2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
Blood Flow, Vein and Nerves Detector Using
an NIR Sensor with RLS Estimation for
Embedded Signal Processing
T.Jayaprabha
PG Scholar, Department of EEE
S.A. Engineering College
Chennai, India
prabhasobi@gmail.com
Abstract— In recent years vein and nerve identification is
most important role in medical field. Vein and nerve
finding used for anesthesia injection during the major
surgery. Nowadays doctors use manual method to find
these vein, artery and nerve and they inject the medicine.
Suppose they inject wrongly it will lead to major health
disorder in children’s and adults. So the proposed
automatic detector was used. NIR (Near Infrared)
technology based automatic detector is an advanced one
to detect the blood flow, vein and nerves. This technique
includes to sense, signal conditioning and ADC (Analog to
Digital Converter) to sample electrical signal into digital
values for further processing. Sensor signal was given to
the LPF (Low Pass Filter) section and get the filtered
output. Controller was used to verify the output signal
and display the output in both LCD and LEDs. This
simulated output was verified with both software and
hardware.
Index Terms— NIR sensor, Data Acquisition, RLS
Decomposition, Low pass filter, Piccolo controller, Liquid
Crystal Display, Light Emitting Diode.
I. INTRODUCTION
Effective blood flow detection is clinically very
important for previously find the disease. To detect the
health disorders and health problems about the patient
doctors need the complete information about their
health. Health details are blood pressure and blood
sugar level, heart beat rate, ECG (Electro Cardio Gram)
etc. finding vein and nerve is used for injecting
medicine and taking the blood for lab test. Detection of
blood flow, vein and nerve is very important for
today’s machine world. Because sometimes doctors
whose make it small mistake to find the vein and nerve
wrongly during the major surgery. That will affect the
patient severely. That cause some health issues such as
back pain, nerves problem and sometimes they lost
their life. So the correct medical information will save
the patient’s life and give more secure.
Blood flow measurement was used for early disease
finding for all and this is useful in cardiac field.
Doppler ultra sound used to monitor the blood flow rate
in rat [1]. Here smart phone based ultra sound pulsedwave system was used to measure the blood flow in
real time. This is a portable embedded device to
analysis the blood flow data in both hardware and
software.
ISBN No.978-1-4673-9545-8
A.Suresh
Professor, Department of EEE
S.A. Engineering College
Chennai, India
asuresz@gmail.com
18g low weight sensor was attached to the chicken
without wired connection. This sensor consist two
diodes one is photo diode and another one is laser
diode. Here they monitor the blood flow rate
continuously five days in chicken [2]. This module
mainly uses the wireless sensor networks concepts and
blood flow data are monitored by using personal
computers.
This paper presents the antenna model to
identify and analyze the human nerve. Several
equations are presented to solve the problem but here
pocklington equations are used throughout the module
[3].
Infrared sensors are widely available in
today’s world. Several real time applications are made
by IR (Infrared) sensors only. Sensors are used to
detect the motion form the object to classify their
activities. But these types of low cost sensors are not
suitable for video surveillance applications. The new
PIR (Passive Infrared) sensor was used to perform the
motion identification and classification effectively in
this method. This type of smart sensors is very
important in wild life analyzing field [4].
Bio-metric system is an important method to
identify the person. There are several methods are
available to identify the person such as face, foot print,
finger print, thump recognition and vein, nerve etc.
Here, the hand vein identification is done in the form of
following image processing techniques are preprocessing, enhancement and segmentation, extraction
and image matching [5]. This was taken from 100
different peoples and the overall efficiency is 96.97%.
Wireless vein finder is a best method to find
the human nerve. JPEG camera image was taken from
the vein pattern that absorbs the blood flow in vein.
Wireless Transmitter and Receiver also used in this
vein finding system [6]. Results are verified with PC
and vein finder is used in medical applications.
Channel estimation technique uses MIMOOFDM concept to estimate the correct channel. LSE
(Least Square Error), MMSE (Minimum Mean Square
Error), and EP (Evolutionary Programming) methods
are also used in this paper [7]. Vein identification and
visualization is most important for needle injection or
insertion. This is more complex in children and elder
people. This paper deals with smart phone based vein
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2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
visualization system using wiener Filter. Wiener
estimation was uses the color images that was received
from the RGB (Red Green Blue) camera. For the
further investigation here the Smart phone system was
used [8].
VLSI technology was used to Interface the
peripheral and visceral nerve. This is useful for diabetic
and hypertension patients for further medical treatment
[9]. Low cost and low power consumption was
achieved in this method.
Magnetic coil was placed in rat sciatic nerve
and the electric field was induced in rat nerve that was
used to find the peripheral nerve. Several
computational methods are available to find this type of
sciatic nerve but this magnetic coil method is effective
one for easy computation purpose [10].
Tree partitioning and peripheral vessel
matching was used for separate and classifies the
Artery and veins in human CTs (Computed
Tomography) [11]. Multiple stages of tree partitioning
and peripheral matching algorithms contain preprocessing, geometric graph representation and
separation, sub tree relationships and classification.
Here, 55 CT scans are analysed and verified with 89%
successful separation of both artery and vein.
In the above literature survey, there is no
automatic device for detecting blood flow, vein and
nerves in human body. Hence, in the proposed an
automatic NIR (Near Infrared) sensor based blood
flow, vein and nerve detector was used and this can be
discussed in the next chapter.
II. PROPOSED MODEL
NIR (Near Infrared) technology uses infrared
waves have the wavelengths between 0.75 and
1000μm. region for NIR waves are 0.75 to 3μm and it
is very useful for all sensor applications. Advantages
for this technology are low power requirements, simple
circuitry and portable device. One simple example is
TV remote.
Absorption of NIR light in human body to find
out the hemoglobin level changes in their normal level.
Specific area of the brain was monitored continuously
by using NIR absorption technique. These applications
are used in medical field and all sensing application.
Sensor output can be given to the DAQ (Data
Acquisition) system. Here the sampling process is
carried out to convert the electrical signals into a digital
value for mathematical analysis. Filter section was used
to get the result as filtered signal and reconstructed
output. Controller was used to verify the output in
terms of LCD and LEDs.
Data acquisition gets the input from the NIR
sensor and samples the signal to produce digital values
for further processing. Here RLS (Recursive Least
Square) decomposition unit was used to generate the
result as 3D images. This simulated output was
analyzed in power spectrum using MATLAB software.
Fig.1. NIR Based Vein and Nerves Detector
Once the simulation is over then verifies the
result using hardware setup. This section includes low
pass filter, real time controller and LCD, LEDs. Here
the attenuated signal was processed by using the C2000
controller. Controllers decide which signal is sent from
the sensor and analyze the signal changes. Finally this
result was displayed in both LCD and LEDs.
IV. SYSTEM FLOW
III. BLOCK DIAGRAM
Automatic detector based vein and nerves
detection is a most important technique for medical
usage. Transmitter and receiver of NIR sensor that
senses the signal from the human body this is shown in
fig.1.
Fig.2. Flow Chart
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2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
Automatic detection of blood flow, vein
and nerves process is explained below. This method
have the system flow is depicted in fig.2. First human
bodies selected and use the NIR sensor to sense the
signal is received from the forearm. Next sampling
process is carried out then ADC output is given to the
filter section. Here RLS filter used for simulation
purpose and LPF used for hardware verification
purpose. Filtered output was send to the controller
which uses the principle of decision making process to
decide each signal received from infrared sensor.
If the blood flow, vein and nerve are detected
then display the result in LCD display and
corresponding LED will also glow. Otherwise will not
display the result and stop the detecting process.
V. RESULTS AND DISCUSSION
Simulation results are compared with existing
methods like LMS (Least Mean Square), Wavelet
transform. New RLS estimation was used to get an
exact signal with low SNR and high throughput values.
This simulated output was verified with hardware and
this is explained in the next chapter.
A. SIMULATION RESULTS
Filtered output for blood flow and the
environment is depicted in fig. 3. Take the input signals
in terms of sound wave from human body. This input is
given to the RLS decomposition unit.
Fig.5. Filtered Output for Nerve
Exact signal is received in the form 3D images
and this type of output was analyzed using power
spectrum. Reference vs. filtered output for vein and
nerve is shown in fig.4 and 5. NIR sensor was sensing
the signal and sends this signal to the DAQ board. Here
the sampling process was done. Accurate signal and
images are achieved by using MATLAB simulation.
Different methods are compared with a new proposed
RLS filter to get low SNR, maximum throughput and
separation of modes. Various ranges of values for each
method are given in table 1. Results shows from table1
proposed method is a better one compared to existing
methods. Another benefit in this project was to find out
the signal ranges are negative compared to other
methods such as wavelet and RMS method.
TABLE I: EXISTING VS. RLS FILTER
Types of method
Wavelet
transform
SNR
in db
through
put max
Scale&
Normaliz
e value
20.48
0.45
0.6
0.0039
0.5
0.0038
0.8
14.25
0.564
0.7
-20
0.6421
0.9
Environment
Vein
Nerve
RMS
Fig.3. Filtered Output for Environment
RLS Filter
(proposed)
Environment
Vein
Nerve
Environment
Vein
Nerve
B. Proteus software simulation
Fig.4. Filtered Output for Vein
Proteus is a software technology that allows
creating hardware executable decision support
guidelines with little effort. Hardware simulation result
will be shown in Fig.6. Sensor places a major role in
vein and nerve detection. This type of Proteus software
simulation will support further hardware setup
verification. To change the sensor values that will
produce the output as vein or nerve in LCD display.
Sensor values from 50 to 120 we get the output as vein
that show in fig.7. And the sensor values from 120 to
200 we get the output as nerve that will show in fig.8.
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2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
Fig.9. Working Module
Fig.6. Hardware Simulation for Vein and Nerve
Detection
Fig.10. LCD Output for Vein detection
Fig.7. Output for Vein detection
Fig.8. Output for Nerve detection
C. Hardware setup
Vein and nerve detection hardware setup is
shown in fig.9. Bi-cell & Quadrant photodiode uses 910
nm wave lengths to sense the signal from the human
body. Sensor senses the vein or nerve that will send the
corresponding signal to the controller. Controller
decides which signal was sensed by the NIR sensor and
display the output as vein or nerve in LCD. Here, the
Blood flow, vein and nerve detection is done by using an
automatic detector this type embedded devices are very
effective and useful in medical field.
Hardware setup for this NIR sensor based
blood flow, vein and nerve detection uses NIR sensor,
Piccolo Controller, LCD, LED and Power supply unit.
TMS C2000 controller support the IDE (Integrated
Development Environment) tool is Energia. It is an open
source and easy to us this code. Place the sensor in
human forearm that will sense the signal and this is
given to the controller unit. Controller was trained to
check the output as vein or nerve and send this result to
LCD unit. Piccolo controller C2000 USB connection
was connected with PC. This interface was used to
check the output in user terminal. Energia tool was used
to show the result in monitor. Upload the sensor values
and train the controller to get the result.
Fig.11. LCD Output for Nerve detection
Power supply unit is used produce the
corresponding voltage for this hardware module and
the Amplification unit also used in this setup. LCD
output for vein, nerve and warning messages are shown
in fig.10, fig.11. 2x16 LCD was used to display the
corresponding output. Piccolo controller C2000 USB
connection was connected with PC. This interface was
used to check the output in user terminal this is shown
in fig.12.
Fig.12. PC Interface for Controller Output
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2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
D. Features
x Real-time JTAG isolation and JTAG emulator
circuitry.
x USB connection and 20 PCB pins.
x Serial TX/RX LEDs are available.
x Programmable push button: GPIO 12 is
possible.
x Memory: 128-256 KB flash, 52-100 KB RAM
and Boot ROM.
E. Applications
x
x
x
x
x
Precision control and sensing applications.
Drives and motor control.
Power line communication integration.
Line monitoring and protection.
Process and valve control.
VI.CONCLUSION
Detection of blood flow, vein and nerves are
most important thing in medical field. Blood flow
detection used for diabetes and hypertension patients
having pre-micro vascular changes in blood circulation.
NIR sensor was used to measure the signal and this
output can be given to the data acquisition system. RLS
decomposition unit was used to reconstruct an original
image without mixing of modes. Another benefit in this
proposed method we get an output with low SNR, max
throughput. From the simulation results, it was clear
that the proposed method was better than the existing
RMS, MSE and wavelet transform methods. The
proposed method was analyzed using both software and
hardware.
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