NON INVASIVE MONITORING OF GLUCOSE
LEVEL IN BLOOD USING NEAR-INFRARED
SPECTROSCOPY
Nikhil Nair, G.Sravani
Midhun Nair, S. Shruthi
Dept. of Electronics & Telecomm. Engg.
V.E.S.I.T., Chembur
Mumbai, India
2015nikhil.nair@ves.ac.in, 2015sravani.g@ves.ac.in
Dept. of Electronics & Telecomm. Engg.
V.E.S.I.T., Chembur
Mumbai, India
2015midhun.nair@ves.ac.in, 2015s.shruthi@ves.ac.in
Anuradha Jadiya
Dept. of Electronics & Telecomm. Engg.
V.E.S.I.T., Chembur
Mumbai, India
anuradha.jadiya@ves.ac.in
Abstract—
Diabetes is a non-communicable disease wherein
the human body does not produce the passable quantity of insulin
required to maintain normal blood glucose level. It is a major
challenge of the current century and in 2016, an estimated 1.6
million deaths were directly caused by diabetes. Fingerstick
devices, also called lancing devices, are devices that are used to
prick the skin and obtain drops of blood for testing and are often
linked to multiple HBV (Hepatitis B virus) infection outbreaks.
Also expenses associated with strips and Lancets are more
because each test requires a new test-strip and Lancet and have
proved to be an expensive inconvenience for the patients. Due to
the complexity of human body composition and physiological
processes, there has been no single non invasive method to date
that can achieve sound clinical test results alone; therefore,
researchers have conducted their studies on the integration of
multiple methods, which has become a major trend for research
on non invasive glucose monitoring. In this paper we discuss a
viable prototype which we aim to utilize using NIR (Near
Infrared) spectroscopy which is based on the scattering property
that has a direct effect on glucose. The methodology is based
impinging NIR light on a body part and the amount of light that
is reflected. The presence of glucose blocks the light after passing
through skin and muscles. This reflected light is examined to find
out the concentration of glucose non- invasively. The project is
implemented using Arduino IDE for acquiring and operating the
NIR sensor unit, finding the performance matrix of the system as
well as various analytical studies and implementing it as an IoT
device.
Keywords—Diabetes; NIR spectroscopy; non invasive;
regression analysis; IoT
I.
INTRODUCTION
Diabetes is metabolic disorder in which blood glucose
fluctuates from its normal range (90-140mg/dl). Insulin is a
hormone produced in body to regulate blood glucose level
naturally. Under some pathological failure, body is not able to
produce insulin or body cells become unable to use insulin.
Poor management of diabetes can lead to serious health
problems such as cardiovascular diseases, damage of blood
vessels, stroke, blindness, chronic kidney failure, nervous
system diseases, amputation of foot due to ulceration and early
death. The number of diabetic people is increasing across the
world due to population growth, unhealthy diet, obesity and
lack of physical activity. According to the International
Diabetes Federation (IDF), 382 million people suffered from
diabetes in 2013, an alarming figure which is set to reach 592
million by 2035. Only in India, about 65 million people suffer
from diabetes, making it the 'Diabetes Capital' of the world.
China, India and USA are among the top three countries
suffering from diabetes. According to world health
organization (WHO), every year 35 million people die because
of diabetes.
At present none of the available methods can cure diabetes
completely. Occurrence of complications can be prevented by
keeping blood glucose levels in check through regular glucose
monitoring, diet plan, insulin shots and oral medications
thereby forming the foundation of diabetes treatment. Regular
blood glucose monitoring is the key step in efficient
management of diabetes to control blood glucose. Most of
commercially available glucose measurement devices are
invasive. Diabetic patients need to monitor their blood glucose
two to three times a day. The invasive methods are painful,
have high recurring cost and danger of spreading infectious
diseases like HBV and HIV. Non-invasive methods are more
desirable but are under-implemented due to lack of research
and corporate backing. Enhancing glucose measurement
techniques to allow easy and continuous monitoring has
received a lot of attention from both academic and industrial
researchers over the past three decades. Non-invasive glucose
monitoring could make millions of people more accessible to a
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robust and cheap blood glucose testing kit that is painless and
easy to use.
II. PREVALENT
GLUCOSE MONITORING
METHODOLOGIES
A. Invasive methodology
Invasive devices constitute sensors that are completely
implanted (either subcutaneously or intravenously) and
interface with an external controller via wireless
communication. Upon implantation, the sensor continuously
measures glucose levels and feeds this information back to an
external controller for display and various other actions. While
most of implantable sensors are based on enzymatic oxidation
of glucose and subsequent assay (electrochemical or optical)
of the enzymatic products, communication to the external
controller is achieved either via radio frequency or optical
signalling.
to a digital signal. The finger tip is placed between the NIR
light source and NIR photo detector as shown in Fig 1. For
more insight into the operation flow refer Fig 2. And for a
detailed look of the circuit of the sensor unit refer Fig 3.
Fig 1. Proposed Block Diagram
In a different approach of invasive sensors, microdialysis
technology utilizes a catheter housing a dialysis membrane
inserted within the SC tissue to continuously pass glucose-free
isotonic fluid across the skin. Upon passage through the skin,
the isotonic fluid picks up glucose that is assayed externally
using optical or electrochemical techniques.
B. Non-invasive methodology
Fluorescence intensity of human tissue, using glucose
itself as the fluorophore. When human tissue is excited with
light at 308 nm wavelength, the glucose molecules become
excited and the fluorescence emission can be detected at either
340, 380, or 400 nm.
Raman spectroscopy that is based on inelastic scattering of
photons. When monochromatic light interacts with glucose,
owing to the Raman effect, there will be a shift in the energy
of the photons proportional to the vibrational or rotational
energy of glucose. Since the Raman spectrum is characteristic
of a specific intramolecular motion (vibrational or rotational)
of the molecular bonds of glucose, it can provide selective
information about the concentration of glucose. For example,
Raman spectroscopy can be utilized to differentiate between
galactose and glucose, two epimers with the same chemical
composition but structurally different in the position of one
atom.
III. METHODOLOGY
The main objective is obtain the glucose concentration,
using NIR spectroscopy between wavelengths 940 nm to 2450
nm. For this purpose we have chosen TCRT5000. TCRT5000
is a reflective sensor which includes an near-infrared emitter
and phototransistor in a leaded package which blocks visible
light. This takes care of the transmission and reception of NIR
rays and the fingertip is established as the body inspection
site. The reflected light is converted into voltage by photo
detector and by reflectance spectroscopy. .This voltage signal
is then processed for signal conditioning before feeding into
the microcontroller. These signal conditioning parts consist of
filters and amplifiers. This filtered and amplified signal is fed
into microcontroller (in this case NodeMCU) at one of the
GPIO (General Purpose Input/ Output) pins for converting it
Fig 2. Flowchart of the functioning of the device
Fig 3. Circuit of Sensor unit
The device has two operations that happening concurrently
. One is the sensor unit that acquires the voltage readings of
the subsequent glucose readings and the other is the display of
glucose readings that is handled by the NodeMCU which
continuously feeding it to a secure website with the help of the
real-time database feature of the Google Firebase platform.
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IV. MATHEMATICAL MODELLING
Beer-Lambert Law forms the basis of the mathematical
modelling of this device. The Beer-Lambert law (or Beer's
law) is the linear relationship between absorbance and
concentration of an absorbing species. The general BeerLambert law is usually written as:
A = a( ) * b * c
TABLE I.
(1)
where A is the measured absorbance, a( ) is a
wavelength-dependent absorptivity coefficient, b is the path
length, and c is the analyte concentration.
From (1) it is evident that absorbance is directly
proportional to glucose i.e. analyte concentration. Therefore
we can infer that the reflected NIR voltage is directly
proportional to the glucose concentration.
Further to quantify the reflected voltage we need to
authenticate our voltage readings with an actual industry grade
glucometer. For this purpose we use a mass produced
glucometer which sold in a pharmacy or medical retail store.
To correlate the voltage readings and glucometer readings
we need to use a powerful statistical tool i.e. ‘Regression’. It is
a technique to model and analyse the relationship between the
mean value of one variable (in this glucose concentration) and
corresponding values of other variables (in this case voltage
readings). Since the change in glucose concentration is
dynamic, “non-linear” regression is used. It is a form of
regression analysis in which observational data are modelled
by a function which is a non-linear combination of the model
parameters and depends on one or more independent variables.
The data are fitted by a method of successive approximations.
This is showcased in fig 4 given below.
READINGS
Sr No
Volt(mV)
Sugar(mg
/dl)
Best Function
calculated Sugar(mg/dl)
Accuracy (in
%)
1
747
156
150.6077845
97%
2
737
130
127.21905
99%
3
692
154
167.6933734
92%
4
714
117
118.1434113
99%
5
700
164
143.0966536
88%
6
725
109
114.7111596
95%
7
736
117
125.5286139
93%
This formula now becomes an integral component of sensor
unit computation.
V. RESULT AND ANALYSIS
In this paper, we present a working model of a glucose
measuring device using non-invasive system using IoT. As
you can see in the Accuracy column of Table I, the values
range between percentages of 88 to 99. This large swing in
accuracy is owed to the insufficient data that was collected.
We expect to shorten this swing in the future. But these are
encouraging signs and we present to you, a very satisfactory
prototype.
VI.
CONCLUSION
In this paper, an alternative for the already existing traditional
and costlier invasive glucometer sold by pharmaceutical
companies was presented and discussed, wherein the principle
of Near-Infrared Spectroscopy was used to calculate the blood
sugar level painlessly.
REFERENCES
[1]
[2]
[3]
Fig 4. Non Linear regression analysis
The curve equation of the given graph will provide us with the
formulation of glucose concentration. In order to verify the
feasibility of this device we have provided the readings
recorded and its accuracy in Table 1.
[4]
[5]
https://www.who.int/news-room/fact-sheets/detail/diabetes
https://www.cdc.gov/injectionsafety/blood-glucose-monitoring.htmls
Santhisagar Vaddiraju, Ph.D., Diane J. Burgess, Ph.D., Ioannis Tomazos,
Ph.D., M.B.A., Faquir C. Jain, Ph.D. and Fotios Papadimitrakopoulos,
Ph.D., “Technologies for Continuous Glucose Monitoring: Current
Problems and Future Promises ”, Journal of Diabetes Science and
Technology Volume 4, Issue 6, November 2010 © Diabetes Technology
Society .
Saina Sunny, S.Swapna
Kumar, “Optical Based Non Invasive
Glucometer with IoT ”, 2018 International Conference on Power,
Signals, Control and Computation (EPSCICON) January 2018.
http://life.nthu.edu.tw/~labcjw/BioPhyChem/Spectroscopy/beerslaw.htm
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