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iGLU: An Intelligent Device for Accurate
NonInvasive Blood Glucose-Level Monitoring
in Smart Healthcare
Prateek Jain Amit M. Joshi
Malaviya National Institute of Technology Malaviya National Institute of Technology
Saraju P. Mohanty
University of North Texas
Abstract—In case of diabetes, fingertip pricking
for blood sample is inconvenient for glucose mea-
surement. Invasive approaches like laboratory test
and one touch glucometer enhance the risk of blood
related infections. To mitigate this important issue,
in the current paper, we propose a novel Internet-
of-Medical-Things (IoMT) enabled edge-device for
precise, non-invasive blood glucose measurement. The
novel device called “Intelligent Glucose Meter” (i.e.
iGLU) is based on near-infrared (NIR) spectroscopy
and machine learning (ML) model of high accuracy.
iGLU has been validated in a hospital and blood
glucose values are stored IoMT platform for remote
monitoring by endocrinologist.
I. INT ROD UC TI ON
SMart healthcare system comprises of ambient
intelligence, quality of service and also offers
continuous support of the critical diseases moni-
toring [1], [2]. This system is most demandable for
remote monitoring of diabetic patients with low cost
and rapid diagnosis [3]. Traditional blood glucose
measurement is unable to serve everyone’s need
at remote location. Despite having good diagnos-
tic centers for clinical test facility in urban area,
medical services are not approachable to everyone
at remote location [4], [5]. It is necessary to monitor
blood glucose of diabetic patients where diagnosis
facility are not easily available. The instant di-
agnose of blood glucose and frequent monitoring
are the recent challenges in smart healthcare. The
process flow of blood glucose diagnosis in smart
healthcare is depicted in Figure 1.
Non Invasive Measurement
Fasting and Postprandial
Blood Glucose Values (mg/dl)
Internet Connectivity
Internet Connectivity
Connecting to the
available Endocrinologist
Blood Glucose
Readings of the Patient
Prescribed Medicine and
Precautions for Instant
Treatment
Cloud Storage
Nearest Hospital
For Treatment
Endocrinologist
Fig. 1: Blood glucose diagnosis in smart healthcare.
IoMT enabled handheld non-invasive glucose
measurement end-device has strong potential for
rapid monitoring as well as to facilitate the interact
with endocrinologist to the remote located diabetic
patients where diagnosis centers and hospitals are
not easily available. According to this environment,
patients measure their glucose without pricking
blood and directly store to the cloud where nearby
endocrinologist can monitor the glucose data of
each patient. The prescription would also be pro-
vided by endocrinologist to the remote located
patient for further treatment. The ubiquity of di-
abetic patients has become double from 2010 over
the world. The estimated diabetes dissemination
from 2009 is 290 million and is expected to affect
450 million people by 2030. Hence, it is essential
2
to develop the glucose measurement device for
rapid diagnosis of diabetes. People will be more
conscious for their glucose level with frequent mon-
itoring. Invasive method for glucose measurement
is not advisable in case of continuous monitoring.
Therefore, it required to design the non-invasive
device for clinical tests, which is beneficial for
health care. In proposed work, optical detection is
involved. Blood glucose is predicted by machine
learning based computation model.
II. STATE-OF-ART IN BLOOD GLUCOSE-LEVEL
MEASUREMENT
Blood glucose measurement is possible using
invasive, minimally invasive and non-invasive meth-
ods (Figure 2). Frequent pricking, as needed in
invasive methods, for glucose measurement causes
trauma. Therefore, the semi-invasive approach has
the advantage of continuous glucose monitoring
without multiple times pricking. However, non-
invasive methods can completely eliminate prick-
ing which opens door to painless and continuous
glucose monitoring (CGM).
A. Invasive Methods
A low-invasive amperometric glucose monitoring
biosensor has been proposed using fine pointed glu-
cose oxidase immobilized electrode which doesn’t
require more than 1mm in length to be inserted
in skin [6]. A fully implanted first-generation pro-
totype sensor has been presented for long-term
monitoring of subcutaneous tissue glucose [7]. This
wearable sensor which is integrated as an implant
is based on a membrane containing immobilized
glucose oxidase and catalase coupled to oxygen
electrodes, and a telemetry system.
B. Minimally Invasive Methods
Implantable biosensors have been deployed for
continuous glucose monitoring [8]. Wearable min-
imally invasive microsystem has been explored for
glucose monitoring [9]. A microsystem has been
presented for glucose monitoring which consists
of microfabricated biosensor flip-chip bonded to a
transponder chip [10]. A method has been discussed
to reduce the frequency of calibration of minimally
invasive Dexcom sensor [11]. An artificial pancreas
has been represented along with glucose sensor
to control diabetes [12]. But, approaches based
semi-invasive devices have not been tried for real
time application. These wearable microsystems are
neither painless nor cost effective solutions.
C. Non-invasive Methods
To make the painless system, photoacoustic spec-
troscopy has been introduced for non-invasive glu-
cose measurement [13]. However, utilization of
LASER makes the setup costly and bulky. An
enzyme sensor has been explored for glucose mea-
surement in saliva [14]. Glucose detection is possi-
ble using Intensity Modulated Photocurrent Spec-
troscopy (IMPS) spectroscopy that connects the
electrodes to the skin which is affected by sweat
[15]. High precision level is not possible through
these methods as sweat and saliva properties vary
for individuals. The blood glucose measurement has
also been explored using Raman spectroscopy in
laboratory [16]. The experimental setup for Raman
spectroscopy required a large area and will not be
portable. Glucose measurement has also been done
from anterior chamber of the eye which limits it’s
usage of continuous monitoring [17]. Blood glucose
can been estimated using photoplethysmography
(PPG) signal [18], [19].
PPG signal analysis is not based on principle
of glucose molecule detection. Therefore, specific
wavelengths are not required for glucose estimation.
Hence, iGLU is more precise compared to PPG sig-
nal analysis based system for glucose measurement.
In this way, long NIR wave for optical detection has
been considered for glucose measurement which
is not comparatively precise glucose measurement
system as long wave has shallow penetration [20].
Therefore, small NIR wave is preferred for glucose
detection (Figure 3).
Prior works related to glucose monitoring have
been discussed which represent wearable and non-
wearable approaches. Raman spectroscopy, photoa-
coustic spectroscopy and invasive approach based
systems are not wearable. Minimally invasive de-
vices which have been discussed, are implantable.
Other approaches based non-invasive device are
wearable. Here, iGLU is non-invasive, optical de-
tection based wearable device for continuous glu-
cose monitoring with IoMT framework.
3
Blood Glucose
Monitoring System
Invasive Minimally Invasive Non Invasive
In-vivo In-vitro
Photoacoustic
Raman
Spectroscopy Polarimetry Photoplethysmography
(PPG) Signal
Long Wave Near-
Infrared (NIR)
Detection
Implanted Micro
System
Glucose Control
System
Electrochemical
Biosensor
Implanted Sensor Amperometric
Near-Infrared (NIR)
Spectroscopy -
iGLU
Bio-impedance
Fig. 2: An overview of various blood glucose-level measurement devices or systems.
LED Detector
Logged voltage values
after absorption and
reflectance of light from
glucose molecule
Specific
Wavelengths
needed for glucose
molecule detection
NIR Spectroscopy
LED Detector
Logged signal for pulse
analysis and features
extraction
Specific
Wavelengths are
not required
PPG Signal Analysis
Fig. 3: PPG signal versus NIRS based glucose
estimation
D. Consumer Electronics for Glucose-Level Moni-
toring
Several devices have been developed for non-
invasive blood glucose measurement. Some prod-
ucts such as glucotrack, glutrac, glucowise, DiaMon
Tech and device from CNOGA medical are not
commercialized. Glutrac is multi-parameter health
test device with smart healthcare. However, they
have limitations in terms of precise measurement.
The cost of product is also high which varies in
the range of 300-400 USD. Therefore, the cost
effective solution for non-invasive blood glucose
measurement is needed.
III. NOVE L DEV IC E IGLU TO ADVANC E TH E
STATE-OF-ART IN WEARABLE FOR
CONTINUOUS BLO OD GL UC OS E MONITORING
Non-invasive measurement reduces the possibil-
ity of blood-related diseases. However, this ap-
proach have some limitations such as large set-
up, measuring object (ratina) and skin properties
(including dielectric constant and sweat level).
Therefore, portable non-invasive precise glucose
measurement device for continuous monitoring is
needed. An initial example is a non-invasive glu-
cose measurement using NIR spectroscopy and
Huber’s regression model [21]. There are several
glucose monitoring systems which neither provide
precise measurement nor cost effective solution.
These systems are not enabled for smart healthcare.
The following questions are resolved in iGLU for
the advancement of smart healthcare: (1) How can
we have a device that automatically performs all
the tasks of blood-glucose monitoring at the user
end without internet connectivity and stores the
data in cloud for future use by the patient and
healthcare providers? (2) Can we have a device that
can perform automatically to avoid hassle and risky
finger pricking all the time monitoring is needed?
This article introduces an edge-device called “In-
telligent Glucose Meter” (i.e. iGLU) for noninva-
sive, precise, painless, low-cost continuous glucose
monitoring at the user-end and stores the data
on cloud in an IoMT framework. A non-invasive
device has been proposed with precise and low
cost solution. The proposed device is also integrated
with IoMT where the data is accessible to caretaker
for point of care. The device will be portable after
packaging to use everywhere. The device is fast
operated and easy to use for smart healthcare. The
flow of proposed iGLU is represented in Figure 4.
The contributions this article to advance the state-
of-art in smart healthcare include the following:
1) A novel accurate non-invasive glucometer
(iGLU) by judiciously using short NIR waves
4
Post-
processing
Computation
Model
Estimated
Blood
Glucose
Value
Cloud
Storage
Analog-to-
Digital
Converter
(ADS1115)
Infrared
Emitters
(940nm,
1300nm)
Transmitted
Wave
Attenuated
Wave
Vibrations
(Stretching, Wagging,
Bending)
Infrared
Detector
Infrared
Detector
Fig. 4: A conceptual overview of iGLU.
with absorption and reflectance of light using
specific wavelengths (940 and 1300 nm) has
been introduced. The wavelengths are judi-
ciously selected after experimental analysis
which has been done in material research
center MNIT, Jaipur (India).
2) A novel accurate machine learning based
method for glucose sensor calibration has
been presented with calibrated and validated
healthy, prediabetic and diabetic samples.
3) The proposed non-invasive blood glucose
measurement device has been integrated in
IoMT framework for data (blood glucose val-
ues) storage, patient monitoring and treatment
on proper time with cloud access by both the
patient and doctor.
IV. PROPOSED NON-INVASIVE BLOO D
GLU CO SE MEASUREMENT DEVICE (IGLU)
The proposed device based on NIR spectroscopy
with two short wavelengths is designed and im-
plemented using three channels. Each channel is
embedded with emitter and detector of specific
wavelength for optical detection. The data is col-
lected and serially processed by 16 bit ADC with
sampling rate of 128 samples per second. The
logged data is calibrated and validated thorough
existing regression techniques to analyse the op-
timized model. The flow of data acquisition for
proposed iGLU is presented in Figure 5.
A. The Approach for Glucose Molecule Detection
Glucose molecule vibrates according to its
atomic structure at specific wavelengths. It is an-
Start
Power Supply ON for Emitters and Detectors of
Three Channels and ADS 1115
Connect the Arduino Uno Board to PC through
USB Port
Upload the Program of Data Processing through
ADS 1115 using Arduino 1.8.5 Software
Check
Mode
Placing of objects (Fingers or Ear Lobes) in
Clips for Glucose Measurement
Run the Tera Term Application on PC for Data
Logging
Re-connect the
Arduino Uno Board to
PC through USB Port
Sampling and Averaging of Collected Data from
Three Channels for Calibration and Validation of
Regression Models
Stop
Uploading Failed
Program Uploaded
Fig. 5: Process flow data acquisition for iGLU.
alyzed that absorbance and reflectance are sharper
and stronger in short wave NIR region [22]. The
absorption peak of glucose spectra at 1314 nm has
been analyzed [23]. The non-invasive blood glucose
measurement using 850, 950 and 1300 nm has
been implemented [15]. The 940 nm wavelength for
detection of glucose molecule has been identified
[24]. NIR spectra of sucrose, glucose and fructose
are elaborated with CH2, CH and OH stretching at
930, 960 and 984 nm, respectively [25].
B. Proposed Module for Data Acquisition
Proposed iGLU uses NIR spectroscopy to im-
prove the accuracy. A 2-Layer PCB has been devel-
oped to embed infra-red emitters (MTE1300W -for
1300 nm, TSAL6200 -for 940 nm, TCRT1000 -for
940 nm) and detectors (MTPD1364D -for 1300 nm,
3004MID -for 940 nm, TCRT1000 -for 940 nm).
The hardware is designed for data acquisition from
emitters, detectors and ADC with 5V DC supply.
According to the emitters and detectors, compatible
passive components have been chosen. Architecture
of glucose sensing is shown in Figure 6. Detectors
with daylight blocking filters are packaged and not
affected by sweat. ADS 1115 with 860 SPS, 16
bit, I2Ccompatible and single ended is controlled
through microcontroller ATmega328P and used to
convert the data (in Volts) from all channel in
decimal form. The noise power and signal-to-noise
5
ratio (SNR) have also been found 0.08 and 25.2 dB,
respectively, which show the minimum noise level.
Arduino Uno
Board
GND
O/P
Voltage
10k Ω
GND
Photo
Diode
LED
O/P 2
Power Supply
Node
1300 nm
Channel 1
100Ω
10k Ω
GND
GND
Photo
Diode
LED O/P 3
Object
940 nm
(Reflectance)
Channel 3
A
D
S
1
1
1
5
O/P 2
O/P 1
O/P 3
Power Supply
Data
Output
940 nm
(Absorption)
Channel 2
100 Ω
GND
Fig. 6: Circuit topology of proposed device (iGLU).
C. A Specific Prototype of the iGLU
Absorption and reflectance at 940 nm and ab-
sorption at 1300 nm are implemented for detection
of the glucose molecules. The detector’s voltage
depends on received light intensity. After placing
the fingertip between emitter and detector, the
voltage values are logged. Change in light inten-
sity depends upon glucose molecule concentration.
During experiments, blood glucose is measured
through the invasive device standard diagnostics
(SD) check glucometer for validation of the non-
invasive results. The reading is taken as refer-
enced blood glucose values (mg/dl). During the
process, optical responses through detectors have
been collected from 3 channels simultaneously.
During measurement, the channels data is collected
in the form of voltages from 3 detectors. These
collected voltages correspond to referenced blood
glucose concentration. These voltage values are
converted into decimal form using 4-channel ADS
1115 (Texas Instruments) ADC [26]. Coherent av-
eraging has been done after collection of responses.
Specification of a iGLU prototype are presented in
Table I.
The prototype view of proposed iGLU is shown
in Figure 7. The data is collected after fixing three
fingers in free space of pads. The pads are designed
in such a way that emitters and detectors are placed
beneath the surfaces of pads. Because of this, there
will be enough free spaces between the object
TABLE I: Specification of iGLU prototype
Channel 1 Channel 2 Channel 3
Measured (Ideal)
Arduino Supply 4.95V 4.96V 4.95V
(5V) (5V) (5V)
Forward Voltage 0.96V 1.42V 1.40V
(Emitter) (1.1V) (1.5V) (1.5V)
Forward Current 53.4mA 52.8mA 52.9mA
(Emitter) (100mA) (60mA) (60mA)
Reverse Voltage 4.25V 4.16V 4.25V
(Detector) (5V) (5V) (5V)
Output Current 0.45mA 0.5mA 0.52mA
(Detector) (1mA) (1mA) (1mA)
Measurement range 3.2-4.68V 0.8-4.7V 0.5-4.7V
Specific
Wavelength
1300nm 940nm 940nm
Spectroscopy Absorption Absorption Reflectance
and sensors (emitters and detectors). Hence, the
probability of a faulty measurement is minimized.
Fig. 7: Prototype view of proposed device (iGLU).
V. PROPOSED MACHINE-LEARNING (ML)
BASED METHOD F OR I GL U CALIBRATION
Regression models are calibrated to analyze the
optimized computation model for glucose estima-
tion. The detector’s output from three channels
are logged as input vectors for glucose prediction.
The collected data from the samples is required to
convert in the form of estimated glucose values.
It is necessary to develop optimal model for pre-
cise measurement and hence analysis of MAD,
mARD,AvgE and RM SE are performed to
ensure accuracy. The estimated and reference blood
glucose concentration are calculated as BGE st and
BGRef , respectively. A total of 97 samples are
taken for device calibration which include predi-
abetic, diabetic and healthy samples. The baseline
6
characteristics of samples for calibration is repre-
sented in Table II. The proposed process flow of
calibration and validation is shown in Figure 8.
TABLE II: Baseline characteristics of samples
Samples Basic Calibration Validation
Characteristics and Testing
Age (Years) Gender Wise Samples
Male:- 22-77 Male:- 53 Male:- 64
Female:- 17-75 Female:- 44 Female:- 29
Age (Years) Prediabetic
Male:- 22-65 Male:- 18 Male:- 11
Female:- 26-75 Female:- 13 Female:- 10
Age (Years) Diabetic
Male:- 30-68 Male:- 16 Male:- 17
Female:- 30-73 Female:- 14 Female:- 11
Age (Years) Healthy
Male:- 22-65 Male:- 19 Male:- 36
Female:- 17-70 Female:- 17 Female:- 08
Calibration through
Machine Learning
Technique
Error Analysis from
Predicted Glucose
Concentration
Referenced Blood
Concentration (mg/dl)
Prediction from Calibrated
Machine Learning Model for
Validation
Data Set
(Input Voltage values with
Referenced Blood Glucose)
Proposed Machine
Learning based
Regression Model
Input Vectors
(Voltage values from
3 Channels)
MARD (%)
AvgE (%)
MAD (mg/dl)
RMSE (mg/dl)
Referenced Blood
Concentration (mg/dl)
Fig. 8: The process flow of calibration and valida-
tion of proposed device (iGLU).
Deep Neural Network (DNN) based machine
learning model has been applied for precise blood
glucose prediction (Figure 9) [2]. Proposed DNN
uses sigmoid activation functions and has been
trained through Levenberg-Marquardt backpropaga-
tion algorithm [15]. In proposed model, 10 hidden
neurons and 10 hidden layers are analyzed to esti-
mate the precise blood glucose values. This model
has been used to analyze the non-linear statistical
data which is utilized to calibrate and validate the
model for precise measurement. Here, the voltage
values from three channels are used as inputs of
proposed DNN model. The predicted blood glucose
values are formed through the modeling of three
channels voltage values. Weights of the voltage
values correlate predicted glucose values to the
channels data. The overall accuracy is improved
using 10 hidden layers.
10 Hidden Layers
1
2
3
10
1
2
3
ww
ww
Ʃ
w’
w’
w’
w’
Channel 1
Channel 2
Channel 3
Voltage
Weight of the Voltage Weight of the
Glucose Value
Neural Network for Computation of
Blood Glucose Output Layer
Prediction of Blood Glucose
bias
Input
Output
Sigmoid
Activation
Function
Predicted
Blood
Glucose
(mg/dl)
1
2
3
10
1 10
Fig. 9: The Deep Neural Network (DNN) for pro-
posed work
The Pearson’s correlation coefficient (R) is 0.953.
The error analysis of calibrated machine learning
models is represented in Table III.
TABLE III: Analysis of calibration and validation
of proposed combination and ML model (DNN).
mARD AvgE MAD RMSE
(%) (%) (mg/dl) (mg/dl)
Calibration 6.65 7.30 12.67 21.95
(Validation) 7.32 7.03 09.89 11.56
VI. VAL IDATION OF THE PRO PO SE D IGLU
DEVICE
To validate and test iGLU, 93 healthy, prediabetic
and diabetic samples aged 17-75 are taken follow-
ing medical protocols. A total of 64 males and 29
females are identified during collection of these 93
samples. All samples are taken in fasting, post-
prandial and random modes. The baseline charac-
teristics and error analysis is represented in Table
II and III, respectively. A 10-fold cross validation
has been performed to validate iGLU.
To test the device stability, an experiments have
been performed from multiple measurements of
same sample by couple of times. For this exper-
imental work, a volunteer has been recruited to
measure blood glucose through iGLU and invasive
method with time intervals of 5 minutes.
A value of 10 mg/dl deviation are considered in
observations during 7 iterations of blood glucose
7
measurement. During analysis, 2-4 mg/dl deviation
has been observed (Figure 10(a)). A different vol-
unteer has also been taken for another experimental
analysis to validate the accuracy of iGLU (Figure
10(b)). Measurement has been done with time inter-
val of 60 minutes using 7 iterations. Variations (low
to high) in reference blood glucose values between
8:00 AM-10:00 AM, 10:00 AM-2:00 PM and 2:00
PM-4:00 PM represent the glucose intakes in the
form of food. During analysis, 5-10 mg/dl deviation
represents the stability of iGLU. It was observed
that the effect of fingers or earlobes changes is
negligible. CEG analysis is used to analyze the
accuracy of predicted glucose values from proposed
device. CEG categorizes the devices in terms of
precise measurement and elaborates the zones by
the difference between referenced and predicted
glucose values [27]. The predicted values are in the
zone A and B; then the device will be desirable.
During analysis, all predicted glucose values found
in zone A and B (Figure 11).
8 : 0 0 A M 8 : 0 5 A M 8 : 1 0 A M 8 : 1 5 A M 8 : 2 0 A M 8 : 2 5 A M 8 : 3 0 A M
120
122
124
126
128
130
132
134
136
138
140
B l o o d G l u c o s e C o n c e n t r a t i o n ( m g / d l )
T i m e
R e f e r e n c e d B l o o d G l u c o s e C o n c e n t r a t i o n ( m g / d l )
P r e d i c t e d B l o o d G l u c o s e C o n c e n t r a t i o n ( m g / d l )
W i t h d i f f e r e n t f in g e r c o m b i n a t i o n
F r o m e a r l o b e
(a) Time interval of 5 minutes
8 : 0 0 A M 9 : 0 0 A M 1 0 : 0 0 A M 2 : 0 0 P M 3 : 0 0 P M 4 : 0 0 P M 5 : 0 0 P M
100
110
120
130
140
150
160
170
180
190
200
210 R e f e r e n c e d B l o o d G l u c o s e C o n c e n t r a t i o n ( m g / d l )
P r e d i c t e d B l o o d G l u c o s e C o n c e n t r a t i o n ( m g / d l )
W it h d i f f e r e n t f i n g e r c o m b i n a t i o n
F r o m e a r l o b e
B l o o d G l u c o s e C o n c e n t r a t i o n ( m g / d l )
T i m e
(b) Time interval of 60 minutes
Fig. 10: Predicted and reference blood glucose con-
centration for validation of iGLU on single sample.
Reference Concentration [mg/dl]
0 100 200 300 400
Predicted Concentration [mg/dl]
0
100
200
300
400
A
D
E C
C E
D
B
B
Clarke's Error Grid Analysis
A (90%)
B (10%)
(a) Validation
Reference Concentration [mg/dl]
0 100 200 300 400 500
Predicted Concentration [mg/dl]
0
100
200
300
400
500
A
D
E C
CE
D
B
B
Clarke's Error Grid Analysis
A (94%)
B (06%)
(b) Testing
Fig. 11: CEG analysis of predicted glucose values
VII. CONCLUSIONS AND FUTURE DIRECTIONS
This article introduced a dual short-wave spec-
troscopy NIR technique based non-invasive glucose
monitoring low cost (approximately 20-25 USD)
device iGLU for real-life application. The error
margins for iGLU are improved compared to other
non-invasive approach based systems. After CEG
analysis, 100% samples come in the zone A and B.
During analysis of possible combinations with pro-
posed ML model, iGLU is found more optimized
compared to other measurement device.
In the future research on iGLU, we will involve
more features of IoMT. Glucose-level measurement
from serum is a immediate next goal to further
improve accuracy of iGLU. Integration of stress
measurement along with blood-glucose level is also
in pipeline. A closed feedback from healthcare
providers-end to the end-user side for control of
effects when needed to ensure remote healthcare
when there may be shortage of healthcare providers
can be more effective.
ACK NOWLEDG ME NT
The authors would like to thank Dispensary
MNIT Jaipur (India). We would also thank Dr.
Navneet Agrawal (diabetologist) and his team for
the support at Diabetes, Obesity and Thyroid Centre
Gwalior (India).
REFERENCES
[1] S. Ruan, “Intelligent systems for smart health care: Lever-
aging information for better well-being,” IEEE Consumer
Electronics Magazine, vol. 8, no. 2, pp. 71–71, March
2019.
[2] P. Sundaravadivel, K. Kesavan, L. Kesavan, S. P. Mo-
hanty, and E. Kougianos, “Smart-Log: A Deep-Learning
Based Automated Nutrition Monitoring System in the
IoT,” IEEE Trans. Consum. Electron, vol. 64, no. 3, pp.
390–398, Aug 2018.
[3] S. P. Mohanty and E. Kougianos, “Biosensors: A Tutorial
Review,” IEEE Potentials, vol. 25, no. 2, pp. 35–40,
March 2006.
[4] S. P. Mohanty, U. Choppali, and E. Kougianos, “Every-
thing you wanted to know about smart cities: The Internet
of things is the backbone,” IEEE Consumer Electronics
Magazine, vol. 5, no. 3, pp. 60–70, July 2016.
[5] P. Sundaravadivel, E. Kougianos, S. P. Mohanty, and
M. K. Ganapathiraju, “Everything You Wanted to Know
about Smart Health Care: Evaluating the Different Tech-
nologies and Components of the Internet of Things for
Better Health,” IEEE Consumer Electronics Magazine,
vol. 7, no. 1, pp. 18–28, January 2018.
8
[6] 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.
[7] 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.
[8] A. Sun, A. G. Venkatesh, and D. A. Hall, “A multi-
technique reconfigurable electrochemical biosensor: En-
abling personal health monitoring in mobile devices,”
IEEE Transactions on Biomedical Circuits and Systems,
vol. 10, no. 5, pp. 945–954, Oct 2016.
[9] G. Wang, M. D. Poscente, S. S. Park, C. N. An-
drews, O. Yadid-Pecht, and M. P. Mintchev, “Wearable
microsystem for minimally invasive, pseudo-continuous
blood glucose monitoring: The e-mosquito,” IEEE Trans-
actions on Biomedical Circuits and Systems, vol. 11,
no. 5, pp. 979–987, Oct 2017.
[10] 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.
[11] G. Acciaroli, M. Vettoretti, A. Facchinetti, G. Spara-
cino, and C. Cobelli, “Reduction of blood glucose mea-
surements to calibrate subcutaneous glucose sensors: A
bayesian multiday framework,” IEEE Transactions on
Biomedical Engineering, vol. 65, no. 3, pp. 587–595,
2018.
[12] I. Pagkalos, P. Herrero, C. Toumazou, and P. Georgiou,
“Bio-inspired glucose control in diabetes based on an
analogue implementation of a β-cell model,” IEEE Trans-
actions on Biomedical Circuits and Systems, vol. 8, no. 2,
pp. 186–195, April 2014.
[13] P. P. Pai, A. De, and S. Banerjee, “Accuracy enhancement
for noninvasive glucose estimation using dual-wavelength
photoacoustic measurements and kernel-based calibra-
tion,” IEEE Transactions on Instrumentation and Mea-
surement, vol. 67, no. 1, pp. 126–136, 2018.
[14] M. Yamaguchi, M. Mitsumori, and Y. Kano, “Nonin-
vasively measuring blood glucose using saliva,” IEEE
Engineering in Medicine and Biology Magazine, vol. 17,
no. 3, pp. 59–63, May 1998.
[15] K. Song, U. Ha, S. Park, J. Bae, and H. J. Yoo,
“An impedance and multi-wavelength near-infrared spec-
troscopy ic for non-invasive blood glucose estimation,”
IEEE Journal of Solid-State Circuits, vol. 50, no. 4, pp.
1025–1037, April 2015.
[16] W.-C. Shih, K. L. Bechtel, and M. V. Rebec, “Nonin-
vasive glucose sensing by transcutaneous raman spec-
troscopy,” Journal of biomedical optics, vol. 20, no. 5,
p. 051036, 2015.
[17] C. W. Pirnstill, B. H. Malik, V. C. Gresham, and G. L.
Cot´
e, “In vivo glucose monitoring using dual-wavelength
polarimetry to overcome corneal birefringence in the
presence of motion,” Diabetes technology & therapeutics,
vol. 14, no. 9, pp. 819–827, 2012.
[18] E. Monte-Moreno, “Non-invasive estimate of blood glu-
cose and blood pressure from a photoplethysmograph
by means of machine learning techniques,” Artificial
intelligence in medicine, vol. 53, no. 2, pp. 127–138,
2011.
[19] S. Habbu, M. Dale, and R. Ghongade, “Estimation of
blood glucose by non-invasive method using photo-
plethysmography,” S¯
adhan¯
a, vol. 44, no. 6, p. 135, 2019.
[20] S. Sharma, M. Goodarzi, L. Wynants, H. Ramon, and
W. Saeys, “Efficient use of pure component and interfer-
ent spectra in multivariate calibration,” Analytica chimica
acta, vol. 778, pp. 15–23, 2013.
[21] P. Jain, R. Maddila, and A. M. Joshi, “A precise non-
invasive blood glucose measurement system using NIR
spectroscopy and Hubers regression model,” Optical and
Quantum Electronics, vol. 51, no. 2, p. 51, 2019.
[22] Y. Uwadaira, A. Ikehata, A. Momose, and M. Miura,
“Identification of informative bands in the shortwave-
length nir region for non-invasive blood glucose mea-
surement,” Biomedical Optics Express, vol. 7, no. 7, pp.
2729–2737, 2016.
[23] W. Zhang, R. Liu, W. Zhang, H. Jia, and K. Xu, “Discus-
sion on the validity of nir spectral data in non-invasive
blood glucose sensing,” Biomedical optics express, vol. 4,
no. 6, pp. 789–802, 2013.
[24] S. Haxha and J. Jhoja, “Optical based noninvasive glucose
monitoring sensor prototype,” IEEE Photonics Journal,
vol. 8, no. 6, pp. 1–11, 2016.
[25] 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.
[26] P. Jain and S. Akashe, “Analyzing the impact of boot-
strapped adc with augmented nmos sleep transistors con-
figuration on performance parameters,” Circuits, Systems,
and Signal Processing, vol. 33, no. 7, pp. 2009–2025,
2014.
[27] W. L. Clarke, “The original Clarke error grid analysis
(EGA),” Diabetes technology & therapeutics, vol. 7,
no. 5, pp. 776–779, 2005.
ABO UT T HE AU TH OR S
Prateek Jain is a Research Scholar in ECE
department of MNIT, Jaipur, India. He can be
contacted at: prtk.ieju@gmail.com.
Amit M. Joshi is an Assistant Professor in
Department of ECE, MNIT, Jaipur, India. He can
be contacted at: amjoshi.ece@mnit.ac.in.
Saraju P. Mohanty is the Editor in Chief
of the IEEE Consumer Electronics Magazine and
Professor in the Department of Computer Sci-
ence and Engineering (CSE), University of North
Texas (UNT), Denton, TX, USA. Contact him at
Saraju.Mohanty@unt.edu.
