Computer Methods in Biomechanics and Biomedical

Engineering: Imaging & Visualization

ISSN: 2168-1163 (Print) 2168-1171 (Online) Journal homepage: www.tandfonline.com/journals/tciv20

Non-invasive glucometer monitoring system

through optical based near-infrared sensor
method

S. Vanaja, Ravi Babu T, M. Malathi, Kuldeep K Saxena, J. Joselin Jeya Sheela,

S. Suruthi, Stalin .B, N. Nagaprasad, Ramaswamy Krishnaraj, Din Bandhu &
Uma Reddy

To cite this article: S. Vanaja, Ravi Babu T, M. Malathi, Kuldeep K Saxena, J. Joselin Jeya Sheela,
S. Suruthi, Stalin .B, N. Nagaprasad, Ramaswamy Krishnaraj, Din Bandhu & Uma Reddy
(2024) Non-invasive glucometer monitoring system through optical based near-infrared
sensor method, Computer Methods in Biomechanics and Biomedical Engineering: Imaging &
Visualization, 12:1, 2327423, DOI: 10.1080/21681163.2024.2327423

To link to this article: https://doi.org/10.1080/21681163.2024.2327423

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Group.

Published online: 01 Apr 2024.

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION
2024, VOL. 12, NO. 1, 2327423
https://doi.org/10.1080/21681163.2024.2327423

Non-invasive glucometer monitoring system through optical based near-infrared

sensor method
S. Vanajaa, Ravi Babu Tb, M. Malathic, Kuldeep K Saxenad, J. Joselin Jeya Sheelae, S. Suruthia, Stalin .Bf, N. Nagaprasadg,
Ramaswamy Krishnarajh,i, Din Bandhuj and Uma Reddyk
a
Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, India; bDepartment of Electrical and Electronics
Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India; cDepartment of Electronics and Communication Engineering,
Rajalakshmi Institute of Technology, Chennai, India; dDivision of Research and Development, Lovely Professional University, Phagwara, India;
e
Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,
Thandalam, Chennai, India; fDepartment of Mechanical Engineering, Anna University, Regional Campus Madurai, Madurai, Tamil Nadu, India;
g
Department of Mechanical Engineering, ULTRA College of Engineering and Technology, Madurai, Tamil Nadu, India; hCentre for Excellence-
Indigenous Knowledge, Innovative Technology Transfer and Entrepreneurship, Dambi Dollo University, Dembidolo, Oromia Region, Ethiopia;
i
Department of Mechanical Engineering, Dambi Dollo University, Dembidolo, Oromia Region Ethiopia; jDepartment of Mechanical and Industrial
Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India; kDepartment of Artificial Intelligence
and Machine Learning, New Horizon College of Engineering, Bangalore, India

ABSTRACT ARTICLE HISTORY

Diabetes is a fast-developing medical issue that causes most renal and cardiac illnesses. Thus, diabetes Received 2 January 2024
management requires regular glucose monitoring. One potential technology is non-invasive glucometer Accepted 1 March 2024
monitoring. This work aims to develop a user-friendly near-infrared sensor-based non-invasive glucose KEYWORDS
monitoring system, correlating sensor output voltage variations with glucose levels, to provide accurate Photodetector; fingerpick;
and convenient glucose monitoring for diabetes management. The objective is to validate the system’s OPT101 sensor; non-invasive
accuracy against existing fingerpick methods and analyze its performance across different age groups glucose monitoring;
and food intake conditions through experimental testing and Clarke grid analysis. In our research, we prototype system; Avalanche
propose a near-infrared sensor-based non-invasive-type glucose monitoring technique which is a user- photodiode
friendly system. The experimental setup and prototype system are designed and implemented for
measuring the variation of glucose level with respect to a sensor output voltage. Using Beer Lambert’s
law, the established results correlated the absorbance property of light with the sample concentration
level. Demonstration of testing for different aged people was done under various food intake conditions.
The obtained results are tabulated and validated with the existing fingerpick method and achieved an
accuracy of 97.8%. Also, Clarke grid analysis has been done and depicted the pattern obtained.

1. Introduction
immunity network damages the beta cells in the pancreas.
In this contemporary world, diabetes has become a common Type 2 occurs when the body is subjected to insulin resistance
metabolic disorder where the blood glucose concentration is and sugar content develops in our blood (Vashist 2013).
abnormal (Peng et al. 2022). It occurs when our body does not Genetic factors and lifestyle factors become the predominant
produce effective insulin. Over half of the Indian population is cause of diabetes type 2. Gestational diabetes occurs during
supposed to have diabetes with estimated 77 million individuals pregnancy period where the placenta in women creates insulin
in 2019, which is alarmingly supposed to reach almost 140 million blocking hormones leading to high blood sugar levels.
around 2050 (Pradeepa and Mohan 2021). In this urbanisation Diabetes can lead to adverse complications such as cardiovas­
period, sedentary habitual life increases social stress which also cular, neurological and retinopathy health problems. It can also
leads to the risk of diabetes. Hence, diabetes is an escalating lead to hearing loss, foot infections with sores, skin prone to
medical problem with life risk factors which has to be viewed bacterial infections and depression and dementia.
seriously and research is blooming to tackle the complications. Various medications are available to treat diabetes including
Type I and type II and gestational are the categories of insulin injections, diet charts, exercise plans, and so on. Despite
diabetes. Type 1 diabetes might be caused when there is all this, once diabetes is diagnosed, health care monitoring
a deficiency in secretion of insulin in the pancreas, which is an becomes routine, and in specific, self-testing of blood sugar
immunological disease (Notkins 2002). Here, the biological levels at frequent times in a day would be mandatory to track

CONTACT Ramaswamy Krishnaraj prof.dr.krishnaraj@dadu.edu.et Centre for Excellence-Indigenous Knowledge, Innovative Technology Transfer and
Entrepreneurship, Dambi Dollo University, Dembidolo, Oromia Region, Ethiopia; Din Bandhuj din.bandhu@manipal.edu Department of Mechanical and
Industrial Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India
This article has been republished with minor changes. These changes do not impact the academic content of the article.
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the
posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
2 S. VANAJA ET AL.

the progress of treatment irrespective of age and type of 850 nm, 660 nm, and 535 nm) for analysing the average corre­
diabetes. Traditionally, blood sugar levels are determined inva­ lation coefficient. The in vivo experiment was done using
sively either by using a Blood Drawn Test (BDT) or finger stick a limited number of 12 different subjects. The sensitivity and
blood test. In BDT, a large volume of blood is collected from the correlation coefficient for the different subjects is around 6.16
subject using a syringe in the test lab, and then by adding mg/dL and 0.86. In the research work [13], they conducted in-
a reagent, the blood glucose concentration is measured. vitro experiments and in-vivo testing for glucose concentration
Whereas the finger stick method (Haxha and Jhoja 2016) col­ identification. An infrared source of 940 nm is an illuminated
lects blood samples by pricking the finger and determining the ray of light with a PIN photodiode onto the human fingertip to
glucose level in the blood. In the glucose monitoring phase, the detect the light transmitted. It has been evaluated by obtaining
patient has to prick their finger at frequent intervals in a day the correlation between output potential and glucose level.
which creates pain, hurt, tissue damage and also skin In this paper (Althobaiti and Al-Naib 2021) it has been
punctures. proposed that a dual channel near-infrared sensor at 1200 nm
In the Continuous Glucose Monitoring (CGM) method, the to 1900 nm is used to analyse the responsivity of the top and
sensor with transmitter and receiver determines interstitial middle layers of skin (epidermis and dermis). The main aim of
fluid rather than the bloodstream (Klonoff 2007). It also this analysis is to eliminate the interfering noisy signal from the
requires twice fingerpricks per day for accurate CGM measure­ skin’s top layer for accurately detecting the glucose content in
ment. Stress before blood collection, pain suffered by the blood. A consumer-based wearable non-invasive device
patients, risk of external environmental infection, and irrita­ (iGLU 2.0) (Joshi et al. 2020) was used to measure serum glu­
tion are major issues in invasive methods. In addition, the strip cose. For predicting serum glucose, polynomial regression and
used for the fingerprick method may show inaccurate results neural network were followed. The smart healthcare technique
due to improper storage of the strip and when exposed to is adopted by linking the Internet of Medical Things framework
external environmental factors. In underdeveloped countries, with end users. In this paper (Li and Li 2015) authors proposed
disposable needles are reused due to the expensive cost of a monitoring system using glucose aqueous solutions in which
needles. a laser diode is a transmitter, and a light power probe (S302C) is
Hence, research interest focuses on non-invasive techniques a detector of the light source. It was found that a high correla­
to detect glucose levels without creating much damage to tion exists between glucose level and output power. In vitro
human tissues. Various non-invasive methods include optical testing of glucose samples was performed using a dilute solu­
glucose monitoring methods, fluid sampling, microwave meth­ tion and in vivo experiment was performed using a sensor
ods, and minimally invasive and electrochemical methods. patch over the forearm. The glucose concentration is extracted
Optical methods ranging in the near-infrared (NIR, 680–2500 from the acquired data by adopting a suitable signal-proces­
nm) have a great contribution to blood glucose detection, sing technique for a limited number of subjects (Yadav et al.
where light can penetrate body fluids and soft tissues for less 2014).
than 0.05 cm, whose scattering ability is less than the ultraviolet In this review of numerous pieces of research, it has been
or visible light range and also measured by both reflection and found that signal-to-noise ratio has a great impact on near
transmission sensing nature (Tang et al. 2020). infrared-based glucose detection method as measurement
site plays a key role which may be easily affected by external
factors. Measurement tools have been deduced to be designed
2. Related work with great care to have high accuracy and proper sensitivity to
In this research work (Sridevi et al. 2021) they have used light- have a good signal-to-noise ratio. Filtering can be performed to
emitting diode of about 940 nm along with a photodiode for eliminate frequency-sensitive noises in the baseband optical
the non-invasive technique. To verify the sensitivity of the signal. Also, the major challenge falls in the design of wearable
device, a vitro test was done in different glucose concentrations devices to accurately measure blood serum glucose, which
which were performed using an Easy Touch GCHb glucometer. should also be applicable for various diabetic conditions.
The results show that voltage decreases with a rise in glucose From all the observed flaws and challenges, to tackle the
level due to absorption of light intensity. In this paper (Saleh problems stated, we propose a non-invasive measurement
et al. 2018) authors have proposed near-infrared (NIR) transmit­ device to measure blood serum glucose by continuous means
tance spectroscopy to measure the amount of glucose content to provide data accurately. The process has been explained in
in the blood. Artificial mixing of sugar mixed with aqueous detail in the following sections.
distiled water identified the glucose concentration by emulat­
ing the body region. The noise power level is reduced by notch
3. Methodology
filter usage, and correspondingly accuracy has been elevated.
Collective optical signals are utilised to estimate glucose levels In this article, two different approaches, namely the experimen­
(Segman 2018) where tissue glucose concentration at the fin­ tal approach and prototype system are presented to inspect
gertip is predicted based on the colour image sensor. the glucose level in the blood frequently. All methods were
Predicted the blood glucose from the subject’s wrist using carried out in accordance with relevant guidelines and regula­
a visible and near-infrared (Vis-NIR) optical-based wearable tions. All experimental protocols were approved by Easwari
sensor (Rachim and Chung 2019). The voltage value is Engineering College (NO. TH5684239) and also informed con­
computed for four different channels of wavelength (950 nm, sent was obtained from all the participants.
 COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 3

Figure 1. Experimental setup workflow diagram.

4. Experimental setup 5.2. Receiver section

The complete workflow diagram for the experimental setup The transmitted light could pass through 0.4 to 0.8 inches of
with glucose samples is shown in Figure 1. If the light is incident dermis layer, and hence the detector should be fair enough to
on a material, a particular amount of passed beam gets con­ respond to it for the absorption of near-infrared light by glu­
sumed based on the concentration of the subject material. In cose component in the blood molecules. Below the fingertip, is
the proposed methodology, the artificial mixing of aqueous the detector, which consists of the OPT101 sensor. The sensor
distiled water with sugar helps to identify the glucose concen­ used in the detector is made up of optoelectronic type along
tration by emulating the body region. A designed electrical with current to voltage converters followed by operational
circuit comprising NIR LED (940 nm wavelength) as amplifiers which are integrated with a single IC as shown in
a transmitter and the receiver is Avalanche photodiode Figure 4. OPT101 has good sensitivity towards 940 nm wave­
(FGA015) of 940 nm wavelength. In the middle of the NIR LED length being transmitted and has added merits of small size,
and photodiode, a glucose solution is positioned. When the NIR low cost, compact in nature, high responsivity of 0.57 V/µW,
LED transmits light, a portion of it is consumed by the liquid less leakage error and more suitable for wearable devices.
phantom. The receiver photodiode will spot the amount of
transmitted light and develop an equivalent electrical current
for the respective concentration of the glucose solution. The 5.3. Data acquisition system
relationship between the glucose concentration and current is The data acquisition system has a data sensing unit and an ADC
studied and analysed. unit. The data acquisition unit consists of Arduino interfaced
The accurate weight of the sugar is determined by the with MATLAB which transmits the output obtained from
precise weight equipment (ELB300 PLATFORM BALANCE) is OPT101 to the Laptop via a USB connection. It analyses the
shown in Figure 1. The complete experimental setup to identify collected sensor data and processes the acquired voltage value
the amount of current and its proportional voltage flow due to by considering attenuation to its equivalent corresponding
light absorbed by the sample is shown in Figure 2. wavelength. MATLAB stores the data as variables in text files.
The Arduino program is set to process each sample with a delay
of 0.5 s estimating 120 data units for every 1-min duration. The
5. Prototype system ADC converts the analog voltage input collected from the
The complete workflow diagram for the blood glucose level mon­ sensor output into digital values between 0 and 1023.
itoring system using the prototype system is shown in Figure 3.

5.4. FFT
5.1. Transmitter section The digital information obtained from the data acquisition
Initially, the source identification is crucial as the light incident system, which is in the time domain, is processed into the
on the subject should be properly intact with the fingertip. frequency domain by FFT algorithms before being subjected
Also, tiny-sized with not much heavyweight type of light to filtering operation. FFT helps to analyse spectral content as
sources are preferred for illumination. The near-infrared wave­ well as phase information.
length of 940 nm is found to be optimal, which has the char­
acteristics of diffusing into organic tissue without much
5.5. Blackman filter
absorption by other biological components present in human
membranes like water, lipids, and proteins. The compound The digitalised data is then filtered to remove the noise due to
semiconductor Al GaAs would be the best choice for source environmental effects such as water, fat, protein and so on
base material which could radiate at the optimal wavelength in present in the blood (Ingle and Crouch 1988). In the proposed
a range of 20 mW to 30 mW. To achieve the area coverage system, the Blackman filter is used to remove the low-fre­
exactly in the penetration region, the LED is positioned at quency and high-frequency tones ranging from 2.34 Hz to
a right angle to the fingertip. 1.59 kHz.
4 S. VANAJA ET AL.

Figure 2. Experimental setup (a) circuit diagram (b) experimental setup.

Blackman window equation for the length N is of the sample as shown in Figures 5 and 6. When a light
beam passes over a uniform sample, the intensity of the
transmitted radiation declines with the increase in thick­
ness and concentration of the liquid phantom.
The transmittance of the liquid phantom is based on absor­
bance (A) and the optical depth

5.6. Estimating glucose concentration

Beer-Lamberts law clearly states the absorbance property
of materials on any kind of sample. It takes into account
sample concentration, medium thickness, platform tem­ Or
perature, and wavelength of radiation. It is defined as the
amount of light absorbed in a homogeneous medium that
is directly proportional to the concentration and thickness
 COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 5

Figure 3. Workflow diagram for the blood glucose monitoring system.

where, T; A and τ is the term of transmittance, absorbance, and

optical depth, ;te corresponds to transmitted radiant flux, ;ie is
the received radiant flux, the intensity of light entering the
liquid sample is I0 whereas leaving the sample is IT .
As per the Beer-Lambert law

Or equivalently

Figure 4. OPT101 light intensity sensor.

Figure 5. Passing of light beam in liquid phantom.

6 S. VANAJA ET AL.

Where l represents path length; N represents attenuating mate­

rial number density; 2 represents attenuation coefficient; σ is
the cross-section of the attenuation material; c represents
attenuating material molar concentration.
Under consistent attenuation, the equation follows as

Hence Beer-Lambert law shows the correspondence between

the concentration and absorbance of light by a sample.

6. Results and discussion

In this section, the results of both the experimental approach
Figure 6. Passing of light beam in human fingertip. and prototype system are presented and explained in detail.

Figure 7. Variation in voltage flow for the corresponding glucose concentration.

Figure 8. Variation in current flow for the corresponding concentration of glucose.

 COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 7

6.1. Experimental approach results glucose in the liquid phantom increases. Hence,
a downward pattern is observed in Figure 8 which clearly
The accurate weight of the sugar is determined by the precise
helps to conclude that the blood glucose concentration (mg/
weight measure equipment notably ELB300 PLATFORM
dl) is inversely proportional to the current emitted from the
BALANCE, as shown in Figure 2. A liquid phantom or sample
photodetector.
is created by mixing sugar with distiled water of 30 ml is used
Similarly, the voltage from the photodetector declines with
for the experimentation.
the increase in glucose concentration because of light-intensity
The experimental results obtained from the setup in
Figure 2 for identification of the amount of current and its absorption by the glucose molecules present in the liquid
proportional voltage flow due to light absorption by phantom. Hence, a downward pattern of voltage is observed
a sample are presented in Figure 7 as a graphical represen­ in Figure 7. Therefore, it is concluded that the concentration of
tation. It is clearly shown that the current from the receiver glucose (mg/dl) in the blood is inversely proportional to the
photodetector end decreases when the concentration of voltage generated by the photodetector.

Figure 9. Prototype of the complete glucose estimation system (a) prototype setup (b) fingertip subject placed over OPT101 (c) transmission and reception of optical
light (d) entire set-up covered with black cloth.
8 S. VANAJA ET AL.

6.2. Prototype results two hours of food intake in the morning, the estimated values
are shown in Figure 10.
A group of different age people with diabetic and non-diabetic
Initially, the subject (girl) underwent blood glucose testing in
conditions were preferred to analyse the performance of the
an optical mode without having food. During fasting conditions,
proposed prototype system. The concentration of blood glu­
the intensity of light received between the NIR LED and the
cose examined in the proposed prototype system for a girl of
optical receiver is found to be less. The concentration of blood
age 20 years is shown in Figure 9. The entire prototype setup is
glucose level during fasting (without food) is measured as 85
demonstrated by arranging the optical transmitter and receiver
mg/dl. As per the Beer-Lamberts law, the light traversing through
over the breadboard with Arduino as shown in Figure 9(a).
a subject possessing blood would absorb the photon molecule.
Later, the fingertip subject is placed above the optoelectronic
As the concentration of glucose level increases, the amount of
sensor (OPT 101) to test the concentration of glucose in the
photon molecules absorbed by the blood also increases.
blood sample as shown in Figure 9(b). Afterwards, the NIR LED
Therefore, with respect to transmitted light intensity, blood glu­
is placed in contact above the forefinger to transmit the light as
cose concentration value varies at the proposed system output.
shown in Figure 9(c). To assure maximum detector efficiency, it
Afterwards, high carbohydrate food (bread and milk) has been
is essential to block or minimise the amount of ambient light in
taken by the subject (girl). The food is digested in the small intes­
the room. Accordingly, the entire set-up of the prototype is
tine, and the carbohydrates are absorbed as glucose in the blood.
completely screened by black cloth as shown in Figure 9(d).
Meanwhile, the intensity of light received by the OPT 101
In the first stage of the data acquisition, a girl of age 20 years
decreases from the previous intensity level. The estimated glucose
old is examined for the identification of concentration variation
level in the blood after one hour is 123 mg/dl. Likewise, the glucose
in blood glucose. Initially, with respect to the prototype, power
level in the blood is estimated after one and a half hours and after
supply is given to the NIR LED (AlGaAs LED) to emit the invisible
two hours of food intake which is shown in Figure 10.
light. The forefinger of the subject is placed above the optoe­
lectronic sensor (OPT 101) to test the concentration of glucose
in the blood sample through optical mode. Later, the NIR LED is
placed in contact above the forefinger to receive the light 7. Validation
passing through the medium. The subject took the test for The results obtained are validated by the comparison experi­
four different conditions namely fasting, after one hour of mentation using the existing invasive fingerpick method which
food intake, after one and half hours of food intake, and after is shown in Figure 11. Table 1 lists the results obtained from four

Figure 10. Estimated glucose level using non-invasive technique.

Figure 11. Validation of Non-invasive technique using an invasive device for a person.
 COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 9

Table 1. Correlation between the commercially available fingerprick method (invasive) and sensor-based method (non-invasive) on four volunteers.
Subject 2 glucose concentration Subject 3 glucose concentration Subject 4 glucose concentration
Subject 1 glucose concentration (mg/dl) (mg/dl) (mg/dl) (mg/dl)

Non-invasive Invasive Error Non-invasive Invasive Error Non-invasive Invasive Error Non-invasive Invasive Error
technique device (%) technique device (%) technique device (%) technique device (%)
85 81 4.9 87 82 6.1 92 96 4.1 202 199 1.5
123 118 4.2 125 119 5.0 126 129 2.3 232 225 3.1
98 95 3.1 88 90 2.2 102 107 4.6 215 212 1.4

different conditions namely fasting, after one hour and one and
a half hours after food intake. The proposed non-invasive method
is almost accurate with respect to the invasive method and shows
a high correlation between the variation in photon intensity
received from the photodetector and glucose level. Finally, from
Clarke grid analysis, data points obtained from the test measure­
ments are at a satisfactory level, showing that our developed non-
invasive glucose monitoring system has better accuracy. In the
future, the impact of sensor system performance due to skin
roughness and body fluids concentration can be analysed to
improve the calibration and system sensitivity.

Disclosure statement
This study was performed as a part of the employment of the author(s).
Figure 12. Clarke grid error analysis.

Notes on contributors
different people at three conditions namely fasting, one hour Dr. S. Vanaja is an Assistant Professor in the Department of Electronics and
after food intake, and one and a half hours after food intake using Communication Engineering, Easwari Engineering College, Chennai-
both existing invasive technique and the proposed non-invasive 600089, India. She received his Ph.D. degree in Electronics and
technique. The accuracy level is tested by error analysis using the Communication Engineering from Anna University, Chennai, India. She
percentage error formula. has published over 10 SCI Indexed journals and 5 Scopus Indexed journals.
Percentage error = [Measured value (non-invasive method) – Dr. T. Ravi Babu is an Assistant Professor in the Department of Electrical and
Actual value (invasive method)]/Actual value] *100 Electronics Engineering, Institute of Aeronautical Engineering, Hyderabad,
Telangana, India. He received his Ph.D. degree in Electrical and Electronics
Engineering from Anna University, Chennai, India. He has published over 15
SCI Indexed journals and 8 Scopus Indexed journals.
8. Clarke error grid analysis
Dr. K. Malathi is an Assistant Professor in the Department of Electronics and
Clarke error grid study shows the accuracy level of the pro­ Communication Engineering, at Rajalakshmi Institute of Technology, India.
posed method, glucose level monitoring by non-invasive, opti­ She received his Ph.D. degree in Electronics and Communication
cal-based NIR technique in comparison with the predicted Engineering from Anna University, Chennai, India. She has published over
concentration is shown in Figure 12. Here, the region spread 10 SCI Indexed journals and 3 Scopus Indexed journals.
by A represents the clinically accurate conditions and the Prof. Kuldeep K Saxena has 13+ years of experience in academics, research,
region spread by B and C represents an acceptable range, and industry. Prof. Saxena holds expertise in the hot deformation behavior
of materials, microstructural characterization of materials, and micromanu­
whereas D and E are erroneous for treatment.
facturing. He has served as Senior Research Fellow (SRF) for 2 years and 8
months on a project sponsored by the Board of Research in Nuclear
Sciences (BRNS), a research unit of Bhabha Atomic Research Centre,
9. Conclusions Trombay, Mumbai. He is the author of many book chapters published by
reputable publishers such as Elsevier and many more. He has authored 272
This research article employs a unique approach to demonstrate
+ research papers which are published in reputed international journals
the relationship between the glucose concentration and the sen­ indexed by SCI/ Scopus. He has organized many International Conferences
sor output voltages based on the received light intensity from Near in India and Abroad. He is currently working as a Professor and Head of the
Infrared LED, where the sensor voltage decreases with an increase Department of Research Impact and Outcome (LFTS) in the Division of
in glucose concentration. The proposed NIR LED-based glucose Research and Development, Lovely Professional University, Phagwara,
India. He is an active member of The Indian Institute of Metals (IIM) and
sensor prototype is a promising invasive monitoring technique for
Secretary of The Indian Institute of Metals Mathura Chapter. He is also a
the glucose levels in the blood. The prototype proposed has been guest editor in many reputed journals.
experimented, and results are obtained and depicted in Figures 5–
Dr. J. Joselin Jeya Sheela is an Assistant Professor in the Department of
7. The test results are displayed within 50 s of duration. The Electronics and Communication Engineering, Saveetha School of
proposed non-invasive prototype is validated against the tradi­ Engineering, Chennai- 600124, India. She received his Ph.D. degree in
tional invasive methods for four different volunteers under three Electronics and Communication Engineering from Anna University,
10 S. VANAJA ET AL.

Chennai, India. He has published over 25 SCI Indexed journals and 8 Scopus Data availability statement
Indexed journals.
The data used to support the findings of this study are included in the article.
Dr. S. Suruthi is an Assistant Professor in the Department of Electronics and
Communication Engineering, Easwari Engineering College, Chennai-
600089, India. She received his Ph.D. degree in Electronics and
Communication Engineering from Anna University, Chennai, India. He has References
published over 2 SCI Indexed journals and 4 Scopus Indexed journals.
Althobaiti M, Al-Naib I. 2021. Optimization of dual-channel near-infrared
Dr. B. Stalin is an Assistant Professor in Mechanical Engineering at Anna non-invasive glucose level measurement sensors based on monte-carlo
University, Regional Campus Madurai, Madurai 625 019, Tamil Nadu, India. simulations. IEEE Photonics J. 13(3):1–9. doi: 10.1109/JPHOT.2021.3079408.
He received his Ph.D. degree in Mechanical Engineering from Anna Haxha S, Jhoja J. 2016. Optical based noninvasive glucose monitoring sensor
University, Chennai, India. His research interests include the Design and prototype. IEEE Photonics J. 8(6):1–11. doi: 10.1109/JPHOT.2016.2616491.
manufacturing of composite structures and Structural analysis using FEA. Ingle JD, Crouch SR. 1988. Spectrochemical analysis. 1st ed. New Jersey:
He has published over 100 SCI Indexed journals and 50 Scopus Indexed Prentice Hall.
journals. Joshi AM, Jain P, Mohanty SP, Agrawal N. 2020. iGLU 2.0: A new wearable for
Dr. N. Nagaprasad is an Associate Professor in Mechanical Engineering at accurate non-invasive continuous serum glucose measurement in IoMT
ULTRA College of Engineering and Technology, Madurai, Tamilnadu, India. framework. IEEE Trans Consum Electron. 66(4):327–335. doi: 10.1109/
He received his Ph.D. degree in Mechanical Engineering from Anna TCE.2020.3011966.
University, Chennai, India. His research interests include the Design and Klonoff DC. 2007. The benefits of implanted glucose sensors. J Diabetes
manufacturing of composite structures and Structural analysis using FEA. Sci Technol. 1(6):797–800. doi: 10.1177/193229680700100601 .
He has published over 78 SCI Indexed journals and 15 Scopus Indexed Li X and Li C, 2015, October. Research on non-invasive glucose concentration
journals. measurement by NIR transmission. In 2015 IEEE International Conference on
Dr. R. Krishnaraj is a Professor in the Department of Mechanical Computer and Communications (ICCC) p. 223–228.
Engineering at Dambi Dollo University, Dembi Dollo, Ethiopia. He Notkins AL. 2002. Immunologic and genetic factors in type 1 diabetes. J Biol
received his Ph.D. degree in Mechanical Engineering from Anna Chem. 277(46):43545–43548. doi: 10.1074/jbc.R200012200.
University, Chennai, India. His research interests include Peng X, Yan YX, Liu H. 2022. On the use of fiber lasers in non-invasive blood
Environmental Pollution and Structural analysis using FEA. He has glucose monitoring. Opt Fiber Technol. 68:102822. doi: 10.1016/j.yofte.
published over 100 SCI Indexed journals and 45 Scopus Indexed 2022.102822.
journals. Pradeepa R, Mohan V. 2021. Epidemiology of type 2 diabetes in India.
Indian J Ophthalmol. 69(11):2932. doi: 10.4103/ijo.IJO_1627_21.
Dr. Din Bandhu is presently working as an Assistant Professor (Senior Rachim VP, Chung WY. 2019. Wearable-band type visible-near infrared
Scale) in the Department of Mechanical and Industrial Engineering at optical biosensor for non-invasive blood glucose monitoring. Sens
Manipal Institute of Technology (MIT), MAHE Bengaluru Campus,
Actuators B Chem. 286:173–180. doi: 10.1016/j.snb.2019.01.121.
Karnataka, India. Before joining MIT, he worked as an Assistant
Saleh G, Alkaabi F, Al-Hajhouj N, Al-Towailib F, Al-Hamza S. 2018. Design of
Professor in Mechanical Engineering at IIITDM, Kurnool (An Institute
non-invasive glucose meter using near-infrared technique. J Med Eng
of National Importance, Govt. of India). He holds a Ph.D. in Mechanical
Technol. 42(2):140–147. doi: 10.1080/03091902.2018.1439114.
Engineering from IITRAM Ahmedabad (A Research-Based Autonomous
Segman Y. 2018. Device and method for noninvasive glucose assessment.
University) in Gujarat. He has over 12 years of academic and research
J Diabetes Sci Technol. 12(6):1159–1168. doi: 10.1177/1932296
experience at many prestigious Indian institutions. Advanced welding
818763457.
techniques, welding metallurgy, characterization, composite materials,
Sridevi P, Arefin AS, Ibrahim ASM. 2021. A feasibility study of non-invasive
performance optimization, and biofuels are among his research inter­ blood glucose level detection using near-infrared optical spectroscopy.
ests. So far, he has published 75 SCI/SCIE/ESCI and Scopus-indexed Bangla J Med Phys. 14(1):1–13. doi: 10.3329/bjmp.v14i1.57313.
research papers in various international journals and conferences of Tang L, Chang SJ, Chen CJ, Liu JT. 2020. Non-invasive blood glucose monitoring
repute. He has also written one book titled ‘A Guide to Injection technology: a review. Sensors. 20(23):6925. doi: 10.3390/s20236925.
Moulding Techniques’. He is also a potential reviewer for many peer- Vashist SK. 2013. Continuous glucose monitoring systems: a review.
reviewed international journals and conferences. Diagnostics. 3(4):385–412. doi: 10.3390/diagnostics3040385.
Dr. Uma Reddy is a Professor & Head of the Artificial Intelligence and Yadav J, Rani A, Singh V and Murari BM, 2014, February. Near-infrared LED
Machine Learning Department at New Horizon College of Engineering, based non-invasive blood glucose sensor. In 2014 International
Bangalore, Karnataka. She has published several articles in reputable jour­ Conference on Signal Processing and Integrated Networks (SPIN);
nals and conferences. Noida, India. IEEE. p. 591–594.