. The blood glucose is controlled in much better way
if one can measure serum glucose at regular interval. Laboratory analysis of
glycosylated haemoglobin (HbA1c) which provides 6-8 weeks blood glucose
measurement is also being done through the serum blood only. For the non-
invasive measurement point of view, serum and capillary glucose are being
measured through the optical spectroscopy. The mechanism of blood glucose
measurement is based on received IR light after absorptions and scattering
from glucose molecules which flow in blood vessels. The methodology is quite
similar for both types of glucose measurement except the post-processing
computation models which are necessary for blood glucose estimation.

###  III-F Non-invasive Method for Glucose Level Estimation by Saliva

As the most convenient method to estimate glucose level is via saliva [59] and
is used for children and adults. This saliva has specific type of parts which
can be defined as: (1) gland-specific saliva and (2) whole saliva. The
collection of the Gland-specific saliva is done by individual glands like
parotid, Sub mandibular, sublingual, and minor salivary glands. This diagnosis
is done by the history of the patient in terms of associated risk factors,
family history, age, sex, duration of diabetes,and any associated illness.
Other Glucose measuring methods consist of measurement using photo-metric
glucometers requiring very small sample volumes [60]. The basic approach is
based on the reaction of the chemical test strip that reacts with sample.
Measurement is done by capturing the reflections of the test area and then
glucose level is estimated. It requires validation in large number of
patients.

##  IV Approaches for Noninvasive Glucose-Level Measurement

This Section presents detailed discussions of various available approaches for
noninvasive glucose-level monitoring. There have been several efforts for
noninvasive glucose measurement using optical techniques [27, 29, 61, 62].
These techniques are mainly based on various spectroscopy based methods. For
the development of a non-invasive measurement device, it is considered by the
researcher that the device would be much convenient for the users perspective.
Fig. 11 presents summary of various types of noninvasive glucose measurement
techniques, whereas their comparative perspectives are presented in Fig. 12. A
qualitative comparative perspective of various noninvasive methods is
summarized in Table IV.

Figure 11: Various spectroscopy techniques for noninvasive glucose
measurement. Figure 12: Comparative Perspective of Various popular
spectroscopy techniques for noninvasive glucose measurement.

Table I: Qualitative comparison of various noninvasive glucose-level monitoring methods. Technique |  Advantages |  Disadvantages
---|---|---
Near Infra-Red (NIR) |  • The signal intensity is directly proportional to glucose molecule • The glucose detection concept would work with other interfacing substance such as plastic or glass |  • The glucose signal weak comparatively so complex machine learning model is required for interpretation • High scattering level
Mid Infra-Red (MIR) |  • The glucose molecule absorption stronger • Low scattering |  • The light has limited penetration with tissue • Noise is present in the signal so water and other non-glucose metabolites would be detected.
Far Infra-Red (FIR)/Thermal emission spectroscopy |  • Frequent Calibration is not required • Least sensitive towards scattering |  • The radiation intensity depends on temprature and substance thickness • Strong absorption with water so it is difficult to have precise glucose measurement
Raman Spectroscopy |  • Less sensitivity towards temperature and water • High specificity |  • Requirement of the laser radiation source hence it can dangerous cell for CGM • Susceptible towards noise interference so low SNR
Photo acoustic |  • Simple and compact sensor design • Optical radiation will not harmful for the tissue |  • Signal is vulnerable towards acoustic noise, temperature,motion etc. • It carries some noise from some non-glucose blood components
Polarimetry |  • The laser intensity variation will not change much the glucose prediction |  • Requirement external laser source and requires proper alignment with eye • sensitive for the change in PH and temperature
Reverse Iontophoresis |  • Based on simple enzyme based electrode system • Highly accurate as it measure glucose from interstitial fluid |  • Difficult to have proper calibration • Not so user-friendly approach due to passing of the current through skin
Fluorescence |  • Highly sensitive for glucose molecule detection due to immune for light scattering • Good sensitivity because of distinctive optical properties |  • Very much sensitive for local pH and/or oxygen, • Suffers from foreign body reaction
Bio impedance spectroscopy |  • Comparatively less extensive • Easy for CGM |  • Prone towards sweating, motion and temperature • Require large calibration period
Millimetre and Microwave sensing |  • Deep penetration depth for precise glucose measurement • No risk for ionization |  • Poor selectivity • Very much sensitive for physiological parameters such as sweating, breathing and cardiac activity
Optical Coherence Tomography |  • High resolution and good SNR • Not vulnerable for blood pressure and cardiac activity |  • Glucose value may change as per skin and motion • Suffers from tissue inhomogeneity
Surface Plasma Resonance |  • Small glucose molecule can be detected due to high sensitivity |  • Long calibration process and size is bulky • Glucose value changes with variation in temperature,sweat and motion
Time of flight and THz Time domain Spectroscopy |  • strong absorption and dispersion for glucose molecule |  • Lesser depth resolution and longer time for measurement
Metabolic Heat Conformation |  • Uses the concept of well-known various physiological parameters for glucose prediction |  • Sensitive towards variation in temperature and sweat
Electromagnetic sensing |  • low-cost and can be easily miniaturized • No risk of ionization |  • Lack of selectivity due to dielectric constant is mainly affected with other blood components • More sensitive for the slight change of temperature
Ultrasound Technology |  • Well established technology with not much harm to tissue cell • Long penetration below the skin or tissue |  • Limited accuracy with ultrasound only hence mostly used with NIR as multi-model • costly technology for measurement and not useful for CGM
Sonophoresis |  • Favourable technology as there is no side-effect to skin • Based on well known enzymatic method |  • Error prone due to environmental parameters

###  IV-A Near-Infrared (NIR) Spectroscopy

It is well known as Infrared spectroscopy (IR spectroscopy) or vibration
spectroscopy where radiation of infrared type are incident on the matter [63,
64]. Various types of IR spectroscopy is shown in Fig. 13. In general, IR
spectroscopy includes reflection, scattering and absorption spectroscopy [65].
The wave from IR absorption cause the molecular vibration and generate the
spectrum band with wavelength number in $cm^{-1}$ [66].

Figure 13: Classification of vibrational spectroscopy [67].

In this case, the light in the wavelength range of 700nm to 2500nm for Near-
infrared region is applied at the object (may be finger or ear lobe) [68]. The
light may interact with blood components and it may scattered, absorbed and
reflected [69, 70]. The intensity of received light varies as per glucose
concentration as per Beer-Lambert law [71, 72] The receiver would help to
measure the presence glucose molecule from the blood vessel [73].

Figure 14: Block diagram representation of IR spectroscopy.

####  IV-A1 Long-Wave versus Short-Wave NIR Spectroscopy

The optical detection is useful approach to have precise glucose measurement.
FIR (Far infra-red) based optical technique help to get the resonance between
OH and CH for first overtone. However, long wave NIR has good performance in
vitro testing. In similar way, the fiber-optic sensor is used along with laser
based mid-infrared spectroscopy for vitro based glucose measurement. The
continuous glucose measurement has been achieved with multivariate calibration
model for error analysis [74]. The FIR approach has limitation of shallow
penetration in comparison with short wave NIR. The short NIR would help to
detect the glucose molecule more accurately [75]. The concept of NIR
spectroscopy for glucose detection is shown in Fig. 15. The specific
wavelength of NIR spectroscopy has already been applied earlier for precise
glucose measurement using non-invasive measurement [76]. Some specific
wavelength such as 940 nm has been considered for the detection of glucose
[77]. The vibration of CH molecule has been observed at 920 nm with NIR
spectroscopy [76]. In some other works, the glucose absorption has been
validated for the range 1300 to 1350 nm and stretching of glucose has been
identified in NIR region [78, 79]. The presence of glucose component has been
measured at 1300 nm in the work [80].

Figure 15: Penetration depth various Infrared Signals in Human Skin [25, 32].

####  IV-A2 NIR Spectroscopy Based Methods

A method to estimate the non-invasive blood glucose with NIR spectroscopy
using PPG has been proposed in literature [81]. This method is performed using
NIR LED and photo detector with an optode pair. At NIR wavelengths(935nm,
950nm, 1070nm), PPG signal is obtained by implementation of analog front end
system. The glucose levels has been estimated using Artificial Neural Network
(ANN) running in FPGA. A microcontroller is used, for the painless and
autonomous blood extraction [82]. The ideal system Blood Glucose Measurement
(BGM) in which the microcontroller is used to display the blood glucose and
for the transmission of blood glucose. A remote device is used for the
tracking of the insulin pump which is needed for diabetes management. This
type of measurement [83] method uses change in the pressure of the sensitive
body part, because it generates the sound waves. The response of the photo
acoustic signals will be stronger when glucose concentration is higher. In
order to improve SNR and for the reduction of noise to transfer the signal to
the computer for further processing,the signal is then amplified. Feature
extraction and glucose estimation is estimated by photo acoustic amplitude. In
order to gather the photo acoustic signals, two pulsed laser diodes and
piezoelectric transducer is used. Utilization of the LASER makes the setup
costly and bulky.

####  IV-A3 Non-invasive Blood Glucose Measurement Device (iGLU)

In this approach, “Intelligent Glucose Meter (iGLU)” [84] has been utilized
for the acquisition of data. This device works on a combination of NIR
spectroscopy and machine learning. This device has been implemented using
three channels. It uses an Internet-of-Medical-Things platform for storage and
remote monitoring of data. In the proposed device, an NIR Spectroscopy is used
with multiple short wavelengths [85]. It uses three channels for data
collection. Each channel has its own emitter and detector for optical
detection. Then the data collection processed by a 16 bit ADC with the
sampling rate of 128 samples/second. Regression techniques is used to
calibrate and validate the data and analyse the optimized model. The data that
is stored on cloud can be used and monitored by the patients and the doctors.
Treatment can be given based on the stored data values. This is a low cost
device with more than 90 % accuracy but it does not give real time results.

####  IV-A4 Why NIR is Preferred Over other Noninvasive Approaches?

Glucose measurement has been done using various non-invasive approaches such
as impedance spectroscopy, NIR light spectroscopy, PPG signal analysis and so
on. But, apart from optical detection, other techniques have not be able to
provide the precise measurement. PPG is one of the promising alternative but
the PPG signal varies according to blood concentration [86, 87]. It may not be
useful to have precise prediction of the blood glucose. The saliva and sweat
properties vary from one person to another person. Therefore, it could not be
reliable glucose measurement method. The other spectroscopy have been also
applied for glucose measurement. However, they are not able to provide
portable, cost effective and accurate prediction of body glucose.The glucose
measurement using optical detection using long NIR wave which is not capable
to detect the glucose molecules beneath the skin as it has shallow penetration
[75]. Therefore, small NIR wave has been considered as potential solution for
real-time glucose detection [77, 88].

###  IV-B Mid Infrared (MIR) Spectroscopy

The bending and stretching of glucose molecules would be observed very well
with Mid Infrared (MIR) spectroscopy [89]. The depth of skin penetration is
very less because it tends to have larger absorption of water. This technique
helps to have ISF glucose value in vivo measurement. There are some attempts
for precise glucose measurement through saliva and palm samples.

###  IV-C Blood Glucose Level Measurement using PPG

The change of blood volume with absorption of the light from tissue has been
detected with PPG signal [87]. The change of the blood volume has been
measured using pressure pulse with help of light detector [86]. The change in
volume of blood would result as the change of light intensity hence it may not
be occur due to glucose molecule. This may result as inaccurate glucose
measurement. The difference of NIR against PPG has been shown in Fig 16. The
intelligent glucose measurement device iGLU is mainly based on principle of
NIR spectroscopy which helps to have precise glucose measurement. There have
been several work for glucose detection based on PPG signal [90]. The data
from patient body has logged to estimate the presence of glucose using PPG.
Subsequently, various machine leaning models have been used for prediction of
body glucose value [91]. The different parameters from total 70 subjects of
healthy and diabetes have been considered for the prediction using Auto-
Regressive Moving Average (ARMA) models [92]. There have been also several
other smart solutions for glucose estimation using PPG signal with intelligent
algorithms [93, 94, 95].

One of the optical based techniques is Photo-plethysmography (PPG) which is
used in advanced health care. It is non-invasive glucose measurement
technique. In NIR spectrum a sensor similar to a pulse oximeter is used to
record the PPG signal [87]. Photo transmitter and receiver is used to build
the sensor which will operate in near infrared region at 920nm. At wavelength
920nm, by measuring changes in the absorption of light, a PPG signal can be
obtained. The veins in the finger grow and contract with every heartbeat.

A method of measuring blood glucose using pulse oximeter and transmission of
the PPG glucose monitoring system is available [90]. As the glucose
concentration increases, there is decrease in the light absorbance in the
blood. The obtained signal is in the form of photo current, and for the
filtering of this signal is then changed into the measurable voltage values.
For the processing of filtered signal, lab view is used to estimate the blood
glucose level.

A system using machine learning techniques and PPG system for the measurement
of blood glucose level non-invasively has been prototyped [86]. In this model,
a PPG sensor, an activity detector, and a signal processing module is used to
extract the features of PPG waveform. It finds the shape of the PPG waveform
and the blood pressure glucose levels, the functional relationship between
these two can be obtained then.

In PPG, the change in light intensity will be varied according to changes in
blood volume. PPG signal analysis is not based on the principle of glucose
molecule detection. Hence, the system has limited accuracy [25, 32]. Fig. 16
illustrates the differences.

Figure 16: PPG Versus NIR for Non-invasive Glucose Measurement [25, 32].

###  IV-D Impedance Spectroscopy

Impedance spectroscopy (IMPS) refers to the dielectric spectroscopy [96]. The
steps of impedance spectroscopy (IMPS) is shown in Fig. 17. This technique
finds the dielectric properties of skin [97]. The current is directed through
the skin [98]. Due to directed small current at multiple wavelengths, the
impedance range is obtained [99]. The range lies between 100 Hz to 100 MHz
[100, 101]. Change in glucose concentration will reflect the change in sodium
ions and potassium ions concentration [102]. So, the cell membrane potential
difference will be changed [103]. Thus, the dielectric value will be changed
which predicts the glucose value of human body [104].

Figure 17: The Steps of Impedance Spectroscopy (IMPS).

An enzyme sensor in a flow cell has been explored for glucose measurement in
saliva [105]. Polypyrrole (PPy) supported with copper (Cu) nanoparticles on
alkali anodized steel (AS) electrode for glucose detection in human saliva is
available in [106]. The high precision level cannot be possible through these
methods as sweat and saliva properties vary according to person. Hence, this
approach is not suitable for glucose measurement in smart healthcare.

###  IV-E Raman Spectroscopy

Due to the interaction of light with a glucose molecule, the polarization of
the detected molecule will change [107]. In this technique, oscillation and
rotation of molecules of the solution are possible through the incident of
LASER light [108]. The vibration of the molecule affects the emission of
scattered light [109]. Due to this principle, blood glucose concentration can
be predicted as [110]. This technique provides more accuracy with compared to
infra-red spectroscopy technique [111]. There has been several research based
on Raman spectroscopy to have precise glucose measurement. The validation has
been also carried out on using vivo testing. Fig. 18 presents basic framework
of Raman spectroscopy, whereas Fig. 19 presents its usage for noninvasive
glucose measurement.

Figure 18: Building blocks of Raman spectroscopy. Figure 19: Noninvasive
glucose measurement using Raman spectroscopy.

###  IV-F Time of Flight and THz Domain

The blood glucose estimation is adopted though Time of Flight (TOF)
measurements for vitro testing [112]. The short pulse of laser light is
inserted in the sample for photon migration measurement. This photon will
experience scattering and absorption phenomenon while traveling from the
sample. The optical analysis of the photons would be useful for precise
glucose measurement.

###  IV-G Photo Acoustic Spectroscopy

Photoacoustic spectroscopy refers to the photoacoustic effect for the
generation of the acoustic pressure wave from an object (refer Fig. 20) [113].
In this spectroscopy technique, the absorption of modulated optical input
provides the estimation of blood glucose detection [114]. High intense optical
light is absorbed by an object according to its optical conditions [115]. This
process provided excitations of particular molecules according to its resonant
frequency [116]. The absorbed light is considered as heat which provides
rising in localized temperature and thermal expansion of the sample [117]. The
expansion in volume generates pressure in acoustic form [118]. The generated
photoacoustic wave can be used to predict the glucose concentration through
specific excited wavelengths which are resonant for the vibration of glucose
molecules [119]. At the specific resonance frequency, the glucose molecule
changes own characteristic. This change is in the acoustic waveform [120]. In
previous work, 905 nm wavelength optical light is used for excitation [121,
122].

Figure 20: Photo acoustic spectroscopy.

###  IV-H Capacitance Spectroscopy

In the capacitance spectroscopy technique, inductor stray capacitance varies
according to body capacitance (Fig. 21) [123]. The body capacitance is used to
estimate body glucose concentration [124]. Flexible inductor based sensor
follows the coupling capacitance principle for body glucose detection. In this
technique, there is not any interaction between the inductive sensor and body
skin through the current [125]. This is the advantage of the impedance
spectroscopy technique. The stray capacitance of the inductive sensor will
vary according to body glucose. In this technique, the effect of fat and
muscles will be negligible with respect to body glucose [126].

Figure 21: The typical steps of capacitance spectroscopy.

###  IV-I Surface Plasmon Resonance (SPR)

The Surface Plasmon Resonance (SPR) utilizes electron oscillation approach at
dielectric and metal interface for glucose sensing [127]. It detects mainly
the change in refractive index before as well as after the analytes
interaction. The optical fiber based SPR has been used for point of care
measurement for glucose due to its portability.

###  IV-J Radio Frequency (RF) Technique and Microwave Sensing

In the RF technique, the variation in the s-parameters response reflects the
change in blood glucose [128, 129]. Fig. 22 shows typical steps of this
technique. The response is determined through the antenna or resonator [130,
131]. They follow the changes in dielectric constant value through the
transmission [132]. The change in dielectric constant can be found as the
change in resonance frequency spectrum through the antenna or resonator [133,
134]. The dielectric of blood varies according to blood glucose concentration.
The human finger is an appropriate measurement object but there are many
factors that play a cardinal and dominant role in the accuracy of measurement
and repeatability. These are; the skin thickness, fingerprints, the applied
pressure by the fingertip during measurement and positioning of a finger on
the sensor [135].

Figure 22: Glucose measurement using RF sensing technique.

###  IV-K Ocular Spectroscopy

In the Ocular Spectroscopy technique, glucose concentration is measured
through the tears. A specific lens is used to predict the body glucose
concentration [136]. A hydrogel wafer is deposited to the lens. This wafer is
prepared by boronic acid with 7 $\mu m$ thickness. The wafer is deposited on
lens and then optical rays are inserted on the lens. Then reflected light will
change its wavelength. Change in wavelength will refer to a change in glucose
concentration in tears.

###  IV-L Iontophoresis

In the Iontophoresis or Ionization technique, a small electric current passes
through the skin diffusively. Three electrodes are used for the same [137]. A
small potential is applied through the electrodes to the different behaviour
electrodes. During this process, glucose is transferred towards the cathode.
The working electrode can have the bio-sensing function by the generation of
current during applied potential through electrodes. This biosensor determines
passively body glucose. The measurement is possible through wrist frequently
[138].

###  IV-M Optical Coherence Tomography

The Optical Coherence Tomography technique is based on the principle followed
by reflectance spectroscopy. In this technique, low coherent light is excited
through the sample (sample is placed in an interferometer). In an
interferometer, a moving mirror is placed in reference arc. A photodetector is
placed on another side and it detects the interferometric signal. This signal
contains backscattered and reflected light. Due to this process, we could get
high-quality 2-D images. The glucose concentration increases with the
increment of the refractive index in interstitial fluids. Change in the
refractive index indicates the change in the scattering coefficient [107]. So,
the scattering coefficient relates to glucose concentration indirectly.

###  IV-N Polarimetry

The Polarimetry technique is commonly used in a clinical laboratory with more
accuracy. The optical linear polarization-based technique is used for glucose
monitoring [139]. This technique is usually based on the rotation of vector
due to thickness, temperature and concentration of blood glucose. Due to the
process of prediction of glucose, the polarized light is transmitted through
the medium containing glucose molecule. Due to high scattering through the
skin, the depolarization of beam is possible. To overcome this drawback, a
polarimetric test has been done through the eye. The light passes through the
cornea. This technique is totally unaffected due to rotation of temperature
and pH value of blood [140].

Figure 23: Non-invasive glucose measurement using Polarimetry.

###  IV-O Thermal Emission Spectroscopy

The Thermal Emission Spectroscopy based technique is based on the naturally
generated IR wave from the body. The emitted IR waves will vary according to
body glucose concentration. The usual mid-IR emission from tympanic membrane
of human body is modulated with tissue emitting. The selectivity of this
technique is same as the absorption spectroscopy. Due to this technique,
glucose can be determined through the skin, fingers and earlobe. This
technique is highly precise and accurate for glucose measurement [141]. It
could provide the useful solution which is precise and acceptable at clinical
with measurement of thermal emission from tympanic membrane.

###  IV-P Ultrasound

The Ultrasound method is based on low frequency components to extract the
molecules from skin similar as reverse iontophoresis method [142]. It is also
alike sonophoresis and has larger skin permeability than reverse
iontophoresis. Few or several tens of minutes of ultrasound exposure are
required to pull glucose outward through the skin. There are few attempts for
such technology and there is not any commercial device with such type of
technology.

###  IV-Q Metabolic Heat Conformation (MHC)

The Metabolic Heat Conformation (MHC) method helps to measure the glucose
value with metabolic heat and oxygen level along various physiological
parameters considerations [143]. The mathematical model for metabolic energy
conservation has been modified by several physiological parameters
consideration such as pulse rate, oxyhemoglobin saturation, heat metabolic
rate and the blood flow volume. This method has shown good reproducibility and
decent accuracy in humans.

###  IV-R Fluorescence

The Fluorescence technique is based on the excitation of blood vessels by UV
rays at particular specified frequency ranges [144]. This is followed through
the detection of fluorescence at a specified wavelength. The sensing of
glucose using fluorescence through tear has been done by the diffraction of
visible light. At 380 nm, an ultraviolet LASER was taken for excitation
through the glucose solution medium. Fluorescence was estimated which is
directly related to glucose concentration. In this technique, the signal is
not affected by variation in light intensity through the environment.

###  IV-S Kromoscopy

The Kromoscopy technique uses the response from various spectroscopic of NIR
light with four different detectors over different wavelength [145]. It
employs the multi-channel approach with overlapped band-pass series filters to
determine the glucose molecule. In this method, the radiation of IR are
imparted on the sample and this will be divided among four detectors with
band-pass filter. Each detector will detect the light of the similar structure
of the tissue. Subsequently, the complex vector analysis has been utilised to
measure the glucose concentration.

###  IV-T Electromagnetic Sensing

In the Electromagnetic Sensing method, the variations in blood sample
conductivity is observed by change in blood glucose concentration [146]. The
alternation of electric field would be measured by electromagnetic sensor
whenever there will be change in blood glucose concentration. This method
utilizes the dielectric parameter of the blood samples. The frequency range
for electromagnetic sensing is in the range of 2.4 to 2.9 MHz. The glucose
molecule has maximum sensitivity at particular optimal frequency for given
temperature of the medium.

###  IV-U Bioimpedance Spectroscopy and Dielectric Spectroscopy

It is useful to measure the variation of the blood glucose with help of
conductivity and permittivity from red blood cells membrane [147]. The
spectrum of bioimpedance spectrum is measured from 0.1 to 100 MHz frequency
range. It help to find the resistance with passing through electric current
which is flowing from human biological tissue. The change of plasma glucose
would allow the changes in potassium and sodium to have the change in
conductivity of the membrane of the red blood cell. The multisensor approach
is usually incorporated with this spectroscopy in order to measure sweat,
moisture, movement and temperature for precise glucose measurement.

###  IV-V Reverse Ionospheresis

The small DC current is passed from anode to cathode on the skin surface to
have small interstitial fluid (ISF). Iontophoresis is employed for ionized
molecules penetration at skin surface by such low current [148]. The electric
potential is passed from anode and cathode to electroosmotic flow across the
skin. This would allow to extract the molecules through skin whereas the the
molecules of glucose are moved towards the cathode. The enzyme method helps to
sense the concentration of glucose molecules through oxidation process. the
method has very widely accepted and has good potential to measure accurate
glucose value.

###  IV-W Sonophoresis

The Sonophoresis technique is based on the cutaneous permittivity of the
interstitial fluid (ISF) [149]. It also uses enzyme method for glucose
measurement. The low frequency ultrasound wave has been applied in order to
have glucose molcules at the skin surface. The cutaneous permittivity of the
ISF is increased to enable glucose at the epidermis surface. The contraction
and expansion occurs in stratum corneum that subsequently opens the ISF
pathway. There has been some attempts with this method for glucose detection
but it has been observed that it could be helpful in drug delivery in stead of
glucose measurement.

###  IV-X Occlusion Spectroscopy

The Occlusion spectroscopy based methods depend on the concept of light
scattering which is of inverse proportion of glucose concentration [150]. The
flow is ceased for few seconds by applying pressure with pneumatic cuff. The
volume of blood would change due to pulse generated from the pressure
excursion. The light is transmitted through the sample and the variation of
the intensity of in a received light defines the glucose concentration. The
momentary blood flow cessation helps to get higher SNR value of the received
signal. Hence, the sensitivity for glucose detection would be increases with
good robustness for accurate glucose measurement.

###  IV-Y Skin Suction Bluster Technique

The Skin Suction Bluster technique uses the concept of blister generation
through vacuum suction over limited skin area [151]. The glucose measurement
is performed on fluid which is collected from the blister. It has lower
glucose molecules than plasma but it is well enough to have the glucose
measurement. This method has low risk of infection, painless and well-
tolerated. It is actually useful to measure HbA1c value which represents three
month average glucose value.

###  IV-Z Multimodal approach based measurement

A two modal spectroscopy combining IMPS and mNIR spectroscopy is explored for
high-level reproducibility of non-invasive blood glucose measurement [152].
These two techniques are combined to overcome the limitation of individual
employed technique [153]. Impedance spectroscopy based circuit measures the
dielectric constant value of skin or tissue through RLC resonant frequency and
impedance to predict glucose level [154]. To improve the accuracy of NIR
spectroscopy, mNIR spectroscopy technique is used. In this technique, three
wavelengths 850 nm, 950 nm and 1300 nm are used [155]. For precise and
accurate measurement, IMPS and mNIR are joined by an ANN (Artificial Neural
Network) through DSP processor [80]. Therefore multimodel approaches have been
explored for precise glucose measurement in the literature [joo2020vivo,
feng2019multi].

Figure 24: Multimodal IC based non invasive glucose measurement. Table II: Approaches Comparison with Noninvasive Works [25, 32]. Works | Spectroscopy | Spectra | Specific | Measurement | Linearity
---|---|---|---|---|---
| technique |  | wavelength | range | (%)
Singh, et al. [160] | Optical | - | - | 32-516 mg/dl | 80
Song, et al. [80] | Impedance and Reflectance | NIR | 850-1300 nm | 80-180 mg/dl | -
Pai, et al. [161] | Photoacoustic | NIR | 905 nm | upto 500 mg/dl | -
Dai, et al. [162] | Bioimpedance | - | - | - | -
Beach, et al. [163] | Biosensing | - | - | - | -
Ali, et al. [88] | Transmittance and Refraction | NIR | 650 nm | upto 450 mg/dl | -
Haxha, et al. [77] | Transmission | NIR | 940 nm | 70-120 mg/dl | 96
Jain, et al. [164] | Absorption and Reflectance | NIR | 940 nm | 80-350 mg/dl | 90
Jain, et al. (iGLU 1.0) [32, 28] | Absorption and Reflectance | NIR | 940 and 1300 nm | 80-420 mg/dl | 95
Jain et al. (iGLU 2.0) [29, 25] | Absorption and Reflectance | NIR | 940 and 1300 nm | 80-420 mg/dl | 97
Table III: Statistical and Parametrical Comparison with Noninvasive Works [25, 32]. Works | R | MARD | AvgE | MAD | RMSE | Samples | Used | Measurement | Device
---|---|---|---|---|---|---|---|---|---
| value | (%) | (%) | (mg/dl) | (mg/dl) | (100%) | model | sample | cost
Singh, et al. [160] | 0.80 | - | - | - | - | A&B | Human | Saliva | Cheaper
Song, et al. [80] | - | 8.3 | 19 | - | - | A&B | Human | Blood | Cheaper
Pai, et al. [161] | - | 7.01 | - | 5.23 | 7.64 | A&B | in-vitro | Blood | Costly
Dai, et al. [162] | - | 5.99 | 5.58 | - | - | - | in-vivo | Blood | Cheaper
Beach, et al. [163] | - | - | 7.33 | - | - | - | in-vitro | Solution | -
Ali, et al. [88] | - | 8.0 | - | - | - | A&B | Human | Blood | Cheaper
Haxha, et al. [77] | 0.96 | - | - | - | 33.49 | A&B | Human | Blood | Cheaper
Jain, et al. [164] | 0.90 | 5.20 | 5.14 | 5.82 | 7.5 | A&B | Human | Blood | Cheaper
Jain, et al. (iGLU 1.0) [32] | 0.95 | 6.65 | 7.30 | 12.67 | 21.95 | A&B | Human | Blood | Cheaper
Jain et al. (iGLU 2.0) [29] | 0.97 |  4.86 | 4.88 | 9.42 | 13.57 | Zone A | Human | Serum | Cheaper

##  V Post-processing and calibration techniques for non-invasive Glucose-
Level measurement

This Section presents various post-processing and calibration techniques which
are deployed in various systems or frameworks for noninvasive glucose-level
monitoring.

###  V-A Post processing and calibration techniques

Various calibration processes have been applied for a high level of accuracy
and noise reduction from received signal. These post-processing techniques are
used to design the model for errorless continuous monitoring [165, 166].

####  V-A1 Noise Minimization and Signal Conditioning

The coherent averaging technique has been adapted to minimize the variance of
random noise [167]. The impact of noise is minimized with averaging of N
number of individual samples coming from the continuous frames [168]. Frames
in the maximum count have been chosen for averaging to have SNR improvement
[169]. This proposed coherent averaging has been used frequently through
MATLAB and coherent averaged signal acquired. Golay code has been proposed as
calibration of measured data. The filtering or cancellation of unusual
measured data has been achieved through the implementation of Golay code [170,
171, 172].

####  V-A2 Computation Models for glucose Estimation

The regression model of regularized least square is proposed by several
researchers for measurement [173]. The estimated value is computed from
photoacoustic signals. These photoacoustic signals are used to calibrate for
estimation of glucose concentration [174]. This can be possible through multi-
variable linear regression model [175]. With the objective of high-level
accuracy, a post-processing SVM technique is proposed [176]. Support vector
machine is a better option of correct measurement in glucose monitoring system
[177]. Artificial Neural Network (ANN) has also been proposed for data
combining [178]. The measured data from multiple techniques are combined
through the proposed neural network model [179]. This artificial neural
network has been implemented in DSP processor [180, pancholi2018portable].
This proposed data interpretation model has been used for combining and
calibrating of data for final estimated glucose concentration [181].

###  V-B Metrics for Model Validation

The calibration method is used to have precise blood glucose estimation for
measurements [182]. The obtained glucose concentration values are used to
compare with conventionally measured glucose concentrations [183]. The Clarke
error grid analysis has been considered maximum measurement for analysis which
is used to check the performance of any device for accuracy measurement [184].
The process flow is represented in Fig. 25.

Figure 25: Representation of Metrics for model Validation.

###  V-C Clinical Accuracy Evaluations using Clarke error grid analysis

The Clarke Error Grid has been analysed as benchmark tool to examine the
clinical precision for biomedical application. It has prediction of point as
well as rate accuracy, and it amends for physiologic time lags inherent for
measurement of body glucose. The exploitation of the Clarke error grid
modelling will significantly make easy the development and refinement of a
precise biomedical device. In 1970, this technique was developed by C.G. Clark
to identify the accuracy of the clinical trials which helps to find the
precision of estimated blood glucose with blood glucose value through the
conventional method. A description of the Error Grid Analysis came into view
in diabetes care in 1987. The grid is divided with five different zones mainly
as zone A, zone B, zone C, zone D, and zone E. If the values residing in
either zone A or zone B then it signifies satisfactory or accurate prediction
of glucose results according to Beckman analyzer. The zone C values may prompt
gratuitous corrections which may lead to a poor outcome. If the values are
under zone D which actually defines a hazardous failure to sense. Zone E
reflects the “erroneous treatment” [185].

Figure 26: Clarke error grid analysis.

##  VI Consumer Products for Glucose-Level Measurement

There have been several non-invasive glucometer at market(such as Freestyle
Libre sensor, SugarBEAT from Nemaura medical) which used for proper
medication. They would be like skin-patch with daily disposable feature and
adhesive to have the continuous glucose monitoring. Most of the consumer
products fail to provide precise glucose measurement and hence they are much
popular for diabetes management. There are some products as DiaMon Tech,
glucowise, glucotrack, glutrac and CNOGA medical device. Glutrac is smart
healthcare device but it has accuracy issues for the blood glucose
measurement. It has higher cost while precision is still not acceptable. The
non-invasive stripless device known as Omelon B-2 has been used for the CGM.
The fluorescent technique based Glucosense has been made for contonuous
monitoring of the glucose value. The flexible textile-based biosensor has
developed from Texas University to measure the glucose level. All the
available device have accuracy issues and considerable higher cost.

###  VI-A Wearable versus Non-wearable for Glucose Monitoring

The glucose monitoring have been attempted using non-wearable and wearable
solutions in the literature. Most of the non-wearable approaches are based on
various spectroscopy such as photoacoustic spectroscopy, Raman spectroscopy
etc. The implantable devices are of semi-invasive type and are mainly of
biosensors in nature. Sweat patches, Glucowatch and Smart contact lenses are
of wearable devices category. LifePlus has developed non-invasive and wearable
device for CGM purpose and it is under consideration for commercialization.
Most of non-invasive device are wearable and helpful for frequent glucose
measurement. The continuous glucose monitoring would be more acceptable if
they could measure the blood glucose values in day to day life. Therefore, the
wearable devices are more state of art solutions then non wearable devices.

###  VI-B Noninvasive Glucose Measurement Consumer Products

There are variety of products such as GlucoTrack®, glucometer from Labiotech
[186], and similar available solutions have accuracy issues and cost is also
high. The glucowise is another non-invasive device for continuous glucose
measurement from Medical Training Initiative (MTI) ™. The Raman scattering
spectroscopy based non-invasive solution is also developed by 2M Engineering
[187]. These devices are not much popular because of their cost and precision.
Further for the high level of accuracy of glucose measurement, Glucotrack™ has
been developed by integrity applications Ltd. [188]. This non-invasive glucose
monitoring device employed three consecutive ultrasonic spectroscopy, thermal
emission and electromagnetic techniques. This device is highly precise and
accurate because of a combination of three techniques [189]. A comparative
perspective of various consumer products for noninvasive glucose measurement
has been summarized in Table VI-B.

Table IV: A Comparative perspective of a selected consumer products for noninvasive glucose measurement. Company |  Device |  Technology |  Object |  Summary |  Snapshot
---|---|---|---|---|---
Cygus Inc. (USA) |  GlucoWatch G2 Biographer |  Reverse iontophoresis |  Wrist skin |  It would be worn as watch is used with disposable component, autosensor which is to be attached at back of biographer that contact with the skin to provide frequent glucose monitoring |
CNOGA (Israel) |  Combo glucometer |  Tissue photography analysis |  Finger |  On basis of tissue photography analysis from fingertip capillaries, this device can analyze various bio parameters in very short time |
Pendragon Medical (Switzerland) |  Pendra |  Impedance Spectroscopy |  Wrist Skin |  It helps to measure the glucose with sodium transport of erythrocyte membrane, The change of fluxes of transmembraneous sodium occur due to impedance field which is detected by device to generate final glucose value |
OrSense Ltd. (Israel) |  OrSense NBM-200G |  Occlusion Spectroscopy |  Fingertip skin |  It is based on optical concept on finger which is attached to a ring-shaped sensor probe. The probe has red/near-infrared RNIR spectral region light source as well as detector. It has pneumatic cuffs which generates systolic pressure to produce optical signal for glucose monitoring |
C8 Medisensors (USA) |  C8 Medisensor Glucose detector |  Raman Spectroscopy |  Fingertip skin |  This technique is based on monochromatic light source passes through skin where scattered light is detected. The generated colors from Raman spectra helps to exact chemical structure of glucose molecule |
Integrity Applications (Israel) |  Glucotrack |  Combination of Electromagnetic, ultrasonic and Thermal |  Ear lobe tissue |  In this device, three different techniques are used concurrently to increase the accuracy and precision |
Tech4Life Enterprises (USA) |  Non invasive glucometer |  Infra red Spectroscopy |  Finger |  It is helpful for Hyperglycemia or Pre-Diabetic patients which allow for regular monitoring of precise blood glucose measurement at every 30 seconds |
MediWise Ltd. (United Kingdom) |  Glucowise |  Radio Wave Spectroscopy |  Forefinger skin/Earlobe |  This non invasive wireless device can measure glucose concentration in very short time. It is based on electromagnetic waves of specific frequencies for blood glucose detection. It uses a thin-film layer of metamaterial which increases the penetration for precise glucose measurement |
Nemaura Medical (united Kingdom) |  SugarBeat |  Reverse iontophoresis |  Arm,Leg and adbomen |  This has been proved accurate device, pain-free continuous blood glucose monitoring. SugarBEAT® provides real-time, needle-free glucose measurement. Generally, it needs one time finger-prick test for calibration. One time finger prick is used when new patch is required to insert |
Abott Ltd. (USA) |  Free Style Libre |  Glucose oxidase method |  Fore-arm skin |  It uses enzyme glucose sensing technology for the detection of glucose levels through interstitial fluid Glucose oxidase method is applied through sensor where electrical current proportional to the glucose concentration and glucose can be measured. |
C8 Medisensor |  Non invasive glucose monitor |  Raman Spectroscopy |  Fore arm skin |  Raman spectroscopy technique based this device can detect glucose in blood through returning spectrum from the skin |

##  VII Glucose Level Controls Approaches and Consumer Products

Various models have been developed for diet control using various parameters
for glucose-insulin balance. The parameters are mainly includes net hepatic
glucose balance, renal excretion rate, glucose absorption rate and peripheral
glucose utilization for the glucose consumption prediction for the diabetic
patients. These are useful parameters to calculate the glucose level by proper
insulin dosage along with scheduled diet plan. Therefore, the glucose-insulin
control model was designed to balance glucose insulin level in the body for
diabetes persons using proper medication.

###  VII-A Glucose Controls Approaches

The mathematical models for insulin delivery have been presented to determine
the coefficients of blood regulation. The model has been proposed for insulin
secretion with glycemic profile for type 2 diabetic person [190, 191]. The
non-linear model is developed using differential equation with delay model
with help of non-diabetic subjects [192]. Most poplar “Uva/Padova Simulator”
was also explored which was approved from FDA to have the proper clinical
trials. The parameter are extracted with type 1 diabetic virtual patients
[193]. The intravenous test for glucose tolerance with Hovorka maximal model
has explored for non-diabetic subjects [194]. The samples from type 1 diabetic
persons were collected to explain the model with help of time monitoring. The
model is proposed mathematically for blood glucose value prediction in the
postprandial mode for type 1 diabetes patients [195, 196]. The mathematical
model for glucose-insulin balance for longer period is explored using two days
clinical information [197]. A algorithm was developed for T1DM patient meal
detection for the purpose of frequent glucose measurement. The work has
integrated bolus meal mathematical model for glucose-insulin delivery model
[198]. Diabetic and healthy people were considered to acquire the values for
the variable state dimension algorithm. The diet plan was examined in the
absence of meal profile to have the glycemic profile balance, an intelligent
PID controller (iPID) was developed to type 1 diabetic person [199, 200].

###  VII-B Glucose Controls Consumer Products

Type-1 diabetic patients aren’t able to produce insulin. Insulin is a hormone
that can balance body sugar (glucose) which is a prime source of energy that
obtains from carbohydrates. If anybody has type 1 diabetes, it is necessary to
be ready for insulin therapy. Insulin may be injected by self-injection,
patients who take multiple injections daily of insulin may also think about
use of an insulin pump. An insulin pump gives short-acting insulin all day
long continuously. The insulin pump replaces the requirement of long-acting
insulin. A pump also substitutes the requirement of multiple injections per
day along with continuous insulin infusion and also serves to improve the
glucose levels. Various types of insulin pumps are already available in the
market as consumable product mainly as Animas, Medtronic, Roche, Tandem and
Omnipod insulin pump are consumables. These insulin pumps are advanced to each
other in terms of their upgraded features. A comparative perspective of a
selected state of art approach for glucose measurement to have better
glyncenic profile control is presented in Table VII-B.

Table V: A comparative perspective of a selected state of art approaches for glucose measurement Work |  Technology |  Object |  Findings |  Observation
---|---|---|---|---
[86] |  photoplethy-
-smography (PPG) |  Finger |  It helps to extract the features of PPG signal through machine learning models to estimate Systolic and diastolic blood pressure and blood glucose values |  machine learning models applied where random forest technique has best prediction results as $R^{2}_{SBP}$ = 0.91, $R^{2}_{DBP}$ = 0.89 and $R^{2}_{BGL}$ = 0.90. CEG has 87.7% observation in Zone A, 10.3 % in Zone B, and 1.9% in Zone D
[201] |  mid-infrared attenuated total reflection (ATR) spectroscopy and trapezoidal multi-reflection ATR prism |  oral mucosa inner lips |  Using a multi-reflection prism brought about higher sensitivity, and the flat and wide contact surface of the prism resulted in higher measurement reproducibility & spectra around 1155 cm$-1$ for different blood glucose levels for fasting and before fasting |  the coefficient of determination $R^{2}$ is 0.75. The standard error is 12 mg/dl, and all the measured values are in Region A
[202] |  Optical Coherence Tomography |  Fingertip |  It measures the optical rotation angle and depolarization index of aqueous glucose solutions with low and high scattering, respectively. The value of angle increases while depolarization index decreases with glucose value increases |  The correlation factor has a value of $R^{2}$ 0.9101, the average deviation is found around 0.027.
[203] |  Contactlenses fluoresence |  Tears |  The fabrication of a soft, smart contact lens in which glucose sensors, wireless power transfer circuits, and display pixels to visualize sensing signals in real time are fully integrated using transparent and stretchable nanostructures |  The usage of smart and soft lens would provide the wireless operation at real-time for glucose monitoring in tears
[204] |  transmission spectroscopy |  Sliva |  After completely absorbing the sufficient amount of saliva on the strip, the sample would reach detection zone via paper microfluidic movement and enzymatic reaction between GOx and salivary glucose would initiate a pH change, resulting in a change in strip color that was recorded by using RGB detector on the handheld instrument which helps for glucose detection |  The developed biosensor had a wide detection range of detection between 32- and 516-mg/dL glucose concentration while the sensitivity of detection was 1.0 mg/dL/count at a limit of detection (LOD) of 32 mg/dL within a response time of 15 s
[205] |  impedance spectroscopy (IMPS) and multi-wavelength near-infrared spectroscopy (mNIRS) |  Left Handand wrist Hand |  IMPS and mNIRS use the indirect dielectric characteristics of the surrounding tissue around blood and the optical scattering characteristics of glucose itself in blood, respectively, the proposed IC can remove various systemic noises to enhance the glucose level estimation accuracy |  mean absolute relative differences (mARD) to 8.3% from 15.0% of the IMPS and 15.0–20.0% of the mNIRS in the blood glucose level range of 80–180 mg/dL. From the Clarke grid error (CGE) analysis, all of the measurement results are clinically acceptable and 90% of total samples can be used for clinical treatment
[84] |  NIR Spectroscopy |  Fingertip |  short NIR waves with absorption and reflectance of light using specific wavelengths (940 and 1,300 nm) has been introduced |  The Pearson’s correlation coefficient (R) is 0.953 and MAD is 09.89 which is RMSE 11.56
[206] |  Microwave Detection |  earlobe |  The absorption spectrum of microwave signal helps to measure using two antenna. The sine wave of 500 MHz is for blood glucose measurement. |  It can measure blood glucose from 0 to 500 mg/dl with step size of 200 mg/dl used for the experiment for testing the resolution. It obtained 0.5226 mean standard deviation while the minimum value of standard deviation is 0.04119.
[94] |  PPG |  Finger |  The prediction of blood glucose was with machine-learning using a smartphone camera. First the invalid data was separated and The system did not require any type of calibration |  The device was able to measure glucose only 70-130 mg/dl range. The results show accuracy of 98.2% for invalid single-period classification and and the overall accuracy is 86.2%.
[46] |  MEMS |  Finger |  It is minimally invasive technique known as e-Mosquito which extracts blood sample with shape memory alloy (SMA)-based microactuator. It considered as first ever wearable device which performs the automatic situ blood extraction and performs the glucose analysis. |  The method provided linear correlation ($R^{2}$ = 0.9733) between standard measurements and the e-Mosquito prototype.
[207] |  Visible NIR |  Wrist |  The paper developed biosensor which helps to exploit pulsation of arterial blood volume from the wrist tissue. The visible NIR spectroscopy was used for reflected optical signal to estimate blood glucose. |  The correlation coefficient (Rp) value after averaging all observation is 0.86, whereas the standard prediction error is around 6.16 mg/dl.
[201] |  mid-infrared attenuated total reflection (ATR) spectroscopy |  inner lip mucosa |  Novel optical fiber probe was introduced using multireflection prism with ATR spectroscopy. The sensitivity increases with the number of reflections while measurement reproducibility was higher due to prism’s wide and flat & wide contact surface. |  The experimental results reveals the glucose signature at various spectra between fasting state and after the glucose injection. The plot for calibration defines peak for absorption at 1155 $cm^{-1}$ which has glucose measurement error less than 20%
[chowdhury2016noninvasive] |  modulated ultrasound and infrared technique |  Finger |  the MATLAB toolbox is used with Fast Fourier Transform (FFT) for blood glucose extraction. The random blood glucose level test and oral glucose tolerance test was done for the human subjects for performance measurement |  The RMSE value of noninvasive and invasive measurement from both tests 28.20 mg/dl and 23.76 mg/dl. The pearson correlation coefficient was 0.85 and 0.76, respectively. At the same time MSE was 17.76 mg/dl and 15.92 mg/dl.

##  VIII Glucose-Level Measurement and Controls - IoMT Perspectives

The practical and sustainable mechanisms are the prime factors of smart and
automated healthcare system. These are being optimized to support the
population migration and quality of life in smart cities and smart villages
[208, 209]. The features of smart healthcare system are continuous monitoring
for critical care, ambient intelligence and quality of service for proper
point of care mechanism [210, 211]. The non-invasive and precise glucose
measurement is requirement for diabetic person and would also needed to store
the information using IoMT for proper treatment [26]. The traditional method
for glucose measurement has limited capability and is not able to assist the
remotely located healthcare provider. The diabetic person would like to
monitor their glycemic profile frequently in a day with support of storing at
cloud server. The smart health care system would allow the point of care
treatment for diabetes person with frequent monitoring.

The internet of Medical Things (IoMT) has allowed to connect the patients with
doctors remotely for rapid treatment and special assistance using smart
healthcare [208]. The continuous monitoring of vital parameters have provided
to awareness about the diet plan and routine activity management with
contemporary healthcare consumers devices. With the active support of remote
healthcare solution, the smart healthcare has potential to ameliorate the
quality of service at reduced cost. The smart sensors would capture the
patient data continuously and help to store the data on cloud data centre. It
is also useful for the analysing the data and easy exchange of the information
through mobile applications to doctors as well as patients. The healthcare
Cyber-Physical System (H-CPS) has been used successfully to address the
various challenges of healthcare sector with intelligent algorithms.

The continuous glucose monitoring would certainly help the diabetic patients
to plan their diet for the purpose of glucose control. The solution should be
precise, low cost and easy to operate for rapid diagnosis [32, 28]. The serum
glucose would always consider as accurate than capillary measurement.
Therefore, the rapid serum glucose measurement solution with continuous
monitoring is desired for the smart healthcare. The novel serum glucometer is
portable device and is also integrated with IoMT to store the glucose values
continuously at cloud. It would be useful for the healthcare provider to track
the health records of remote located diabetes person. The smart healthcare
management of continuous glucose measurement is defined in Fig. 27.

Figure 27: Blood glucose diagnosis and Control in smart health care system.

A detailed example of a closed-loop system that presents glucose-level
monitoring and insulin release to control it is illustrated in Fig. 28 [2].
This IoMT framework can provide a better solution for evaluation of insulin
doses through the closed-loop automated insulin secretion diabetes control.
Such an integrated IoMT framework can be implemented to diagnose and for the
treatment of diabetic patients in terms of controlling their blood glucose
level in smart healthcare and be effective in smart village and smart cities
for healthcare with limited medical personnel.

Figure 28: A closed-loop automated insulin secretion diabetes control system
in an IoMT framework [2].

The security and privacy issues of the medical devices are paramount aspect in
any IoT network. The hardware security of wearable device is very crucial
because control actions mainly occur in wireless media. The security
vulnerabilities are defined for glucose measurement device and its control are
shown in Fig. 29. The devices security are important due to connected health
system in an insecure and unreliable IoMT framework [212]. The integrity of
useful medical information is also crucial security aspect of smart
healthcare. All patients medical records are stored over the server therefore
the security of such data are also really important. The controlled access
with proper authentication is required to have secure monitoring with proper
patient treatment.

Figure 29: Our Long-Term Vision of Security-Assured Non-invasive Glucose-Level
Measurement and Control through our Proposed iGLU.

##  IX Short-Comings of Existing Works and Open Problems

This Section outlines the shortcomings and discusses some open problems of
glucose level measurements and control.

###  IX-A Limitations of the Existing Approaches and Products

  1. 1.

Photoacoustic spectroscopy has been implemented for glucose measurement. Real-
time testing and validation have not been done from human blood. The
artificial solution was prepared in the laboratory for glucose measurement.
The prototype module with LASER and corresponding detector is costly and at
the same time requires considerable bigger area and does not provide portable
solution. Therefore, it is not much popular solution for continuous glucose
monitoring.

  2. 2.

Raman spectroscopy is a nonlinear scattering which occurs when monochromatic
light interacts with a certain sample. Raman spectroscopy based solution is
applicable for a laboratory test and also occupies the significant larger
area. Hence, the system based on this approach will not be applicable for
frequent glucose measurement.

  3. 3.

The retina based glucose measurement is also one of the alternate non-invasive
glucose detection approach, data has also been collected through retina for
glucose measurement. Such technique is not useful for the glucose measurement
all the time.

  4. 4.

In case of bio-capacitance spectroscopy, the slight difference in placing the
sensor at the same location might affect the output of the sensor. Effect of
pressure on the sensor, body temperature and sweat on the skin may also affect
the output of the sensor.

  5. 5.

Glucose detection is performed with the impedance spectroscopy (IMPS) by
electrodes connection to the skin which is affected with skin. The accuracy is
always an issue as the saliva and sweat could change for each individual and
that may reflect to the precision of glucose. Therefore, this technique is not
best for reliable glucose measurement in smart healthcare.

  6. 6.

PPG signal has been used to extract features for blood glucose level
prediction. But the PPG may be precise blood glucose measurement technique
where the output value would vary according the blood volume only. Therefore,
the glucose molecule has not been detected precisely in the blood sample using
this technique.

###  IX-B The Open Problems in Non-invasive Glucose Measurement

There are lots of challenges for commercialization of non-invasive glucose
measurement device. But, some open problems have been discussed which are
prime challenges for precise non-invasive glucose measurement. These
challenges have been represented in Fig. 30. The precise glucose measurement
of hypoglycemic patient and long-time continuous glucose measurement without
instantaneous error are the open problems which are focussed by the
researchers recently.

Figure 30: Open Challenges in Noninvasive Glucose-Level Measurement.

  * •

The effect of blood pressure, body temperature and humidity have not been
considered in the literature which affect the values of glucose measurement.

  * •

The cost effective and portable solution of continuous glucose measurement
device has also not been addressed properly.

  * •

The accurate glucose measurement has been also been open challenge for full
rage from 40 mg/dl to 450 mg/dl.

  * •

The effective integration of glucometer with internet of medical things for
continuously data logging to the cloud has still not potentially resolved.

  * •

The mathematical model for automatic insulin secretion according to measured
glucose value has to be address in better manner with internet framework.

  * •

The privacy and security issues of insulin and blood glucose measurement
system is still not resolved yet.

  * •

The efficient power management mechanism has to be developed for continuous
glucose measurement with insulin delivery system.

##  X Conclusions and Future Research

The paper presents survey of glucose measurement approaches along with
overview of glucose control mechanism. Many techniques available in literature
are only a proof of concept, showing good correlation between device estimated
result and reference value of blood glucose. However, they are neither
accurate nor cost effective solutions and not available for commercial
purpose. The optical detection using short NIR has been potential solution to
mitigate the drawbacks of all previous methods. In future, the multi-model
approaches could be considered for precise glucose estimation. The device or
prototype model should be more effective in different zones to support the
continuous health monitoring. It should be implemented as a portable device
for real time application with more frequently. This device should be
developed as continuous health monitoring with minimum cost.

The future research for upcoming noninvasive glucose monitoring device is
mentioned in Fig. 31. The device is required to be integrated with advanced
IoMT framework. This advanced IoMT framework will alow to connect the device
with all nearest diabetic centers to get best treatment. Unification of
glucose-level measurement and automatic diet quantification can have strong
impact on smart healthcare domain [213]. The durability, portability and user-
friendly device is also the future vision in this era. The device should have
the feature of border-line cross indication. Because of this feature, any
person will be aware to take own blood glucose level. A secured device with
end to end users control and authentication is also necessary for future
advancement. Physical Unclonable Function (PUF) based security of IoMT-devices
can be effective for IoMT-devices which are intrinsically resource and battery
constrained [212, 214]. Unified healthcare Cyber-Physical System (H-CPS) with
blockchain based data and device management can be effective and needs
research [215, 216].

Figure 31: Our Future Vision for Non-invasive Glucose-Level Measurement.

## References

  * [1] Diabetestalk, “What is the normal fasting blood sugar range for adults,” 2018, last Accessed on 18 Jan 2021. [Online]. Available: https://diabetestalk.net/blood-sugar/what-is-the-normal-fasting-blood-sugar-range-for-adults
  * [2] P. Jain, A. M. Joshi, and S. P. Mohanty, “iGLU 1.1: Towards a glucose-insulin model based closed loop iomt framework for automatic insulin control of diabetic patients,” in _2020 IEEE 6th World Forum on Internet of Things (WF-IoT)_. IEEE, 2020, pp. 1–6.
  * [3] A. M. Joshi, U. P. Shukla, and S. P. Mohanty, “Smart healthcare for diabetes during COVID-19,” _IEEE Consumer Electronics Magazine_ , vol. 10, no. 1, pp. 66–71, January 2020.
  * [4] Amit M Joshi, U. P. Shukla, and S. P. Mohanty, “Smart healthcare for diabetes: A COVID-19 perspective,” _arXiv preprint arXiv:2008.11153_ , 2020.
  * [5] A. M. Alsamman and H. Zayed, “The transcriptomic profiling of COVID-19 compared to SARS, MERS, Ebola, and H1N1,” _bioRxiv_ , 2020.
  * [6] I. D. Federation, “IDF Diabetes Atlas - Diabetes is rising worldwide… and is set to rise even further,” 2019, last Accessed on 21 March 2020. [Online]. Available: https://diabetesatlas.org/en/sections/worldwide-toll-of-diabetes.html
  * [7] N. H. Cho, J. E. Shaw, S. Karuranga, Y. Huang, J. D. da Rocha Fernandes, A. W. Ohlrogge, and B. Malanda, “IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045,” _Diabetes Research and Clinical Practice_ , vol. 138, pp. 271–281, April 2018.
  * [8] P. Saeedi, I. Petersohn, P. Salpea, B. Malanda, S. Karuranga, N. Unwin, S. Colagiuri, L. Guariguata, A. A. Motala, K. Ogurtsova _et al._ , “Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the international diabetes federation diabetes atlas,” _Diabetes research and clinical practice_ , vol. 157, p. 107843, 2019.
  * [9] Clevelandclinic, “Diabetes Mellitus: An Overview,” 2020, last Accessed on 18 Jan 2021. [Online]. Available: https://my.clevelandclinic.org/health/diseases/7104-diabetes-mellitus-an-overview
  * [10] Drugs, “Type 1 diabetes mellitus,” 2020, last Accessed on 18 Jan 2021. [Online]. Available: https://www.drugs.com/health-guide/type-1-diabetes-mellitus.html
  * [11] L. M. Leontis and A. Hess-Fischl, “Diabetes mellitus: An overview,” 2019, last Accessed on 18 Jan 2021. [Online]. Available: https://www.endocrineweb.com/conditions/type-2-diabetes/type-2-diabetes-symptoms
  * [12] A. Pietrangelo, “What are the different types of diabetes?” 2018, last Accessed on 18 Jan 2021. [Online]. Available: https://www.healthline.com/health/diabetes/types-of-diabetes
  * [13] M. J. Fowler, “Diabetes: Magnitude and mechanisms,” _Clinical Diabetes_ , vol. 28, no. 1, pp. 42–46, 2010. [Online]. Available: https://clinical.diabetesjournals.org/content/28/1/42
  * [14] H. Yin, B. Mukadam, X. Dai, and N. Jha, “DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks,” _IEEE Transactions on Emerging Topics in Computing_ , pp. 1–1, 2019.
  * [15] P. Zhang, “Global healthcare expenditure on diabetes for 2010 and 2030.” _Diabetes Research and Clinical Practice_ , 2011.
  * [16] J. Venkataraman and B. Freer, “Feasibility of non-invasive blood glucose monitoring: In-vitro measurements and phantom models,” in _2011 IEEE International Symposium on Antennas and Propagation (APSURSI)_ , July 2011, pp. 603–606.
  * [17] S. H. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030,” _Diabetes Care_ , vol. 27, no. 10, pp. 2569–2569, 2004. [Online]. Available: http://care.diabetesjournals.org/content/27/10/2569.2
  * [18] D. R. Whiting, L. Guariguata, C. Weil, and J. Shaw, “Idf diabetes atlas: Global estimates of the prevalence of diabetes for 2011 and 2030,” _Diabetes Research and Clinical Practice_ , vol. 94, no. 3, pp. 311 – 321, 2011.
  * [19] P. H. Siegel, A. Tang, G. Virbila, Y. Kim, M. C. F. Chang, and V. Pikov, “Compact non-invasive millimeter-wave glucose sensor,” in _2015 40th International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW-THz)_ , Aug 2015, pp. 1–3.
  * [20] S. M. Alavi, M. Gourzi, A. Rouane, and M. Nadi, “An original method for non-invasive glucose measurement: preliminary results,” in _2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , vol. 4, 2001, pp. 3318–3320 vol.4.
  * [21] X. Li and C. Li, “Study on the application of wavelet transform to non-invasive glucose concentration measurement by nirs,” in _2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)_ , Sept 2015, pp. 1294–1297.
  * [22] P. P. Pai, P. K. Sanki, S. K. Sahoo, A. De, S. Bhattacharya, and S. Banerjee, “Cloud computing-based non-invasive glucose monitoring for diabetic care,” _IEEE Transactions on Circuits and Systems I: Regular Papers_ , vol. PP, no. 99, pp. 1–14, 2017.
  * [23] P. S. Reddy and K. Jyostna, “Development of smart insulin device for non invasive blood glucose level monitoring,” in _2017 IEEE 7th International Advance Computing Conference (IACC)_ , Jan 2017, pp. 516–519.
  * [24] P. Jain, A. M. Joshi, and S. P. Mohanty, “iGLU: An Intelligent Device for Accurate Non-Invasive Blood Glucose-Level Monitoring in Smart Healthcare,” _IEEE Consumer Electronics Magazine_ , vol. 9, no. 1, p. Accepted, January 2020.
  * [25] A. M. Joshi, P. Jain, S. P. Mohanty, and N. Agrawal, “iGLU 2.0: A new wearable for accurate non-invasive continuous serum glucose measurement in IoMT framework,” _IEEE Transactions on Consumer Electronics_ , no. 10.1109/TCE.2020.3011966, p. in Press, 2020.
  * [26] S. P. Mohanty and E. Kougianos, “Biosensors: A Tutorial Review,” _IEEE Potentials_ , vol. 25, no. 2, pp. 35–40, March 2006.
  * [27] N. A. Salam, W. H. M. Saad, Z. Manap, and F. Salehuddin, “The evolution of non-invasive blood glucose monitoring system for personal application,” _Journal of Telecommunication, Electronic and Computer Engineering_ , vol. 8, pp. 59–65, 2016.
  * [28] P. Jain, A. M. Joshi, and S. P. Mohanty, “iGLU 1.0: An Accurate Non-Invasive Near-Infrared Dual Short Wavelengths Spectroscopy based Glucometer for Smart Healthcare,” _arXiv Electrical Engineering and Systems Science_ , no. arXiv:1911.04471, November 2019.
  * [29] P. Jain, A. M. Joshi, N. Agrawal, and S. P. Mohanty, “iGLU 2.0: A New Non-invasive, Accurate Serum Glucometer for Smart Healthcare,” _arXiv Electrical Engineering and Systems Science_ , vol. abs/2001.09182, 2020\. [Online]. Available: http://arxiv.org/abs/2001.09182
  * [30] Q. Liu, Y. Liu, F. Wu, X. Cao, Z. Li, M. Alharbi, A. N. Abbas, M. R. Amer, and C. Zhou, “Highly Sensitive and Wearable In2O3 Nanoribbon Transistor Biosensors with Integrated On-Chip Gate for Glucose Monitoring in Body Fluids,” _ACS Nano_ , vol. 12, no. 2, pp. 1170–1178, 2018, pMID: 29338249\.
  * [31] P. H. Siegel, W. Dai, R. A. Kloner, M. Csete, and V. Pikov, “First millimeter-wave animal in vivo measurements of l-glucose and d-glucose: Further steps towards a non-invasive glucometer,” in _2016 41st International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW-THz)_ , Sept 2016, pp. 1–3.
  * [32] P. Jain, A. M. Joshi, and S. P. Mohanty, “iGLU: An Intelligent Device for Accurate Non-Invasive Blood Glucose-Level Monitoring in Smart Healthcare,” _IEEE Consumer Electronics Magazine_ , vol. 9, no. 1, pp. 35–42, January 2020\.
  * [33] N. M. Zhilo, P. A. Rudenko, and A. N. Zhigaylo, “Development of hardware-software test bench for optical non-invasive glucometer improvement,” in _2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)_ , Feb 2017, pp. 89–90.
  * [34] S. I. Gusev, A. A. Simonova, P. S. Demchenko, M. K. Khodzitsky, and O. P. Cherkasova, “Blood glucose concentration sensing using biological molecules relaxation times determination,” in _2017 IEEE International Symposium on Medical Measurements and Applications (MeMeA)_ , May 2017, pp. 458–463.
  * [35] M. W. Sari and M. Luthfi, “Design and analysis of non-invasive blood glucose levels monitoring,” in _2016 International Seminar on Application for Technology of Information and Communication (ISemantic)_ , Aug 2016, pp. 134–137.
  * [36] S. Lekha and M. Suchetha, “Non- invasive diabetes detection and classification using breath analysis,” in _2015 International Conference on Communications and Signal Processing (ICCSP)_ , April 2015, pp. 0955–0958.
  * [37] J. Li, P. Koinkar, Y. Fuchiwaki, and M. Yasuzawa, “A fine pointed glucose oxidase immobilized electrode for low-invasive amperometric glucose monitoring,” _Biosensors and Bioelectronics_ , vol. 86, pp. 90–94, 2016\.
  * [38] N. Demitri and A. M. Zoubir, “Measuring blood glucose concentrations in photometric glucometers requiring very small sample volumes,” _IEEE Transactions on Biomedical Engineering_ , vol. 64, no. 1, pp. 28–39, 2017.
  * [39] J. Y. Lucisano, T. L. Routh, J. T. Lin, and D. A. Gough, “Glucose monitoring in individuals with diabetes using a long-term implanted sensor/telemetry system and model,” _IEEE Transactions on Biomedical Engineering_ , vol. 64, no. 9, pp. 1982–1993, 2017.
  * [40] A. Sun, A. G. Venkatesh, and D. A. Hall, “A multi-technique reconfigurable electrochemical biosensor: Enabling personal health monitoring in mobile devices,” _IEEE Transactions on Biomedical Circuits and Systems_ , vol. 10, no. 5, pp. 945–954, Oct 2016.
  * [41] A. Gani, A. V. Gribok, Y. Lu, W. K. Ward, R. A. Vigersky, and J. Reifman, “Universal glucose models for predicting subcutaneous glucose concentration in humans,” _IEEE Transactions on Information Technology in Biomedicine_ , vol. 14, no. 1, pp. 157–165, Jan 2010.
  * [42] G. Wang, M. D. Poscente, S. S. Park, C. N. Andrews, O. Yadid-Pecht, and M. P. Mintchev, “Wearable microsystem for minimally invasive, pseudo-continuous blood glucose monitoring: The e-mosquito,” _IEEE Transactions on Biomedical Circuits and Systems_ , vol. 11, no. 5, pp. 979–987, Oct 2017.
  * [43] M. M. Ahmadi and G. A. Jullien, “A wireless-implantable microsystem for continuous blood glucose monitoring,” _IEEE Transactions on Biomedical Circuits and Systems_ , vol. 3, no. 3, pp. 169–180, June 2009.
  * [44] G. Acciaroli, M. Vettoretti, A. Facchinetti, G. Sparacino, and C. Cobelli, “Reduction of blood glucose measurements to calibrate subcutaneous glucose sensors: A bayesian multiday framework,” _IEEE Transactions on Biomedical Engineering_ , vol. 65, no. 3, pp. 587–595, 2018.
  * [45] I. Pagkalos, P. Herrero, C. Toumazou, and P. Georgiou, “Bio-inspired glucose control in diabetes based on an analogue implementation of a $\beta$-cell model,” _IEEE Transactions on Biomedical Circuits and Systems_ , vol. 8, no. 2, pp. 186–195, April 2014.
  * [46] G. Wang, M. D. Poscente, S. S. Park, C. N. Andrews, O. Yadid-Pecht, and M. P. Mintchev, “Wearable microsystem for minimally invasive, pseudo-continuous blood glucose monitoring: The e-mosquito,” _IEEE Transactions on Biomedical Circuits and Systems_ , vol. 11, no. 5, pp. 979–987, 2017.
  * [47] T. Kossowski and R. Stasinski, “Robust ir attenuation measurement for non-invasive glucose level analysis,” in _2016 International Conference on Systems, Signals and Image Processing (IWSSIP)_ , May 2016, pp. 1–4.
  * [48] L. P. Pavlovich and D. Y. Mynziak, “Noninvasive method for blood glucose measuring and monitoring,” in _2013 IEEE XXXIII International Scientific Conference Electronics and Nanotechnology (ELNANO)_ , April 2013, pp. 255–257.
  * [49] Y. Liu, W. Li, T. Zheng, and W. K. Ling, “Overviews the methods of non-invasive blood glucose measurement,” in _2016 IEEE International Conference on Consumer Electronics-China (ICCE-China)_ , Dec 2016, pp. 1–2.
  * [50] N. K. Sharma and S. Singh, “Designing a non invasive blood glucose measurement sensor,” in _2012 IEEE 7th International Conference on Industrial and Information Systems (ICIIS)_ , Aug 2012, pp. 1–3.
  * [51] X. Zhao, Q. Zheng, and Z. M. Yang, “Two types of photonic crystals applied to glucose sensor,” in _2016 IEEE International Nanoelectronics Conference (INEC)_ , May 2016, pp. 1–2.
  * [52] Y. Tanaka, C. Purtill, T. Tajima, M. Seyama, and H. Koizumi, “Sensitivity improvement on cw dual-wavelength photoacoustic spectroscopy using acoustic resonant mode for noninvasive glucose monitor,” in _2016 IEEE SENSORS_ , Oct 2016, pp. 1–3.
  * [53] I. Gouzouasis, H. Cano-Garcia, I. Sotiriou, S. Saha, G. Palikaras, P. Kosmas, and E. Kallos, “Detection of varying glucose concentrations in water solutions using a prototype biomedical device for millimeter-wave non-invasive glucose sensing,” in _2016 10th European Conference on Antennas and Propagation (EuCAP)_ , April 2016, pp. 1–4.
  * [54] Y. Nikawa and D. Someya, “Non-invasive measurement of blood sugar level by millimeter waves,” in _2001 IEEE MTT-S International Microwave Sympsoium Digest (Cat. No.01CH37157)_ , vol. 1, May 2001, pp. 171–174 vol.1.
  * [55] J. Shao, F. Yang, F. Xia, Q. Zhang, and Y. Chen, “A novel miniature spiral sensor for non-invasive blood glucose monitoring,” in _2016 10th European Conference on Antennas and Propagation (EuCAP)_ , April 2016, pp. 1–2.
  * [56] P. H. Siegel, Y. Lee, and V. Pikov, “Millimeter-wave non-invasive monitoring of glucose in anesthetized rats,” in _2014 39th International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW-THz)_ , Sept 2014, pp. 1–2.
  * [57] D. Wang, “An improved integration sensor of non-invasive blood glucose,” in _The 7th IEEE/International Conference on Advanced Infocomm Technology_ , Nov 2014, pp. 70–75.
  * [58] N. Bayasi, H. Saleh, B. Mohammad, and M. Ismail, “The revolution of glucose monitoring methods and systems: A survey,” in _2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS)_ , Dec 2013, pp. 92–93.
  * [59] R. Agrawal, N. Sharma, M. Rathore, V. Gupta, S. Jain, V. Agarwal, and S. Goyal, “Noninvasive method for glucose level estimation by saliva,” _J Diabetes Metab_ , vol. 4, no. 5, pp. 2–5, 2013.
  * [60] N. Demitri and A. M. Zoubir, “Measuring blood glucose concentrations in photometric glucometers requiring very small sample volumes,” _IEEE Transactions on Biomedical Engineering_ , vol. 64, no. 1, pp. 28–39, 2016.
  * [61] M. Shokrekhodaei and S. Quinones, “Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone,” _Sensors_ , vol. 20, no. 5, p. 1251, 2020.
  * [62] S. Delbeck, T. Vahlsing, S. Leonhardt, G. S. G, and H. M. Heise, “Non-invasive monitoring of blood glucose using optical methods for skin spectroscopy-opportunities and recent advances,” _Anal Bioanal Chem._ , vol. 411, no. 1, pp. 63–77, 2019.
  * [63] N. K. Madzhi, S. A. Shamsuddin, and M. F. Abdullah, “Comparative investigation using gaas(950nm), gaaias (940nm) and ingaasp (1450nm) sensors for development of non-invasive optical blood glucose measurement system,” in _2014 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)_ , Nov 2014, pp. 1–6.
  * [64] N. A. M. Aziz, N. Arsad, P. S. Menon, A. R. Laili, M. H. Laili, and A. A. A. Halim, “Analysis of difference light sources for non-invasive aqueous glucose detection,” in _2014 IEEE 5th International Conference on Photonics (ICP)_ , Sept 2014, pp. 150–152.
  * [65] S. Tommasone, “Infrared spectroscopy: An overview,” 2018, last Accessed on 18 Jan 2021. [Online]. Available: https://www.azolifesciences.com/article/Infrared-Spectroscopy-An-Overview.aspx
  * [66] A. A. Muley and R. B. Ghongade, “Design and simulate an antenna for aqueous glucose measurement,” in _2014 Annual IEEE India Conference (INDICON)_ , Dec 2014, pp. 1–6.
  * [67] K. U. Menon, D. Hemachandran, and A. T. Kunnath, “Voltage intensity based non-invasive blood glucose monitoring,” in _2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)_. IEEE, 2013, pp. 1–5.
  * [68] J. L. Lai, S. Y. Huang, R. S. Lin, and S. C. Tsai, “Design a non-invasive near-infrared led blood glucose sensor,” in _2016 International Conference on Applied System Innovation (ICASI)_ , May 2016, pp. 1–4.
  * [69] M. Tamilselvi and G. Ramkumar, “Non-invasive tracking and monitoring glucose content using near infrared spectroscopy,” in _2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)_ , Dec 2015, pp. 1–3.
  * [70] K. Lawand, M. Parihar, and S. N. Patil, “Design and development of infrared led based non invasive blood glucometer,” in _2015 Annual IEEE India Conference (INDICON)_ , Dec 2015, pp. 1–6.
  * [71] J. Yadav, A. Rani, V. Singh, and B. M. Murari, “Near-infrared led based non-invasive blood glucose sensor,” in _2014 International Conference on Signal Processing and Integrated Networks (SPIN)_ , Feb 2014, pp. 591–594.
  * [72] M. T. B. Z. Abidin, M. K. R. Rosli, S. A. Shamsuddin, N. K. Madzhi, and M. F. Abdullah, “Initial quantitative comparison of 940nm and 950nm infrared sensor performance for measuring glucose non-invasively,” in _2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)_ , Nov 2013, pp. 1–6.
  * [73] Y. Nikawa and T. Michiyama, “Non-invasive measurement of blood-sugar level by reflection of millimeter-waves,” in _2006 Asia-Pacific Microwave Conference_ , Dec 2006, pp. 47–50.
  * [74] M. Goodarzi and W. Saeys, “Selection of the most informative near infrared spectroscopy wavebands for continuous glucose monitoring in human serum,” _Talanta_ , vol. 146, pp. 155–165, 2016.
  * [75] S. Sharma, M. Goodarzi, L. Wynants, H. Ramon, and W. Saeys, “Efficient use of pure component and interferent spectra in multivariate calibration,” _Analytica chimica acta_ , vol. 778, pp. 15–23, 2013.
  * [76] Y. Uwadaira, N. Adachi, A. Ikehata, and S. Kawano, “Factors affecting the accuracy of non-invasive blood glucose measurement by short-wavelength near infrared spectroscopy in the determination of the glycaemic index of foods,” _Journal of Near Infrared Spectroscopy_ , vol. 18, no. 5, pp. 291–300, 2010\.
  * [77] S. Haxha and J. Jhoja, “Optical based noninvasive glucose monitoring sensor prototype,” _IEEE Photonics Journal_ , vol. 8, no. 6, pp. 1–11, 2016.
  * [78] W. Zhang, R. Liu, W. Zhang, H. Jia, and K. Xu, “Discussion on the validity of nir spectral data in non-invasive blood glucose sensing,” _Biomedical optics express_ , vol. 4, no. 6, pp. 789–802, 2013.
  * [79] M. Golic, K. Walsh, and P. Lawson, “Short-wavelength near-infrared spectra of sucrose, glucose, and fructose with respect to sugar concentration and temperature,” _Applied spectroscopy_ , vol. 57, no. 2, pp. 139–145, 2003\.
  * [80] K. Song, U. Ha, S. Park, J. Bae, and H. J. Yoo, “An impedance and multi-wavelength near-infrared spectroscopy ic for non-invasive blood glucose estimation,” _IEEE Journal of Solid-State Circuits_ , vol. 50, no. 4, pp. 1025–1037, April 2015.
  * [81] S. Ramasahayam, K. S. Haindavi, and S. R. Chowdhury, “Noninvasive estimation of blood glucose concentration using near infrared optodes,” in _Sensing Technology: Current Status and Future Trends IV_. Springer, 2015, pp. 67–82.
  * [82] A. Heller, “Integrated medical feedback systems for drug delivery,” _AIChE journal_ , vol. 51, no. 4, pp. 1054–1066, 2005.
  * [83] P. P. Pai, P. K. Sanki, A. De, and S. Banerjee, “NIR photoacoustic spectroscopy for non-invasive glucose measurement,” in _2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)_. IEEE, 2015, pp. 7978–7981.
  * [84] P. Jain, A. M. Joshi, and S. P. Mohanty, “iGLU: An intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare,” _IEEE Consumer Electronics Magazine_ , vol. 9, no. 1, pp. 35–42, 2019.
  * [85] P. Jain, R. Maddila, and A. M. Joshi, “A precise non-invasive blood glucose measurement system using NIR spectroscopy and Huber’s regression model,” _Optical and Quantum Electronics_ , vol. 51, no. 2, p. 51, 2019.
  * [86] E. Monte-Moreno, “Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques,” _Artificial intelligence in medicine_ , vol. 53, no. 2, pp. 127–138, 2011\.
  * [87] S. Habbu, M. Dale, and R. Ghongade, “Estimation of blood glucose by non-invasive method using photoplethysmography,” _Sādhanā_ , vol. 44, no. 6, p. 135, 2019.
  * [88] H. Ali, F. Bensaali, and F. Jaber, “Novel approach to non-invasive blood glucose monitoring based on transmittance and refraction of visible laser light,” _IEEE Access_ , vol. 5, pp. 9163–9174, 2017.
  * [89] C. Vrančić, A. Fomichova, N. Gretz, C. Herrmann, S. Neudecker, A. Pucci, and W. Petrich, “Continuous glucose monitoring by means of mid-infrared transmission laser spectroscopyin vitro,” _Analyst_ , vol. 136, pp. 1192–1198, 2011. [Online]. Available: http://dx.doi.org/10.1039/C0AN00537A
  * [90] B. Paul, M. P. Manuel, and Z. C. Alex, “Design and development of non invasive glucose measurement system,” in _2012 1st International Symposium on Physics and Technology of Sensors (ISPTS-1)_. IEEE, 2012, pp. 43–46.
  * [91] L. A. Philip, K. Rajasekaran, and E. S. J. Jothi, “Continous monitoring of blood glucose using photophlythesmograph signal,” in _2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT)_. IEEE, 2017, pp. 187–191.
  * [92] H. Karimipour, H. T. Shandiz, and E. Zahedi, “Diabetic diagnose test based on ppg signal and identification system,” _Journal of Biomedical Science and Engineering_ , vol. 2, no. 06, p. 465, 2009.
  * [93] F. R. G. Cruz, C. C. Paglinawan, C. N. V. Catindig, J. C. B. Lamchek, D. D. C. Almiranez, and A. F. Sanchez, “Application of reflectance mode photoplethysmography for non-invasive monitoring of blood glucose level with moving average filter,” in _Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology_. ACM, 2019, pp. 22–26.
  * [94] Y. Zhang, Y. Zhang, S. A. Siddiqui, and A. Kos, “Non-invasive blood-glucose estimation using smartphone ppg signals and subspace knn classifier,” _Elektrotehniski Vestnik_ , vol. 86, no. 1/2, pp. 68–74, 2019.
  * [95] Y. Yamakoshi, K. Matsumura, T. Yamakoshi, J. Lee, P. Rolfe, Y. Kato, K. Shimizu, and K.-i. Yamakoshi, “Side-scattered finger-photoplethysmography: experimental investigations toward practical noninvasive measurement of blood glucose,” _Journal of biomedical optics_ , vol. 22, no. 6, p. 067001, 2017.
  * [96] O. Olarte, W. V. Moer, K. Barbé, Y. V. Ingelgem, and A. Hubin, “Influence of the type and position of the sensor on the precision of impedance glucose measurements,” in _2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)_ , May 2013, pp. 1750–1754.
  * [97] S. K. Dhar, P. Biswas, and S. Chakraborty, “Dc impedance of human blood using eis: An appraoch to non-invasive blood glucose measurement,” in _2013 International Conference on Informatics, Electronics and Vision (ICIEV)_ , May 2013, pp. 1–6.
  * [98] Y. Khawam, M. Ali, H. Shazada, S. Kanan, and H. Nashash, “Non-invasive blood glucose measurement using transmission spectroscopy,” in _2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA)_ , Feb 2013, pp. 1–4.
  * [99] M. N. Anas and P. K. Lim, “A bio-impedance approach,” in _2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)_ , Nov 2013, pp. 1–5.
  * [100] C. E. F. Amaral and B. Wolf, “Effects of glucose in blood and skin impedance spectroscopy,” in _AFRICON 2007_ , Sept 2007, pp. 1–7.
  * [101] P. Jain and A. M. Joshi, “Low leakage and high cmrr cmos differential amplifier for biomedical application,” _Analog Integrated Circuits and Signal Processing_ , pp. 1–15, 2017.
  * [102] B. Paul, M. P. Manuel, and Z. C. Alex, “Design and development of non invasive glucose measurement system,” in _2012 1st International Symposium on Physics and Technology of Sensors (ISPTS-1)_ , March 2012, pp. 43–46.
  * [103] M. Hofmann, M. Bloss, R. Weigel, G. Fischer, and D. Kissinger, “Non-invasive glucose monitoring using open electromagnetic waveguides,” in _2012 42nd European Microwave Conference_ , Oct 2012, pp. 546–549.
  * [104] Y. Liu, M. Xia, Z. Nie, J. Li, Y. Zeng, and L. Wang, “In vivo wearable non-invasive glucose monitoring based on dielectric spectroscopy,” in _2016 IEEE 13th International Conference on Signal Processing (ICSP)_ , Nov 2016, pp. 1388–1391.
  * [105] M. Yamaguchi, M. Mitsumori, and Y. Kano, “Noninvasively measuring blood glucose using saliva,” _IEEE Engineering in Medicine and Biology Magazine_ , vol. 17, no. 3, pp. 59–63, May 1998.
  * [106] M. S. Prasad, R. Chen, Y. Li, D. Rekha, D. Li, H. Ni, and N. Y. Sreedhar, “Polypyrrole supported with copper nanoparticles modified alkali anodized steel electrode for probing of glucose in real samples,” _IEEE Sensors Journal_ , vol. 18, no. 13, pp. 5203–5212, July 2018.
  * [107] G. Yoon, K. J. Jeon, A. K. Amerov, Y.-J. Kim, D. Y. Hwang, J. B. Kim, and H. S. Kim, “Non-invasive monitoring of blood glucose,” in _Lasers and Electro-Optics, 1999. CLEO/Pacific Rim ’99. The Pacific Rim Conference on_ , vol. 4, Aug 1999, pp. 1233–1234 vol.4.
  * [108] Y. Yamakoshi, M. Ogawa, T. Yamakoshi, M. Satoh, M. Nogawa, S. Tanaka, T. Tamura, P. Rolfe, and K. Yamakoshi, “A new non-invasive method for measuring blood glucose using instantaneous differential near infrared spectrophotometry,” in _2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , Aug 2007, pp. 2964–2967.
  * [109] G. Yoon, A. K. Amerov, K. J. Jeon, J. B. Kim, and Y.-J. Kim, “Optical measurement of glucose levels in scattering media,” in _Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286)_ , vol. 4, Oct 1998, pp. 1897–1899 vol.4.
  * [110] H. Ishizawa, A. Muro, T. Takano, K. Honda, and H. Kanai, “Non-invasive blood glucose measurement based on atr infrared spectroscopy,” in _2008 SICE Annual Conference_ , Aug 2008, pp. 321–324.
  * [111] T. Harada, K. Yamamoto, M. Kondo, K. Gesho, and I. Ishimaru, “Spectroscpy optical coherence tomography of biomedical tissue,” in _SICE Annual Conference 2007_ , Sept 2007, pp. 3056–3059.
  * [112] A. Popov, A. Bykov, S. Toppari, M. Kinnunen, A. Priezzhev, and R. Myllylä, “Glucose sensing in flowing blood and intralipid by laser pulse time-of-flight and optical coherence tomography techniques,” _IEEE Journal of Selected Topics in Quantum Electronics_ , vol. 18, pp. 1335–1342, 07 2012.
  * [113] J. Y. Sim, C. G. Ahn, E. Jeong, and B. K. Kim, “Photoacoustic spectroscopy that uses a resonant characteristic of a microphone for in vitro measurements of glucose concentration,” in _2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)_ , Aug 2016, pp. 4861–4864.
  * [114] P. P. Pai, P. K. Sanki, and S. Banerjee, “A photoacoustics based continuous non-invasive blood glucose monitoring system,” in _2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings_ , May 2015, pp. 106–111.
  * [115] P. P. Pai, P. K. Sanki, A. De, and S. Banerjee, “Nir photoacoustic spectroscopy for non-invasive glucose measurement,” in _2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)_ , Aug 2015, pp. 7978–7981.
  * [116] Y. Tanaka, Y. Higuchi, and S. Camou, “Noninvasive measurement of aqueous glucose solution at physiologically relevant blood concentration levels with differential continuous-wave laser photoacoustic technique,” in _2015 IEEE SENSORS_ , Nov 2015, pp. 1–4.
  * [117] L. Xiaoli and L. Chengwei, “Research on glucose concentration sensing with single wavelength laser,” in _2015 12th IEEE International Conference on Electronic Measurement Instruments (ICEMI)_ , vol. 03, July 2015, pp. 1547–1551.
  * [118] H. A. A. Naam, M. O. Idrees, A. Awad, O. S. Abdalsalam, and F. Mohamed, “Non invasive blood glucose measurement based on photo-acoustic spectroscopy,” in _2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE)_ , Sept 2015, pp. 1–4.
  * [119] S. Camou, Y. Ueno, and E. Tamechika, “New cw-photoacoustic-based protocol for noninvasive and selective determination of aqueous glucose level: A potential alternative towards noninvasive blood sugar sensing,” in _2011 IEEE SENSORS Proceedings_ , Oct 2011, pp. 798–801.
  * [120] N. Wadamori, R. Shinohara, and Y. Ishihara, “Photoacoustic depth profiling of a skin model for non-invasive glucose measurement,” in _2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , Aug 2008, pp. 5644–5647.
  * [121] S. Koyama, Y. Miyauchi, T. Horiguchi, and H. Ishizawa, “Non-invasive measurement of blood glucose of diabetic based on ir spectroscopy,” in _Proceedings of SICE Annual Conference 2010_ , Aug 2010, pp. 3425–3426.
  * [122] P. Domachuk, M. Hunter, R. Batorsky, M. Cronin-Golomb, F. G. Omenetto, A. Wang, A. K. George, and J. C. Knight, “A path for non-invasive glucose detection using mid-ir supercontinuum,” in _2008 Conference on Lasers and Electro-Optics and 2008 Conference on Quantum Electronics and Laser Science_ , May 2008, pp. 1–2.
  * [123] R. Periyasamy and S. Anand, “A study on non-invasive blood glucose estimation- an approach using capacitance measurement technique,” in _2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)_ , Oct 2016, pp. 847–850.
  * [124] T. Yilmaz, R. Foster, and Y. Hao, “Towards accurate dielectric property retrieval of biological tissues for blood glucose monitoring,” _IEEE Transactions on Microwave Theory and Techniques_ , vol. 62, no. 12, pp. 3193–3204, Dec 2014.
  * [125] M. Gourzi, A. Rouane, M. B. McHugh, R. Guelaz, and M. Nadi, “New biosensor for non-invasive glucose concentration measurement,” in _Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498)_ , vol. 2, Oct 2003, pp. 1343–1347 Vol.2.
  * [126] V. Turgul and I. Kale, “On the accuracy of complex permittivity model of glucose/water solutions for non-invasive microwave blood glucose sensing,” in _2015 E-Health and Bioengineering Conference (EHB)_ , Nov 2015, pp. 1–4.
  * [127] D. Li, D. Yang, J. Yang, Y. Lin, Y. Sun, H. Yu, and K. Xu, “Glucose affinity measurement by surface plasmon resonance with borate polymer binding,” _Sensors and Actuators A: Physical_ , vol. 222, pp. 58 – 66, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0924424714004737
  * [128] R. Kaul and U. P. Khot, “Design of microstrip antennas for glucometer application,” in _2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)_ , Dec 2016, pp. 352–357.
  * [129] V. Turgul and I. Kale, “Influence of fingerprints and finger positioning on accuracy of rf blood glucose measurement from fingertips,” _Electronics Letters_ , vol. 53, no. 4, pp. 218–220, 2017.
  * [130] H. Cano-Garcia, I. Gouzouasis, I. Sotiriou, S. Saha, G. Palikaras, P. Kosmas, and E. Kallos, “Reflection and transmission measurements using 60 ghz patch antennas in the presence of animal tissue for non-invasive glucose sensing,” in _2016 10th European Conference on Antennas and Propagation (EuCAP)_ , April 2016, pp. 1–3.
  * [131] S. Saha, I. Sotiriou, I. Gouzouasis, H. Cano-Garcia, G. Palikaras, P. Kosmas, and E. Kallos, “Evaluation of the sensitivity of transmission measurements at millimeter waves using patch antennas for non-invasive glucose sensing,” in _2016 10th European Conference on Antennas and Propagation (EuCAP)_ , April 2016, pp. 1–4.
  * [132] V. Turgul and I. Kale, “A novel pressure sensing circuit for non-invasive rf/microwave blood glucose sensors,” in _2016 16th Mediterranean Microwave Symposium (MMS)_ , Nov 2016, pp. 1–4.
  * [133] M. S. Ali, N. J. Shoumy, S. Khatun, L. M. Kamarudin, and V. Vijayasarveswari, “Non-invasive blood glucose measurement performance analysis through uwb imaging,” in _2016 3rd International Conference on Electronic Design (ICED)_ , Aug 2016, pp. 513–516.
  * [134] H. Choi, J. Nylon, S. Luzio, J. Beutler, and A. Porch, “Design of continuous non-invasive blood glucose monitoring sensor based on a microwave split ring resonator,” in _2014 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-Bio2014)_ , Dec 2014, pp. 1–3.
  * [135] V. Turgul and I. Kale, “Simulating the effects of skin thickness and fingerprints to highlight problems with non-invasive rf blood glucose sensing from fingertips,” _IEEE Sensors Journal_ , vol. 17, no. 22, pp. 7553–7560, Nov 2017.
  * [136] Y. Miyauchi, T. Horiguchi, H. Ishizawa, S. i. Tezuka, and H. Hara, “Blood glucose level measurement by confocal reflection photodetection system,” in _SICE Annual Conference 2011_ , Sept 2011, pp. 2686–2689.
  * [137] D. W. Kim, H. S. Kim, D. H. Lee, and H. C. Kim, “Importance of skin resistance in the reverse iontophoresis-based noninvasive glucose monitoring system,” in _The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , vol. 1, Sept 2004, pp. 2434–2437.
  * [138] M. Hofmann, T. Fersch, R. Weigel, G. Fischer, and D. Kissinger, “A novel approach to non-invasive blood glucose measurement based on rf transmission,” in _2011 IEEE International Symposium on Medical Measurements and Applications_ , May 2011, pp. 39–42.
  * [139] K. Mitsubayashi, “Novel biosensing devices for medical applications soft contact-lens sensors for monitoring tear sugar,” in _2014 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD)_ , Sept 2014, pp. 349–352.
  * [140] B. D. Cameron and G. L. Cote, “Polarimetric glucose sensing in aqueous humor utilizing digital closed-loop control,” in _Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society_ , vol. 1, Oct 1996, pp. 204–205 vol.1.
  * [141] R. J. Buford, E. C. Green, and M. J. McClung, “A microwave frequency sensor for non-invasive blood-glucose measurement,” in _2008 IEEE Sensors Applications Symposium_ , Feb 2008, pp. 4–7.
  * [142] S. Lee, V. Nayak, J. Dodds, M. Pishkou, and N. B. Smith, “Glucose measurements with sensors and ultrasound,” _Ultrasound in Medicine and Biology_ , vol. 31, no. 7, pp. 971–977, 2005.
  * [143] O. K. Cho, Y. O. Kim, H. Mitsumaki, and K. Kuwa, “Noninvasive Measurement of Glucose by Metabolic Heat Conformation Method,” _Clinical Chemistry_ , vol. 50, no. 10, pp. 1894–1898, 10 2004. [Online]. Available: https://doi.org/10.1373/clinchem.2004.036954
  * [144] A. B. Blodgett, R. K. Kothinti, I. Kamyshko, D. H. Petering, S. Kumar, and N. M. Tabatabai, “A fluorescence method for measurement of glucose transport in kidney cells,” _Diabetes Technology & Therapeutics_, vol. 13, no. 7, pp. 743–751, 2011, pMID: 21510766.
  * [145] A. K. Amerov, Y. Sun, G. W. Small, and M. A. Arnold, “Kromoscopic measurement of glucose in the first overtone region of the near-infrared spectrum,” in _Optical Diagnostics and Sensing of Biological Fluids and Glucose and Cholesterol Monitoring II_ , A. V. Priezzhev and G. L. Cote, Eds., vol. 4624, International Society for Optics and Photonics. SPIE, 2002, pp. 11 – 19. [Online]. Available: https://doi.org/10.1117/12.468318
  * [146] R. Zhang, S. Liu, H. Jin, Y. Luo, Z. Zheng, F. Gao, and Y. Zheng, “Noninvasive electromagnetic wave sensing of glucose,” _Sensors_ , vol. 19, no. 5, p. 1151, 2019.
  * [147] P. Bertemes-Filho, R. Weinert, T. Barato, and T. Baratto de Albuquerque, “Detection of glucose by using impedance spectroscopy,” in _Proc. International Conference on Electrical Bio-Impedance_ , 06 2016.
  * [148] R. O. Potts, J. A. Tamada, and M. J. Tierney, “Glucose monitoring by reverse iontophoresis,” _Diabetes/Metabolism Research and Reviews_ , vol. 18, no. S1, pp. S49–S53, 2002. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/dmrr.210
  * [149] R. Rao and S. Nanda, “Sonophoresis: recent advancements and future trends,” _Journal of Pharmacy and Pharmacology_ , vol. 61, no. 6, pp. 689–705, 2009\. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1211/jpp.61.06.0001
  * [150] O. Amir, D. Weinstein, S. Zilberman, M. Less, D. Perl-Treves, H. Primack, A. Weinstein, E. Gabis, B. Fikhte, and A. Karasik, “Continuous noninvasive glucose monitoring technology based on “occlusion spectroscopy”,” _Journal of Diabetes Science and Technology_ , vol. 1, no. 4, pp. 463–469, 2007, pMID: 19885108.
  * [151] B. M. Jensen, P. Bjerring, J. S. Christiansen, and H. ørskov, “Glucose content in human skin: relationship with blood glucose levels,” _Scandinavian Journal of Clinical and Laboratory Investigation_ , vol. 55, no. 5, pp. 427–432, 1995.
  * [152] T. Kossowski and R. Stasiński, “Multi-wavelength analysis of substances levels in human blood,” in _2017 International Conference on Systems, Signals and Image Processing (IWSSIP)_ , May 2017, pp. 1–4.
  * [153] A. Ficorella, A. D’Amico, M. Santonico, G. Pennazza, S. Grasso, and A. Zompanti, “A multi-frequency system for glucose detection with optical sensors,” in _2015 XVIII AISEM Annual Conference_ , Feb 2015, pp. 1–3.
  * [154] K. Song, U. Ha, S. Park, and H.-J. Yoo, “An impedance and multi-wavelength near-infrared spectroscopy ic for non-invasive blood glucose estimation,” in _2014 Symposium on VLSI Circuits Digest of Technical Papers_ , June 2014, pp. 1–2.
  * [155] E. L. Litinskaia, N. A. Bazaev, K. V. Pozhar, and V. M. Grinvald, “Methods for improving accuracy of non-invasive blood glucose detection via optical glucometer,” in _2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)_ , Feb 2017, pp. 47–49.
  * [156] P. Jain and S. Akashe, “An innovative design: Mos based full-wave centre-tapped rectifier,” _Wireless Personal Communications_ , vol. 90, no. 4, pp. 1673–1693, 2016.
  * [157] P. JAIN and S. AKASHE, “Performance analysis of analog to digital converter with augmented voltage swing boost logic cum schmitt trigger mos switches configuration.” _Journal of Active & Passive Electronic Devices_, vol. 11, 2016.
  * [158] P. Jain and S. Akashe, “Analyzing the impact of bootstrapped adc with augmented nmos sleep transistors configuration on performance parameters,” _Circuits, Systems, and Signal Processing_ , vol. 33, no. 7, pp. 2009–2025, 2014.
  * [159] P.Jain and S. Akashe, “Design and optimization of flash type analog to digital converter using augmented sleep transistors with current mode logic,” _Radioelectronics and Communications Systems_ , vol. 56, no. 10, pp. 472–480, 2013.
  * [160] A. K. Singh and S. K. Jha, “Fabrication and validation of a handheld non-invasive, optical biosensor for self-monitoring of glucose using saliva,” _IEEE Sensors Journal_ , vol. 19, no. 18, pp. 8332–8339, Sep. 2019\.
  * [161] P. P. Pai, A. De, and S. Banerjee, “Accuracy enhancement for noninvasive glucose estimation using dual-wavelength photoacoustic measurements and kernel-based calibration,” _IEEE Transactions on Instrumentation and Measurement_ , vol. 67, no. 1, pp. 126–136, 2018.