1.3 CONTEST CATEGORY:
Student
2. BENEFITS OF USING VIRTUAL INSTRUMENTATION:
A. Instrument can be custom made with desired features for specific application
B. Errors can be easily identified and rectified, while for real instruments repairing and
calibration needs to be done regularly.
C. Easy to use, can be put to use by students, engineers, as well as scientists.
D. Reduced cost of running in the long run.
E. Reduced application development time.
3. NI HARDWARE AND SOFTWARE USED IN THIS APPLICATION:
The choice made for the data acquisition and processing software was NI LabVIEW 8.5.1
and the NI hardware used was NI-DAQ 6024E.
4. PROBLEM TO BE SOLVED:
To automate a reverse osmosis plant so as to increase its efficiency and maintain some
output parameters like product water flux, salt-rejection ratio, product recovery rate at
desired levels.
5. SOLUTION TO THE PROBLEM:
Using the NI-DAQ 6024E hardware and LabVIEW 8.5.1 the plant was automated to
meet the specific requirements on output water quality.
6.INTRODUCTION TO APPLICATION:
The reverse osmosis plant has basic units called membrane modules shown above and
also the real module in Fig.4. Water is pumped into the module using a high pressure
pump and desalinated water is collected at the output.
The reverse osmosis plant for which the system is developed was being operated
manually. For instance, suppose the product water flux decreased, then a supervisor
would increase the pressure till the output flowmeter gave the desired reading.This
method of control was found to have a degrading effect on the plant output and efficiency
over time. This was because the reverse osmosis process is a very complex chemical
process with multiple input parameters like the feedwater flowrate, feed pressure, feed
temperature, feedwater salinity, feedwater pH which are constantly fluctuating
throughout the day and year, which in turn gives rise to controlled variable fluctuations
like unsteady product water flux, and product salinity.
Such a situation was found ideal to implement automatic control in order to increase the
efficiency and maintain throughput of the plant as well as keep track of healthiness of the
plant in terms of membrane fouling due to salt deposition and compation due to constant
subjection to high pressures.
7.APPLICATION DESCRIPTION:
The choice made for the data acquisition and processing software was NI LabVIEW 8.5.1
and the hardware used was NI-DAQ 6024E. A model referenced control system was the
choice to be implemented. To make a control system a model of the plant was to be
developed (Fig.1). Modeling the plant to develop a model referenced control system was
a Multiple Input Multiple Output (MIMO) function approximation task i.e we had to find
that function f in the equation ,
Output parameter = f (input parameters)
Mathematical modeling was tried out using the chemical equations developed in
chemistry literature for reverse osmosis process which proved to be inaccurate due to the
complexity and various disturbances present in the chemical process of reverse osmosis
which the equations neglected.
A VI was then developed to model the plant using the radial basis function neural
networks which are best suited for function approximation problems. The training data
was acquired by sampling the numerous input parameters such as feed water temperature,
feed pressure, feed salinity, feed flowrate, feedwater pH by varying them over
permissible ranges and also the corresponding output parameters like product water flux,
product salinity.
The neural model reference control architecture uses two neural networks: a controller
network and a plant model network, as shown in the following figure. The plant model is
identified first, and then the controller is trained so that the plant output follows the
reference model output.
The model error shown in figure was used to train the plant model network. Having
trained the plant model neural network the plant model was ready. After this the
controller was trained using the dynamic backpropagation algorithm using the control
error. The dynamic backpropagation algorithm backpropagates the control error through
the plant model without readjusting any weights. This backpropagated error is then used
to train the controller network i.e to adjust its weights and biases.
The plant healthiness was tracked using the model error. This was because the plant
model developed at the beginning of operation modeled the plant without any membrane
fouling or compaction. Over time as fouling and compaction degrades membrane
performance the model error automatically will rise thus giving us a measure of the
deterioration in plant health.
Fig.1.Neural model reference control architecture
The training algorithm for both the plant model and controller was programmed using
LabVIEW 8.5.1 using the LabVIEW Mathscript structure.
8. SYSTEM SETUP OF THE APPLCATION:
Fig. 2. The Single Stage RO Plant To Be Controlled.
Fig. 3. Computer Interfacing Through NI-DAQ 6024E
Fig. 4. The RO module used in the single stage RO plant.
Fig. 5. The computer control system.
Fig. 6. Various sensors on input/output flow lines.
The system setup includes the neural control system implemented in NI LabVIEW 8.5.1
interfaced to the plant through the NI-DAQ 6024E. Then there is a power supply unit for
the sensors. The sensors perform the sensing on the input and output flowlines of the
module and collect the input and output parameter data. Those parameters are :
A. Input parameters : Feedwater flowrate, Feedwater pH, Feedwater pressure, Feedwater
temperature, Feedwater salinity.
B. Output parameters: Product flowrate, Product salinity.
The manipulated variable is the pressure generated by the high pressure pump.This
control action i.e pressure is set by the neural controller through the NI-DAQ 6024E
analog channels.
9.SOFTWARE IMPLEMENTATION IN THE APPLICATION:
Fig. 7.Front panel of the SubVI which models the plant
Fig. 8. Block Diagram Of The SubVI which models the plant.
Fig. 9. Block Diagram Of SubVI which generates a neural network.
Fig. 10.User Defined M-files Used By Mathscript Node.
The algorithm for generating the neural network and training them was done using the
LabVIEW Mathscript structure in LabVIEW 8.5.1.
The VI developed iteratively creates a radial basis network one neuron at a time. Neurons
are added to the network until the sum-squared error falls beneath an error goal or a
maximum number of neurons has been reached. At each iteration the input parameter
vector that results in lowering the network error the most, is used to create a radial basis
neuron. The error of the new network is checked, and if low enough the VI is finished.
Otherwise the next neuron is added. This procedure is repeated until the error goal is met,
or the maximum number of neurons is reached. This VI whose panels are shown above
was used to generate both the plant model network as well as the controller network.
MS excel worksheets were used for datalogging purpose. Data was read from the
worksheet for processing and then the control action was sent out using the data
acquisition functions.
10.NATIONAL INSTRUMENTS PRODUCTS PART OF APPLICATION:
NI-DAQ 6024 played a major role in data acquisition and control while the LabVIEW
8.5.1 version helped implement the neural control algorithm with ease.
11.SPECIFIC CAPABILITIES OF NI PRODUCTS USED:
The LabVIEW Mathscript structure available in LabVIEW 8.5.1 was crucial in easy
implementation of the algorithm. The friendly user interface to control the plant was a
bonus. Also the datalogging facility helped in keeping history information about the plant
performance.
12. TIME, MONEY SAVINGS:
The number of staff working at the station was cut down to about 50% of original as a
result the salary cost was reduce to half. Also the regular inspections had to be done
during which the plant had to be shut down. However now the inspection is automatic,
example if the RO membrane has scaled beyond a certain limit due to salt deposition on
it, then backwashing of the membranes is automatically done whenever detected. Also
savings are made as the RO module life time has been found to have increased.
13.ACKNOWLEGEMENT:
The described project is an overview of the bigger 18 months project worth 18 lakhs
sponsored by Central Salt & Marine Chemicals Research Institute (CSMCRI),
Bhavnagar.
We would like to thank Dr.Chandrashekhar, Director CEERI and Dr.L.K.Maheshwari,
Director BITS-pilani for providing us with an opportunity to work together on this
project.
14.CONCLUSION
An appropriate automation for a reverse osmosis plant is developed using radial basis
function neural networks developed in LabVIEW 8.5.1 unlike the traditional approach
of using mathematical modeling of plants for control.
Friday, April 3, 2009
Prediction of epilepsy using electronic diagnosing system,Bluetooth technology,LABVIEW
Introduction:
Epilepsy is a very fatal condition which is caused as a result of imbalance in the nervous
system. The very common symptoms of epilepsy includes sudden fluctuations in heart beat rate
and involuntary muscular movements (seizures). The aura (practical symptom) of epilepsy
includes fluctuations
in heartbeat, nausea, dizziness etc.
The wireless electronic diagnosing system designed here is exclusively meant for epilepsy
patients. The system helps them in accurately predicting the occurrence of seizures. Sudden
occurrence of seizures during driving may lead to accidents and its occurrence during sleeping
hours can even
lead to the patient’s death, if no immediate, proper attention is provided by a bystander or a
doctor. With the aid of this system, the patient can lead a normal life. Since the occurrence of
seizures is unpredictable, it will be a very risky task to leave the patient alone.
The electronic system presented here is a wearable device which predicts the occurrence of
epilepsy in a few minutes advance. The device utilizes the signals from human body to detect the
occurrence of epilepsy. As soon as the device detects the symptoms, it transmits a coded signal.
The signal
is decoded by a wireless receiver to produce control signals for switching an alarm device,
mobile messaging device and an automatic vehicle control system appropriately. In future, GPS
could be incorporated to trace out the exact location of the patient.
Current technologies for acquiring signals from the patient’s body are very much developed.
Many sensors are available which can detect the heart beat and muscular movements noninvasively
and accurately.
Such non invasive technique for measuring heart beat is pulse oximetry. Using this technique,
heart beat can be accurately monitored. Muscular convulsions are collected using micro
electromechanical sensors (MEMS) firmly attached to the body. The sensors used are small in
size and can be firmly attached to the body. The accelerations resulting from epileptic
convulsions are sensed using MEMS accelerometer which is very accurate, precise and small in
size. To provide wireless communication channel low cost network using DATEx protocol is
utilized. DATEx is a standard protocol developed by National Instruments.
Heart beats are to be monitored continuously. Any sudden variation in heart beat which is caused
by the onset of epileptic seizures is detected and confirmed with the MEMS signal. When the
seizure is confirmed, message is transmitted to the surroundings for initiating necessary
protective measures for the patient.
The device is designed as a wireless, wearable and personal equipment. The device can sense the
aura of pre ictal stage in a few minutes advance and takes the necessary safety measures
automatically. Hence a technician’s assistance is not required for the patient. Therefore this
device will be extremely useful for patients (especially youngsters) who wish to be active in their
life. The user gets absolute freedom from wires and can be used when moving.
To practically implement the epilepsy prediction system, the following aspects should be
implemented.
1. Sensing biometric signals: Two types of biological signals are required for processing. They
are heart beat and muscular convulsions. The heart beat be measured using PCI DIO 32 HS and
mascular movements can be measured by an accelerometer.
2. Processing it and taking decisions: Processing of the signals is done by software programmed
into a microcontroller. The software is designed in such a way that it detects the exact symptom
of epilepsy.
3. Communication: Communication is set up using a transmitter and receiver module with
DATEx protocol.
4. Controlling: Automatic vehicle control system, mobile messaging device and an alarm device
PXI-6608 is integrated to the receiver for protecting the patient.
Constraints
1. Smaller size and weight requirement
2. Low power consumption requirement as the device is battery operated.
3. Suitable long life battery
4. Accurate technique or algorithm for foolproof detection of seizure
5. Secure communication between the wearable equipment and the receiving unit
6. Signal processing requirement
7. Cost effectiveness
The application of this system focus on epilepsy patients who wish to move freely without the
assistance of others in their life. The system is a wearable device which can detect the aura of
seizure in an epilepsy victim very much precisely in time by processing the signals available
from the patient’s body at the predictal state. The system uses a processing device to process the
signals from the human body and activates a wireless transmitter which transmits a coded signal.
The receiver decodes the signal using another processing unit which results in the production of
control signals for activating various safety devices mounted on the vehicle or on the dormitory
where the patient commonly resides. For e.g, if the seizure occurs while the victim driving a
vehicle or while sleeping the device automatically generates
control signals for the control of vehicle, setting off an alarm circuit and messaging the doctor
about the patient’s condition via short messaging Service (SMS). The system can be expanded
easily in such a way to include Global Positioning System (GPS) for tracing out the exact
position of the victim of epilepsy in the future. Thus the device saves the patient from accident or
even death and acts as a “LIFE SAVER”.
Design
The design consists of hardware and software sections. The device hardware mainly consist of
three parts namely, (i) Heart beat sensor, (ii) Seizure detector, (iii) Processor and (iv) Wireless
transceiver
(i) Heart beat sensor: The heart beat of the patient is to be monitored. For this purpose, a PCI
DIO 32 HS is used. PCI DIO 32 HS measures heart beat by sensing the difference in absorbance
of infrared radiation by blood during systolic and diastolic activities of heart. The volume of
blood flowing through arteries varies widely during each heart beat. Hence if infrared radiation is
incident on it, the absorbance of IR also varies according to the heart beat. These variations are
sensed using a photo detector to determine the heart
beat.
The PCI DIO 32 HS designed here works using reflective principle. The IR source emits IR
radiation which is reflected in accordance with the flow of blood. The reflected rays are detected
using a photo detector.
A sensor is placed on a thin part of the patient's anatomy, usually a fingertip or earlobe, and light
of infrared wavelength is made incident on the body. Changing absorbance of the infrared is
measured, allowing determination of the absorbance’s due to the pulsing arterial blood alone,
excluding venous blood, skin, bone, muscle, fat, and (in most cases) fingernail polish. The circuit
of PCI DIO 32 HS consists of a trans-resistance amplifier, voltage follower, difference amplifier,
and filter. All these stages are cascaded together to from the complete circuit of PCI DIO 32 HS.
The circuit works in 5 V supply. In order to get perfect amplification sans noise, ultra low offset
operational amplifier OP07 and FET input operational amplifier LF 356N is selected. A trans
resistance amplifier is used in the first stage to convert the photodiode current to voltage. The
major design of this sensor is its output voltage and the output frequency. The output frequency
is band limited to 15 Hz using filters. Low pass first order butterworth filter is used. Low pass
filter is designed at 15 Hz upper cut off frequency with a gain of 1.5. A high pass first order
butter worth filter with lower cut off frequency of 0.5 Hz is cascaded with the low pass to
remove the dc voltage. An amplifier is set at the output of the PCI DIO 32 HS in order to raise
the output signal level to +5V (approx). Amplifier with amplification factor of 50 is designed.
Typical output of the sensor is shown on the graph below. Normal heart beat is 72 beats per
minute. That is the frequency of the signal is 1.2 Hz for a healthy person. The output amplitude
varies from 70mV to 120mV.
(ii) Seizure detector: Seizures are involuntary muscular movements which occur during
epilepsy. Muscular movements are sensed using MEMS (micro electro mechanical sensor)
accelerometer. A 3D accelerometer is used to sense the muscular movements. The SCXI-1530 is
a low cost, low power, complete 3-axis accelerometer with signal conditioned voltage outputs,
which is all on a single monolithic IC. The SCXI-1530 is a complete acceleration measurement
system on a single monolithic IC. The SCXI-1530 has a measurement range of ±3 g. The sensor
is a polysilicon surface-micro
machined structure built on top of a silicon wafer. Polysilicon springs suspend the structure over
the surface of the wafer and provide a resistance against acceleration forces. Deflection of the
structure is measured using a differential capacitor that consists of independent fixed plates and
plates
attached to the moving mass. The fixed plates are driven by 180° out-of-phase square waves.
Acceleration deflects the beam and unbalances the differential capacitor, resulting in an output
square wave whose amplitude is proportional to acceleration. Phase-sensitive demodulation
techniques are then used to rectify the signal and determine the direction of the acceleration.
The demodulator’s output is amplified and brought off-chip through a 32 k resistor. The signal
bandwidth of the device is set by adding a capacitor. This filtering improves measurement
resolution and helps prevent aliasing.
Performance of the project was affected due to the non availability of 3 axis accelerometer.
Hence here I have used a single axis accelerometer MMA1260EG from FREESCALE
semiconductor for detecting the muscular convulsions. It has a sensitivity of 1.5g. The output is
filtered using a n RC low pass filter with values R=1 K ad C=0.10.1μf. The output of the
sensor during typical seizure is shown on the graph. The output of MEMS is given to a 10 bit
analog to digital converter for digitizing the output.
(iii)Processor: The signals from sensors are processed using PIC18F4620 microcontroller. The
microcontroller requires a 10 bit ADC and a comparator circuit for processing the signals from
the sensor. PIC 18F 4620 includes built in ADC and comparator. The processor is clocked at
4MHz. The frequency of normal heart beat rate is 1.2 HZ. approximately. Or the time period of
the heart beat signal is 0.83 secs. The algorithm detects the sudden decrease in pulse width which
is one of the aura of epilepsy. As soon as the variations in the heart beat are detected, the
algorithm checks for the typical seizure waveform from the mems sensor. When these two
signals coincide, the software takes the decision as an epileptic seizure and generates control
signals.
(iv) Wireless transceiver: The device uses DATEx protocol for communication. The DATEx
Wireless Networking Protocol is a simple protocol designed for low data rate, short distance, and
low-cost networks. Fundamentally based on wireless personal area networks (WPANs), the
DATEx protocol provides an easy-to-use alternative for wireless communication. In particular, it
targets smaller applications that have relatively small network sizes, with few hops between
nodes, using Microchip’s MRF24J40 2.4 GHz transceiver for compliant networks. The DATEx
protocol is based on the MAC and PHY layers specification, and is tailored for simple network
development in the 2.4 GHz band. The protocol provides the features to find form and join a
network, as well as discovering nodes on the network and route to them. The card uses PCB
trace antenna. The device uses line of sight communication. The range is approx. 200ft. The
wireless transmitter and receiver hardware consist of a motherboard with PIC 18F4620
microcontroller. The motherboard consists of a daughter card with microchip MRF24J40 2.4
Ghz transceiver. The board is designed to work at 9V to 3.3 V DC. The DATEx protocol stack
can be installed on the board and the required application can be programmed into it. A peer to
peer network is formed using the transceivers.
One node is programmed as the network coordinator and the other as an end device. The
coordinator is set as the transmitter and the en device as the receiver. The long address is
assigned for the network and the short address to the nodes.
The device is designed in such a way that it searches for a network as soon as the module is
switched on. The coordinator assigns the address to the end devices and forms the network if
one is not detected.
The MRF24J40 is an compliant transceiver supporting DATEx, ZigBee™ and other proprietary
protocols. The MRF24J40 integrates wireless RF, PHY layer baseband and MAC layer
architectures that can be combined with a simple microprocessor to apply low data rate to a
multitude of applications The MRF24J40 device integrates a receiver, transmitter, VCO and PLL
into a single integrated circuit. It uses advanced radio architecture to minimize external part
count and power consumption. It mainly consists of TX/RX FIFOs, a CSMA-CA controller,
super frame Constructor, receive frame filter, security engine and digital signal processing
module. The MRF24J40 is fabricated by advanced 0.18 μm CMOS process and is offered in a
40-pin QFN 6x6 mm2 package.
The MRF24J40 consists of four major functional blocks:
1. An SPI interface that serves as a communication channel between the host controller and the
MRF24J40.
2. Control registers which are used to control and monitor the MRF24J40.
3. The MAC (Medium Access Control) module that implements compliant MAC logic.
4. The PHY (Physical Layer) driver that encodes and decodes the analog data. The device also
contains other support blocks, such as the on-chip voltage regulator, security module and system
control logic.
4. Design of software
The processing unit utilizes the logic implemented in the software for accurate detection of
seizures. The software checks the input signal from the PCI DIO 32 HS from the patient’s body
continuously and measures the pulse width of the signal. This width is converted into heart beat
rate by the
software. If there is any abnormalities in heart beat, it can be detected as a change in the pulse
width .As soon as the logic detects a change it triggers the vibrator and the system waits for the
response. The patient has to press a button on his wearable unit. If the patient is unable to do so
due to
occurrence of seizure, then response signal from MEM sensor which senses the muscular
convulsions is captured and analyzed. If there are signals of muscular convulsions the software
concludes that the patient has seizure and warning message is transmitted using the wireless
transmitter. The
seizure detection algorithm from the MEMS signals is to check only the sudden abnormality
occurring in the human body. This algorithm helps to avoid situations where heart beat rises due
to excessive physical work or due to tension etc. The algorithm uses the averaging technique to
determine
abnormalities accurately.
P=(P+N)/2
where p=previous heart beat rate
N=next heart beat rate.
For a person suffering from epilepsy, in the pre ictal stage the heart beat varies abruptly and
hence the value of P also changes. This change in the value of P is detected and the program is
made to wait for the signal from the second sensor which senses the muscular convulsions. If
muscular convulsions are detected from the second sensor, it triggers the transmitter on which
transmits a coded signal which is received by the receiver. The software section contains the
following major functional modules:
1. Heart beat rate calculations
2. Seizure detection from MEMS signal
3. Communication control
4. Overall supervision
5. Implementation
The system requires a heart beat sensor, muscular convulsion sensor, a transmitter, receiver,
mobile messaging device, alarm device and automatic vehicle control system. All the above said
parts are integrated together to a processor to form the device. The epilepsy prediction system
can be practically implemented by incorporating the following components:
a) Heart beat sensor: A pulse oxy meter is used as a heart beat sensor. The implementation of
PCI DIO 32 HS is by cascading several stages as shown in the figure 4. A high pass filter is
designed with lower cut off frequency of 15 Hz. .the high pass filter is cascaded with a low pass
filter designed to an upper cut off frequency of 0.5 Hz. The amplifier at the final stage raises the
voltage from mV level to the required voltage range. An amplification factor of 50 is given to it.
b) Convulsions sensor: An accelerometer is used as a convulsion sensor. Muscular convulsions
are detected using single axis mems IC MMA1260EG. The sensitivity of the sensor is set to
1.55g. The circuit is implemented as shown in the circuit diagram. The output of the sensor is
filtered out sing a low pass RC filter externally. The value of R is selected as 1K and C as
0.1μf.
c) Processing unit: The processing unit contains PIC 18F4620 microcontroller which is clocked
at 40 MHz.. PIC18F4620 have 64 Kbytes of Flash memory. The microcontroller has inbuilt 10
bit ADC which is used to digitize the output from MEMS module. It also includes a comparator
which is used to process the heart beat waveforms from the pulse oxy meter. The incoming
signal is processed using logics implemented in the software which runs the device. The
processing unit continuously checks for symptoms in the incoming signal. As soon as it detects
any abnormality, it triggers a warning
vibrator and the wireless transmitter.
d) Wireless Transmitter and receiver: Wireless transceiver consist of a board consisting of
MRF24J40 IC The transmitter transmits a coded signal which is decoded by a receiver to
generate control signals. The control signal activates an alarm device, mobile messaging device
and automatic vehicle control system appropriately. Apart from the above important blocks, a
buzzer circuit and a DC to DC convertor blocks are also implemented.
e) Enclosure design: The device is a wearable one (on the wrist). Hence the
enclosure is designed suiting to that purpose. The enclosure can be designed in the form of a
watch.
6. Software tools used:
1. MPLAB Integrated development Environment
2. Microchip C18 compiler
3. Keil Integrated development environment
7. Testing
(i) Testing of PCI DIO 32 HS: The PCI DIO 32 HS was tested by wounding the probe of the
device on the index finger of a person and the output were viewed on a DSO. The output is
shown in the graph given below. The PCI DIO 32 HS successfully detected the heart beat
waveform from the patient’s index finger. The out put frequency was 1.2 Hz . And the voltage
level was in the range of 100 to 120 mV.
(ii) Testing of MEMS sensor: The MEMS sensor is connected to the body of the patient using
straps. Typical epileptic seizure waveform is shown in the
figure below. This stage is not yet fully tested and testing is under way.
(iii)Testing of software: The inputs from the sensors were provided to the PIC controller in
which the software was programmed. Wave forms describing different conditions of the patient
were given as input and tested .
(iv)Testing of communication module The transceiver is directly connected to the
microcontroller in which the software was programmed. As soon as the software detected the
epileptic symptom, the transmitter was triggered. Using Zena network analyzer, the network was
detected at a frequency of 2.4G Hz. A peer to peer single node network was formed which
transmitted the message to the receiver node. The system designed here processes the heart beat
continuously and abnormalities are detected accurately. The device transmits the signal only
when seizures of epilepsy are detected. The performance of the device is not restricted by
movement of the patient. By using this device the patient can move freely without worries.
8. Problems encountered
We have encountered many problems as noted below:
1. Non availability of 3 axis accelerometer: We could not procure the 3 axis accelerometer and
hence testing is only performed with a single axis accelerometer. However, the system gives
better results only if a 3 axis accelerometer is used in for detecting muscle contractions.
2. Noise and temperature effect on the sensor outputs: Major problems were encountered due to
noise picked up by the sensors. Use of shielded cable and grounding solved the problems to a
satisfactory level. Heating effect of active components like op amps also created problems like
drifting and thermal noise. This was solved by operating the op amps at a lower voltage.
3. Problem with suitable wearable enclosure: A suitable wearable enclosure is not designed.
Compact PCB must be designed to fit all the components inside a wearable enclosure.
9. Advantages and benefits
The benefit of the project is that a lightweight, rugged, lowcost, wearable (on the wrist) device is
developed which helps a victim of epilepsy to do all sorts of activities like others do.
The device will be extremely cost effective since it uses simple sensors and technology for the
detection.
The sensors are small in size and can be firmly attached to the body.
Batteries can last long as the device consumes only little energy.
The device doesn’t restrict the movement of the patient.
The system is easily expandable paving the way to incorporate much more sophisticated
devices like ECG detector in the future.
Stand alone application.
10. Improvements
The system is easily expandable to incorporate GPS system and to capture and transmit various
patient parameters like ECG , body temperature etc.
11. Conclusion
A light weight, rugged, cost-effective wearable device is developed which helps millions of
victims of epilepsy around the globe. With the device in possession an epilepsy victim can move
around freely like normal people sans worries.
Epilepsy is a very fatal condition which is caused as a result of imbalance in the nervous
system. The very common symptoms of epilepsy includes sudden fluctuations in heart beat rate
and involuntary muscular movements (seizures). The aura (practical symptom) of epilepsy
includes fluctuations
in heartbeat, nausea, dizziness etc.
The wireless electronic diagnosing system designed here is exclusively meant for epilepsy
patients. The system helps them in accurately predicting the occurrence of seizures. Sudden
occurrence of seizures during driving may lead to accidents and its occurrence during sleeping
hours can even
lead to the patient’s death, if no immediate, proper attention is provided by a bystander or a
doctor. With the aid of this system, the patient can lead a normal life. Since the occurrence of
seizures is unpredictable, it will be a very risky task to leave the patient alone.
The electronic system presented here is a wearable device which predicts the occurrence of
epilepsy in a few minutes advance. The device utilizes the signals from human body to detect the
occurrence of epilepsy. As soon as the device detects the symptoms, it transmits a coded signal.
The signal
is decoded by a wireless receiver to produce control signals for switching an alarm device,
mobile messaging device and an automatic vehicle control system appropriately. In future, GPS
could be incorporated to trace out the exact location of the patient.
Current technologies for acquiring signals from the patient’s body are very much developed.
Many sensors are available which can detect the heart beat and muscular movements noninvasively
and accurately.
Such non invasive technique for measuring heart beat is pulse oximetry. Using this technique,
heart beat can be accurately monitored. Muscular convulsions are collected using micro
electromechanical sensors (MEMS) firmly attached to the body. The sensors used are small in
size and can be firmly attached to the body. The accelerations resulting from epileptic
convulsions are sensed using MEMS accelerometer which is very accurate, precise and small in
size. To provide wireless communication channel low cost network using DATEx protocol is
utilized. DATEx is a standard protocol developed by National Instruments.
Heart beats are to be monitored continuously. Any sudden variation in heart beat which is caused
by the onset of epileptic seizures is detected and confirmed with the MEMS signal. When the
seizure is confirmed, message is transmitted to the surroundings for initiating necessary
protective measures for the patient.
The device is designed as a wireless, wearable and personal equipment. The device can sense the
aura of pre ictal stage in a few minutes advance and takes the necessary safety measures
automatically. Hence a technician’s assistance is not required for the patient. Therefore this
device will be extremely useful for patients (especially youngsters) who wish to be active in their
life. The user gets absolute freedom from wires and can be used when moving.
To practically implement the epilepsy prediction system, the following aspects should be
implemented.
1. Sensing biometric signals: Two types of biological signals are required for processing. They
are heart beat and muscular convulsions. The heart beat be measured using PCI DIO 32 HS and
mascular movements can be measured by an accelerometer.
2. Processing it and taking decisions: Processing of the signals is done by software programmed
into a microcontroller. The software is designed in such a way that it detects the exact symptom
of epilepsy.
3. Communication: Communication is set up using a transmitter and receiver module with
DATEx protocol.
4. Controlling: Automatic vehicle control system, mobile messaging device and an alarm device
PXI-6608 is integrated to the receiver for protecting the patient.
Constraints
1. Smaller size and weight requirement
2. Low power consumption requirement as the device is battery operated.
3. Suitable long life battery
4. Accurate technique or algorithm for foolproof detection of seizure
5. Secure communication between the wearable equipment and the receiving unit
6. Signal processing requirement
7. Cost effectiveness
The application of this system focus on epilepsy patients who wish to move freely without the
assistance of others in their life. The system is a wearable device which can detect the aura of
seizure in an epilepsy victim very much precisely in time by processing the signals available
from the patient’s body at the predictal state. The system uses a processing device to process the
signals from the human body and activates a wireless transmitter which transmits a coded signal.
The receiver decodes the signal using another processing unit which results in the production of
control signals for activating various safety devices mounted on the vehicle or on the dormitory
where the patient commonly resides. For e.g, if the seizure occurs while the victim driving a
vehicle or while sleeping the device automatically generates
control signals for the control of vehicle, setting off an alarm circuit and messaging the doctor
about the patient’s condition via short messaging Service (SMS). The system can be expanded
easily in such a way to include Global Positioning System (GPS) for tracing out the exact
position of the victim of epilepsy in the future. Thus the device saves the patient from accident or
even death and acts as a “LIFE SAVER”.
Design
The design consists of hardware and software sections. The device hardware mainly consist of
three parts namely, (i) Heart beat sensor, (ii) Seizure detector, (iii) Processor and (iv) Wireless
transceiver
(i) Heart beat sensor: The heart beat of the patient is to be monitored. For this purpose, a PCI
DIO 32 HS is used. PCI DIO 32 HS measures heart beat by sensing the difference in absorbance
of infrared radiation by blood during systolic and diastolic activities of heart. The volume of
blood flowing through arteries varies widely during each heart beat. Hence if infrared radiation is
incident on it, the absorbance of IR also varies according to the heart beat. These variations are
sensed using a photo detector to determine the heart
beat.
The PCI DIO 32 HS designed here works using reflective principle. The IR source emits IR
radiation which is reflected in accordance with the flow of blood. The reflected rays are detected
using a photo detector.
A sensor is placed on a thin part of the patient's anatomy, usually a fingertip or earlobe, and light
of infrared wavelength is made incident on the body. Changing absorbance of the infrared is
measured, allowing determination of the absorbance’s due to the pulsing arterial blood alone,
excluding venous blood, skin, bone, muscle, fat, and (in most cases) fingernail polish. The circuit
of PCI DIO 32 HS consists of a trans-resistance amplifier, voltage follower, difference amplifier,
and filter. All these stages are cascaded together to from the complete circuit of PCI DIO 32 HS.
The circuit works in 5 V supply. In order to get perfect amplification sans noise, ultra low offset
operational amplifier OP07 and FET input operational amplifier LF 356N is selected. A trans
resistance amplifier is used in the first stage to convert the photodiode current to voltage. The
major design of this sensor is its output voltage and the output frequency. The output frequency
is band limited to 15 Hz using filters. Low pass first order butterworth filter is used. Low pass
filter is designed at 15 Hz upper cut off frequency with a gain of 1.5. A high pass first order
butter worth filter with lower cut off frequency of 0.5 Hz is cascaded with the low pass to
remove the dc voltage. An amplifier is set at the output of the PCI DIO 32 HS in order to raise
the output signal level to +5V (approx). Amplifier with amplification factor of 50 is designed.
Typical output of the sensor is shown on the graph below. Normal heart beat is 72 beats per
minute. That is the frequency of the signal is 1.2 Hz for a healthy person. The output amplitude
varies from 70mV to 120mV.
(ii) Seizure detector: Seizures are involuntary muscular movements which occur during
epilepsy. Muscular movements are sensed using MEMS (micro electro mechanical sensor)
accelerometer. A 3D accelerometer is used to sense the muscular movements. The SCXI-1530 is
a low cost, low power, complete 3-axis accelerometer with signal conditioned voltage outputs,
which is all on a single monolithic IC. The SCXI-1530 is a complete acceleration measurement
system on a single monolithic IC. The SCXI-1530 has a measurement range of ±3 g. The sensor
is a polysilicon surface-micro
machined structure built on top of a silicon wafer. Polysilicon springs suspend the structure over
the surface of the wafer and provide a resistance against acceleration forces. Deflection of the
structure is measured using a differential capacitor that consists of independent fixed plates and
plates
attached to the moving mass. The fixed plates are driven by 180° out-of-phase square waves.
Acceleration deflects the beam and unbalances the differential capacitor, resulting in an output
square wave whose amplitude is proportional to acceleration. Phase-sensitive demodulation
techniques are then used to rectify the signal and determine the direction of the acceleration.
The demodulator’s output is amplified and brought off-chip through a 32 k resistor. The signal
bandwidth of the device is set by adding a capacitor. This filtering improves measurement
resolution and helps prevent aliasing.
Performance of the project was affected due to the non availability of 3 axis accelerometer.
Hence here I have used a single axis accelerometer MMA1260EG from FREESCALE
semiconductor for detecting the muscular convulsions. It has a sensitivity of 1.5g. The output is
filtered using a n RC low pass filter with values R=1 K ad C=0.10.1μf. The output of the
sensor during typical seizure is shown on the graph. The output of MEMS is given to a 10 bit
analog to digital converter for digitizing the output.
(iii)Processor: The signals from sensors are processed using PIC18F4620 microcontroller. The
microcontroller requires a 10 bit ADC and a comparator circuit for processing the signals from
the sensor. PIC 18F 4620 includes built in ADC and comparator. The processor is clocked at
4MHz. The frequency of normal heart beat rate is 1.2 HZ. approximately. Or the time period of
the heart beat signal is 0.83 secs. The algorithm detects the sudden decrease in pulse width which
is one of the aura of epilepsy. As soon as the variations in the heart beat are detected, the
algorithm checks for the typical seizure waveform from the mems sensor. When these two
signals coincide, the software takes the decision as an epileptic seizure and generates control
signals.
(iv) Wireless transceiver: The device uses DATEx protocol for communication. The DATEx
Wireless Networking Protocol is a simple protocol designed for low data rate, short distance, and
low-cost networks. Fundamentally based on wireless personal area networks (WPANs), the
DATEx protocol provides an easy-to-use alternative for wireless communication. In particular, it
targets smaller applications that have relatively small network sizes, with few hops between
nodes, using Microchip’s MRF24J40 2.4 GHz transceiver for compliant networks. The DATEx
protocol is based on the MAC and PHY layers specification, and is tailored for simple network
development in the 2.4 GHz band. The protocol provides the features to find form and join a
network, as well as discovering nodes on the network and route to them. The card uses PCB
trace antenna. The device uses line of sight communication. The range is approx. 200ft. The
wireless transmitter and receiver hardware consist of a motherboard with PIC 18F4620
microcontroller. The motherboard consists of a daughter card with microchip MRF24J40 2.4
Ghz transceiver. The board is designed to work at 9V to 3.3 V DC. The DATEx protocol stack
can be installed on the board and the required application can be programmed into it. A peer to
peer network is formed using the transceivers.
One node is programmed as the network coordinator and the other as an end device. The
coordinator is set as the transmitter and the en device as the receiver. The long address is
assigned for the network and the short address to the nodes.
The device is designed in such a way that it searches for a network as soon as the module is
switched on. The coordinator assigns the address to the end devices and forms the network if
one is not detected.
The MRF24J40 is an compliant transceiver supporting DATEx, ZigBee™ and other proprietary
protocols. The MRF24J40 integrates wireless RF, PHY layer baseband and MAC layer
architectures that can be combined with a simple microprocessor to apply low data rate to a
multitude of applications The MRF24J40 device integrates a receiver, transmitter, VCO and PLL
into a single integrated circuit. It uses advanced radio architecture to minimize external part
count and power consumption. It mainly consists of TX/RX FIFOs, a CSMA-CA controller,
super frame Constructor, receive frame filter, security engine and digital signal processing
module. The MRF24J40 is fabricated by advanced 0.18 μm CMOS process and is offered in a
40-pin QFN 6x6 mm2 package.
The MRF24J40 consists of four major functional blocks:
1. An SPI interface that serves as a communication channel between the host controller and the
MRF24J40.
2. Control registers which are used to control and monitor the MRF24J40.
3. The MAC (Medium Access Control) module that implements compliant MAC logic.
4. The PHY (Physical Layer) driver that encodes and decodes the analog data. The device also
contains other support blocks, such as the on-chip voltage regulator, security module and system
control logic.
4. Design of software
The processing unit utilizes the logic implemented in the software for accurate detection of
seizures. The software checks the input signal from the PCI DIO 32 HS from the patient’s body
continuously and measures the pulse width of the signal. This width is converted into heart beat
rate by the
software. If there is any abnormalities in heart beat, it can be detected as a change in the pulse
width .As soon as the logic detects a change it triggers the vibrator and the system waits for the
response. The patient has to press a button on his wearable unit. If the patient is unable to do so
due to
occurrence of seizure, then response signal from MEM sensor which senses the muscular
convulsions is captured and analyzed. If there are signals of muscular convulsions the software
concludes that the patient has seizure and warning message is transmitted using the wireless
transmitter. The
seizure detection algorithm from the MEMS signals is to check only the sudden abnormality
occurring in the human body. This algorithm helps to avoid situations where heart beat rises due
to excessive physical work or due to tension etc. The algorithm uses the averaging technique to
determine
abnormalities accurately.
P=(P+N)/2
where p=previous heart beat rate
N=next heart beat rate.
For a person suffering from epilepsy, in the pre ictal stage the heart beat varies abruptly and
hence the value of P also changes. This change in the value of P is detected and the program is
made to wait for the signal from the second sensor which senses the muscular convulsions. If
muscular convulsions are detected from the second sensor, it triggers the transmitter on which
transmits a coded signal which is received by the receiver. The software section contains the
following major functional modules:
1. Heart beat rate calculations
2. Seizure detection from MEMS signal
3. Communication control
4. Overall supervision
5. Implementation
The system requires a heart beat sensor, muscular convulsion sensor, a transmitter, receiver,
mobile messaging device, alarm device and automatic vehicle control system. All the above said
parts are integrated together to a processor to form the device. The epilepsy prediction system
can be practically implemented by incorporating the following components:
a) Heart beat sensor: A pulse oxy meter is used as a heart beat sensor. The implementation of
PCI DIO 32 HS is by cascading several stages as shown in the figure 4. A high pass filter is
designed with lower cut off frequency of 15 Hz. .the high pass filter is cascaded with a low pass
filter designed to an upper cut off frequency of 0.5 Hz. The amplifier at the final stage raises the
voltage from mV level to the required voltage range. An amplification factor of 50 is given to it.
b) Convulsions sensor: An accelerometer is used as a convulsion sensor. Muscular convulsions
are detected using single axis mems IC MMA1260EG. The sensitivity of the sensor is set to
1.55g. The circuit is implemented as shown in the circuit diagram. The output of the sensor is
filtered out sing a low pass RC filter externally. The value of R is selected as 1K and C as
0.1μf.
c) Processing unit: The processing unit contains PIC 18F4620 microcontroller which is clocked
at 40 MHz.. PIC18F4620 have 64 Kbytes of Flash memory. The microcontroller has inbuilt 10
bit ADC which is used to digitize the output from MEMS module. It also includes a comparator
which is used to process the heart beat waveforms from the pulse oxy meter. The incoming
signal is processed using logics implemented in the software which runs the device. The
processing unit continuously checks for symptoms in the incoming signal. As soon as it detects
any abnormality, it triggers a warning
vibrator and the wireless transmitter.
d) Wireless Transmitter and receiver: Wireless transceiver consist of a board consisting of
MRF24J40 IC The transmitter transmits a coded signal which is decoded by a receiver to
generate control signals. The control signal activates an alarm device, mobile messaging device
and automatic vehicle control system appropriately. Apart from the above important blocks, a
buzzer circuit and a DC to DC convertor blocks are also implemented.
e) Enclosure design: The device is a wearable one (on the wrist). Hence the
enclosure is designed suiting to that purpose. The enclosure can be designed in the form of a
watch.
6. Software tools used:
1. MPLAB Integrated development Environment
2. Microchip C18 compiler
3. Keil Integrated development environment
7. Testing
(i) Testing of PCI DIO 32 HS: The PCI DIO 32 HS was tested by wounding the probe of the
device on the index finger of a person and the output were viewed on a DSO. The output is
shown in the graph given below. The PCI DIO 32 HS successfully detected the heart beat
waveform from the patient’s index finger. The out put frequency was 1.2 Hz . And the voltage
level was in the range of 100 to 120 mV.
(ii) Testing of MEMS sensor: The MEMS sensor is connected to the body of the patient using
straps. Typical epileptic seizure waveform is shown in the
figure below. This stage is not yet fully tested and testing is under way.
(iii)Testing of software: The inputs from the sensors were provided to the PIC controller in
which the software was programmed. Wave forms describing different conditions of the patient
were given as input and tested .
(iv)Testing of communication module The transceiver is directly connected to the
microcontroller in which the software was programmed. As soon as the software detected the
epileptic symptom, the transmitter was triggered. Using Zena network analyzer, the network was
detected at a frequency of 2.4G Hz. A peer to peer single node network was formed which
transmitted the message to the receiver node. The system designed here processes the heart beat
continuously and abnormalities are detected accurately. The device transmits the signal only
when seizures of epilepsy are detected. The performance of the device is not restricted by
movement of the patient. By using this device the patient can move freely without worries.
8. Problems encountered
We have encountered many problems as noted below:
1. Non availability of 3 axis accelerometer: We could not procure the 3 axis accelerometer and
hence testing is only performed with a single axis accelerometer. However, the system gives
better results only if a 3 axis accelerometer is used in for detecting muscle contractions.
2. Noise and temperature effect on the sensor outputs: Major problems were encountered due to
noise picked up by the sensors. Use of shielded cable and grounding solved the problems to a
satisfactory level. Heating effect of active components like op amps also created problems like
drifting and thermal noise. This was solved by operating the op amps at a lower voltage.
3. Problem with suitable wearable enclosure: A suitable wearable enclosure is not designed.
Compact PCB must be designed to fit all the components inside a wearable enclosure.
9. Advantages and benefits
The benefit of the project is that a lightweight, rugged, lowcost, wearable (on the wrist) device is
developed which helps a victim of epilepsy to do all sorts of activities like others do.
The device will be extremely cost effective since it uses simple sensors and technology for the
detection.
The sensors are small in size and can be firmly attached to the body.
Batteries can last long as the device consumes only little energy.
The device doesn’t restrict the movement of the patient.
The system is easily expandable paving the way to incorporate much more sophisticated
devices like ECG detector in the future.
Stand alone application.
10. Improvements
The system is easily expandable to incorporate GPS system and to capture and transmit various
patient parameters like ECG , body temperature etc.
11. Conclusion
A light weight, rugged, cost-effective wearable device is developed which helps millions of
victims of epilepsy around the globe. With the device in possession an epilepsy victim can move
around freely like normal people sans worries.
Prediction of epilepsy using electronic diagnosing system
Introduction:
Epilepsy is a very fatal condition which is caused as a result of imbalance in the nervous
system. The very common symptoms of epilepsy includes sudden fluctuations in heart beat rate
and involuntary muscular movements (seizures). The aura (practical symptom) of epilepsy
includes fluctuations
in heartbeat, nausea, dizziness etc.
The wireless electronic diagnosing system designed here is exclusively meant for epilepsy
patients. The system helps them in accurately predicting the occurrence of seizures. Sudden
occurrence of seizures during driving may lead to accidents and its occurrence during sleeping
hours can even
lead to the patient’s death, if no immediate, proper attention is provided by a bystander or a
doctor. With the aid of this system, the patient can lead a normal life. Since the occurrence of
seizures is unpredictable, it will be a very risky task to leave the patient alone.
The electronic system presented here is a wearable device which predicts the occurrence of
epilepsy in a few minutes advance. The device utilizes the signals from human body to detect the
occurrence of epilepsy. As soon as the device detects the symptoms, it transmits a coded signal.
The signal
is decoded by a wireless receiver to produce control signals for switching an alarm device,
mobile messaging device and an automatic vehicle control system appropriately. In future, GPS
could be incorporated to trace out the exact location of the patient.
Current technologies for acquiring signals from the patient’s body are very much developed.
Many sensors are available which can detect the heart beat and muscular movements noninvasively
and accurately.
Such non invasive technique for measuring heart beat is pulse oximetry. Using this technique,
heart beat can be accurately monitored. Muscular convulsions are collected using micro
electromechanical sensors (MEMS) firmly attached to the body. The sensors used are small in
size and can be firmly attached to the body. The accelerations resulting from epileptic
convulsions are sensed using MEMS accelerometer which is very accurate, precise and small in
size. To provide wireless communication channel low cost network using DATEx protocol is
utilized. DATEx is a standard protocol developed by National Instruments.
Heart beats are to be monitored continuously. Any sudden variation in heart beat which is caused
by the onset of epileptic seizures is detected and confirmed with the MEMS signal. When the
seizure is confirmed, message is transmitted to the surroundings for initiating necessary
protective measures for the patient.
The device is designed as a wireless, wearable and personal equipment. The device can sense the
aura of pre ictal stage in a few minutes advance and takes the necessary safety measures
automatically. Hence a technician’s assistance is not required for the patient. Therefore this
device will be extremely useful for patients (especially youngsters) who wish to be active in their
life. The user gets absolute freedom from wires and can be used when moving.
To practically implement the epilepsy prediction system, the following aspects should be
implemented.
1. Sensing biometric signals: Two types of biological signals are required for processing. They
are heart beat and muscular convulsions. The heart beat be measured using PCI DIO 32 HS and
mascular movements can be measured by an accelerometer.
2. Processing it and taking decisions: Processing of the signals is done by software programmed
into a microcontroller. The software is designed in such a way that it detects the exact symptom
of epilepsy.
3. Communication: Communication is set up using a transmitter and receiver module with
DATEx protocol.
4. Controlling: Automatic vehicle control system, mobile messaging device and an alarm device
PXI-6608 is integrated to the receiver for protecting the patient.
Constraints
1. Smaller size and weight requirement
2. Low power consumption requirement as the device is battery operated.
3. Suitable long life battery
4. Accurate technique or algorithm for foolproof detection of seizure
5. Secure communication between the wearable equipment and the receiving unit
6. Signal processing requirement
7. Cost effectiveness
The application of this system focus on epilepsy patients who wish to move freely without the
assistance of others in their life. The system is a wearable device which can detect the aura of
seizure in an epilepsy victim very much precisely in time by processing the signals available
from the patient’s body at the predictal state. The system uses a processing device to process the
signals from the human body and activates a wireless transmitter which transmits a coded signal.
The receiver decodes the signal using another processing unit which results in the production of
control signals for activating various safety devices mounted on the vehicle or on the dormitory
where the patient commonly resides. For e.g, if the seizure occurs while the victim driving a
vehicle or while sleeping the device automatically generates
control signals for the control of vehicle, setting off an alarm circuit and messaging the doctor
about the patient’s condition via short messaging Service (SMS). The system can be expanded
easily in such a way to include Global Positioning System (GPS) for tracing out the exact
position of the victim of epilepsy in the future. Thus the device saves the patient from accident or
even death and acts as a “LIFE SAVER”.
Design
The design consists of hardware and software sections. The device hardware mainly consist of
three parts namely, (i) Heart beat sensor, (ii) Seizure detector, (iii) Processor and (iv) Wireless
transceiver
(i) Heart beat sensor: The heart beat of the patient is to be monitored. For this purpose, a PCI
DIO 32 HS is used. PCI DIO 32 HS measures heart beat by sensing the difference in absorbance
of infrared radiation by blood during systolic and diastolic activities of heart. The volume of
blood flowing through arteries varies widely during each heart beat. Hence if infrared radiation is
incident on it, the absorbance of IR also varies according to the heart beat. These variations are
sensed using a photo detector to determine the heart
beat.
The PCI DIO 32 HS designed here works using reflective principle. The IR source emits IR
radiation which is reflected in accordance with the flow of blood. The reflected rays are detected
using a photo detector.
A sensor is placed on a thin part of the patient's anatomy, usually a fingertip or earlobe, and light
of infrared wavelength is made incident on the body. Changing absorbance of the infrared is
measured, allowing determination of the absorbance’s due to the pulsing arterial blood alone,
excluding venous blood, skin, bone, muscle, fat, and (in most cases) fingernail polish. The circuit
of PCI DIO 32 HS consists of a trans-resistance amplifier, voltage follower, difference amplifier,
and filter. All these stages are cascaded together to from the complete circuit of PCI DIO 32 HS.
The circuit works in 5 V supply. In order to get perfect amplification sans noise, ultra low offset
operational amplifier OP07 and FET input operational amplifier LF 356N is selected. A trans
resistance amplifier is used in the first stage to convert the photodiode current to voltage. The
major design of this sensor is its output voltage and the output frequency. The output frequency
is band limited to 15 Hz using filters. Low pass first order butterworth filter is used. Low pass
filter is designed at 15 Hz upper cut off frequency with a gain of 1.5. A high pass first order
butter worth filter with lower cut off frequency of 0.5 Hz is cascaded with the low pass to
remove the dc voltage. An amplifier is set at the output of the PCI DIO 32 HS in order to raise
the output signal level to +5V (approx). Amplifier with amplification factor of 50 is designed.
Typical output of the sensor is shown on the graph below. Normal heart beat is 72 beats per
minute. That is the frequency of the signal is 1.2 Hz for a healthy person. The output amplitude
varies from 70mV to 120mV.
(ii) Seizure detector: Seizures are involuntary muscular movements which occur during
epilepsy. Muscular movements are sensed using MEMS (micro electro mechanical sensor)
accelerometer. A 3D accelerometer is used to sense the muscular movements. The SCXI-1530 is
a low cost, low power, complete 3-axis accelerometer with signal conditioned voltage outputs,
which is all on a single monolithic IC. The SCXI-1530 is a complete acceleration measurement
system on a single monolithic IC. The SCXI-1530 has a measurement range of ±3 g. The sensor
is a polysilicon surface-micro
machined structure built on top of a silicon wafer. Polysilicon springs suspend the structure over
the surface of the wafer and provide a resistance against acceleration forces. Deflection of the
structure is measured using a differential capacitor that consists of independent fixed plates and
plates
attached to the moving mass. The fixed plates are driven by 180° out-of-phase square waves.
Acceleration deflects the beam and unbalances the differential capacitor, resulting in an output
square wave whose amplitude is proportional to acceleration. Phase-sensitive demodulation
techniques are then used to rectify the signal and determine the direction of the acceleration.
The demodulator’s output is amplified and brought off-chip through a 32 k resistor. The signal
bandwidth of the device is set by adding a capacitor. This filtering improves measurement
resolution and helps prevent aliasing.
Performance of the project was affected due to the non availability of 3 axis accelerometer.
Hence here I have used a single axis accelerometer MMA1260EG from FREESCALE
semiconductor for detecting the muscular convulsions. It has a sensitivity of 1.5g. The output is
filtered using a n RC low pass filter with values R=1 K ad C=0.10.1μf. The output of the
sensor during typical seizure is shown on the graph. The output of MEMS is given to a 10 bit
analog to digital converter for digitizing the output.
(iii)Processor: The signals from sensors are processed using PIC18F4620 microcontroller. The
microcontroller requires a 10 bit ADC and a comparator circuit for processing the signals from
the sensor. PIC 18F 4620 includes built in ADC and comparator. The processor is clocked at
4MHz. The frequency of normal heart beat rate is 1.2 HZ. approximately. Or the time period of
the heart beat signal is 0.83 secs. The algorithm detects the sudden decrease in pulse width which
is one of the aura of epilepsy. As soon as the variations in the heart beat are detected, the
algorithm checks for the typical seizure waveform from the mems sensor. When these two
signals coincide, the software takes the decision as an epileptic seizure and generates control
signals.
(iv) Wireless transceiver: The device uses DATEx protocol for communication. The DATEx
Wireless Networking Protocol is a simple protocol designed for low data rate, short distance, and
low-cost networks. Fundamentally based on wireless personal area networks (WPANs), the
DATEx protocol provides an easy-to-use alternative for wireless communication. In particular, it
targets smaller applications that have relatively small network sizes, with few hops between
nodes, using Microchip’s MRF24J40 2.4 GHz transceiver for compliant networks. The DATEx
protocol is based on the MAC and PHY layers specification, and is tailored for simple network
development in the 2.4 GHz band. The protocol provides the features to find form and join a
network, as well as discovering nodes on the network and route to them. The card uses PCB
trace antenna. The device uses line of sight communication. The range is approx. 200ft. The
wireless transmitter and receiver hardware consist of a motherboard with PIC 18F4620
microcontroller. The motherboard consists of a daughter card with microchip MRF24J40 2.4
Ghz transceiver. The board is designed to work at 9V to 3.3 V DC. The DATEx protocol stack
can be installed on the board and the required application can be programmed into it. A peer to
peer network is formed using the transceivers.
One node is programmed as the network coordinator and the other as an end device. The
coordinator is set as the transmitter and the en device as the receiver. The long address is
assigned for the network and the short address to the nodes.
The device is designed in such a way that it searches for a network as soon as the module is
switched on. The coordinator assigns the address to the end devices and forms the network if
one is not detected.
The MRF24J40 is an compliant transceiver supporting DATEx, ZigBee™ and other proprietary
protocols. The MRF24J40 integrates wireless RF, PHY layer baseband and MAC layer
architectures that can be combined with a simple microprocessor to apply low data rate to a
multitude of applications The MRF24J40 device integrates a receiver, transmitter, VCO and PLL
into a single integrated circuit. It uses advanced radio architecture to minimize external part
count and power consumption. It mainly consists of TX/RX FIFOs, a CSMA-CA controller,
super frame Constructor, receive frame filter, security engine and digital signal processing
module. The MRF24J40 is fabricated by advanced 0.18 μm CMOS process and is offered in a
40-pin QFN 6x6 mm2 package.
The MRF24J40 consists of four major functional blocks:
1. An SPI interface that serves as a communication channel between the host controller and the
MRF24J40.
2. Control registers which are used to control and monitor the MRF24J40.
3. The MAC (Medium Access Control) module that implements compliant MAC logic.
4. The PHY (Physical Layer) driver that encodes and decodes the analog data. The device also
contains other support blocks, such as the on-chip voltage regulator, security module and system
control logic.
4. Design of software
The processing unit utilizes the logic implemented in the software for accurate detection of
seizures. The software checks the input signal from the PCI DIO 32 HS from the patient’s body
continuously and measures the pulse width of the signal. This width is converted into heart beat
rate by the
software. If there is any abnormalities in heart beat, it can be detected as a change in the pulse
width .As soon as the logic detects a change it triggers the vibrator and the system waits for the
response. The patient has to press a button on his wearable unit. If the patient is unable to do so
due to
occurrence of seizure, then response signal from MEM sensor which senses the muscular
convulsions is captured and analyzed. If there are signals of muscular convulsions the software
concludes that the patient has seizure and warning message is transmitted using the wireless
transmitter. The
seizure detection algorithm from the MEMS signals is to check only the sudden abnormality
occurring in the human body. This algorithm helps to avoid situations where heart beat rises due
to excessive physical work or due to tension etc. The algorithm uses the averaging technique to
determine
abnormalities accurately.
P=(P+N)/2
where p=previous heart beat rate
N=next heart beat rate.
For a person suffering from epilepsy, in the pre ictal stage the heart beat varies abruptly and
hence the value of P also changes. This change in the value of P is detected and the program is
made to wait for the signal from the second sensor which senses the muscular convulsions. If
muscular convulsions are detected from the second sensor, it triggers the transmitter on which
transmits a coded signal which is received by the receiver. The software section contains the
following major functional modules:
1. Heart beat rate calculations
2. Seizure detection from MEMS signal
3. Communication control
4. Overall supervision
5. Implementation
The system requires a heart beat sensor, muscular convulsion sensor, a transmitter, receiver,
mobile messaging device, alarm device and automatic vehicle control system. All the above said
parts are integrated together to a processor to form the device. The epilepsy prediction system
can be practically implemented by incorporating the following components:
a) Heart beat sensor: A pulse oxy meter is used as a heart beat sensor. The implementation of
PCI DIO 32 HS is by cascading several stages as shown in the figure 4. A high pass filter is
designed with lower cut off frequency of 15 Hz. .the high pass filter is cascaded with a low pass
filter designed to an upper cut off frequency of 0.5 Hz. The amplifier at the final stage raises the
voltage from mV level to the required voltage range. An amplification factor of 50 is given to it.
b) Convulsions sensor: An accelerometer is used as a convulsion sensor. Muscular convulsions
are detected using single axis mems IC MMA1260EG. The sensitivity of the sensor is set to
1.55g. The circuit is implemented as shown in the circuit diagram. The output of the sensor is
filtered out sing a low pass RC filter externally. The value of R is selected as 1K and C as
0.1μf.
c) Processing unit: The processing unit contains PIC 18F4620 microcontroller which is clocked
at 40 MHz.. PIC18F4620 have 64 Kbytes of Flash memory. The microcontroller has inbuilt 10
bit ADC which is used to digitize the output from MEMS module. It also includes a comparator
which is used to process the heart beat waveforms from the pulse oxy meter. The incoming
signal is processed using logics implemented in the software which runs the device. The
processing unit continuously checks for symptoms in the incoming signal. As soon as it detects
any abnormality, it triggers a warning
vibrator and the wireless transmitter.
d) Wireless Transmitter and receiver: Wireless transceiver consist of a board consisting of
MRF24J40 IC The transmitter transmits a coded signal which is decoded by a receiver to
generate control signals. The control signal activates an alarm device, mobile messaging device
and automatic vehicle control system appropriately. Apart from the above important blocks, a
buzzer circuit and a DC to DC convertor blocks are also implemented.
e) Enclosure design: The device is a wearable one (on the wrist). Hence the
enclosure is designed suiting to that purpose. The enclosure can be designed in the form of a
watch.
6. Software tools used:
1. MPLAB Integrated development Environment
2. Microchip C18 compiler
3. Keil Integrated development environment
7. Testing
(i) Testing of PCI DIO 32 HS: The PCI DIO 32 HS was tested by wounding the probe of the
device on the index finger of a person and the output were viewed on a DSO. The output is
shown in the graph given below. The PCI DIO 32 HS successfully detected the heart beat
waveform from the patient’s index finger. The out put frequency was 1.2 Hz . And the voltage
level was in the range of 100 to 120 mV.
(ii) Testing of MEMS sensor: The MEMS sensor is connected to the body of the patient using
straps. Typical epileptic seizure waveform is shown in the
figure below. This stage is not yet fully tested and testing is under way.
(iii)Testing of software: The inputs from the sensors were provided to the PIC controller in
which the software was programmed. Wave forms describing different conditions of the patient
were given as input and tested .
(iv)Testing of communication module The transceiver is directly connected to the
microcontroller in which the software was programmed. As soon as the software detected the
epileptic symptom, the transmitter was triggered. Using Zena network analyzer, the network was
detected at a frequency of 2.4G Hz. A peer to peer single node network was formed which
transmitted the message to the receiver node. The system designed here processes the heart beat
continuously and abnormalities are detected accurately. The device transmits the signal only
when seizures of epilepsy are detected. The performance of the device is not restricted by
movement of the patient. By using this device the patient can move freely without worries.
8. Problems encountered
We have encountered many problems as noted below:
1. Non availability of 3 axis accelerometer: We could not procure the 3 axis accelerometer and
hence testing is only performed with a single axis accelerometer. However, the system gives
better results only if a 3 axis accelerometer is used in for detecting muscle contractions.
2. Noise and temperature effect on the sensor outputs: Major problems were encountered due to
noise picked up by the sensors. Use of shielded cable and grounding solved the problems to a
satisfactory level. Heating effect of active components like op amps also created problems like
drifting and thermal noise. This was solved by operating the op amps at a lower voltage.
3. Problem with suitable wearable enclosure: A suitable wearable enclosure is not designed.
Compact PCB must be designed to fit all the components inside a wearable enclosure.
9. Advantages and benefits
The benefit of the project is that a lightweight, rugged, lowcost, wearable (on the wrist) device is
developed which helps a victim of epilepsy to do all sorts of activities like others do.
The device will be extremely cost effective since it uses simple sensors and technology for the
detection.
The sensors are small in size and can be firmly attached to the body.
Batteries can last long as the device consumes only little energy.
The device doesn’t restrict the movement of the patient.
The system is easily expandable paving the way to incorporate much more sophisticated
devices like ECG detector in the future.
Stand alone application.
10. Improvements
The system is easily expandable to incorporate GPS system and to capture and transmit various
patient parameters like ECG , body temperature etc.
11. Conclusion
A light weight, rugged, cost-effective wearable device is developed which helps millions of
victims of epilepsy around the globe. With the device in possession an epilepsy victim can move
around freely like normal people sans worries.
Epilepsy is a very fatal condition which is caused as a result of imbalance in the nervous
system. The very common symptoms of epilepsy includes sudden fluctuations in heart beat rate
and involuntary muscular movements (seizures). The aura (practical symptom) of epilepsy
includes fluctuations
in heartbeat, nausea, dizziness etc.
The wireless electronic diagnosing system designed here is exclusively meant for epilepsy
patients. The system helps them in accurately predicting the occurrence of seizures. Sudden
occurrence of seizures during driving may lead to accidents and its occurrence during sleeping
hours can even
lead to the patient’s death, if no immediate, proper attention is provided by a bystander or a
doctor. With the aid of this system, the patient can lead a normal life. Since the occurrence of
seizures is unpredictable, it will be a very risky task to leave the patient alone.
The electronic system presented here is a wearable device which predicts the occurrence of
epilepsy in a few minutes advance. The device utilizes the signals from human body to detect the
occurrence of epilepsy. As soon as the device detects the symptoms, it transmits a coded signal.
The signal
is decoded by a wireless receiver to produce control signals for switching an alarm device,
mobile messaging device and an automatic vehicle control system appropriately. In future, GPS
could be incorporated to trace out the exact location of the patient.
Current technologies for acquiring signals from the patient’s body are very much developed.
Many sensors are available which can detect the heart beat and muscular movements noninvasively
and accurately.
Such non invasive technique for measuring heart beat is pulse oximetry. Using this technique,
heart beat can be accurately monitored. Muscular convulsions are collected using micro
electromechanical sensors (MEMS) firmly attached to the body. The sensors used are small in
size and can be firmly attached to the body. The accelerations resulting from epileptic
convulsions are sensed using MEMS accelerometer which is very accurate, precise and small in
size. To provide wireless communication channel low cost network using DATEx protocol is
utilized. DATEx is a standard protocol developed by National Instruments.
Heart beats are to be monitored continuously. Any sudden variation in heart beat which is caused
by the onset of epileptic seizures is detected and confirmed with the MEMS signal. When the
seizure is confirmed, message is transmitted to the surroundings for initiating necessary
protective measures for the patient.
The device is designed as a wireless, wearable and personal equipment. The device can sense the
aura of pre ictal stage in a few minutes advance and takes the necessary safety measures
automatically. Hence a technician’s assistance is not required for the patient. Therefore this
device will be extremely useful for patients (especially youngsters) who wish to be active in their
life. The user gets absolute freedom from wires and can be used when moving.
To practically implement the epilepsy prediction system, the following aspects should be
implemented.
1. Sensing biometric signals: Two types of biological signals are required for processing. They
are heart beat and muscular convulsions. The heart beat be measured using PCI DIO 32 HS and
mascular movements can be measured by an accelerometer.
2. Processing it and taking decisions: Processing of the signals is done by software programmed
into a microcontroller. The software is designed in such a way that it detects the exact symptom
of epilepsy.
3. Communication: Communication is set up using a transmitter and receiver module with
DATEx protocol.
4. Controlling: Automatic vehicle control system, mobile messaging device and an alarm device
PXI-6608 is integrated to the receiver for protecting the patient.
Constraints
1. Smaller size and weight requirement
2. Low power consumption requirement as the device is battery operated.
3. Suitable long life battery
4. Accurate technique or algorithm for foolproof detection of seizure
5. Secure communication between the wearable equipment and the receiving unit
6. Signal processing requirement
7. Cost effectiveness
The application of this system focus on epilepsy patients who wish to move freely without the
assistance of others in their life. The system is a wearable device which can detect the aura of
seizure in an epilepsy victim very much precisely in time by processing the signals available
from the patient’s body at the predictal state. The system uses a processing device to process the
signals from the human body and activates a wireless transmitter which transmits a coded signal.
The receiver decodes the signal using another processing unit which results in the production of
control signals for activating various safety devices mounted on the vehicle or on the dormitory
where the patient commonly resides. For e.g, if the seizure occurs while the victim driving a
vehicle or while sleeping the device automatically generates
control signals for the control of vehicle, setting off an alarm circuit and messaging the doctor
about the patient’s condition via short messaging Service (SMS). The system can be expanded
easily in such a way to include Global Positioning System (GPS) for tracing out the exact
position of the victim of epilepsy in the future. Thus the device saves the patient from accident or
even death and acts as a “LIFE SAVER”.
Design
The design consists of hardware and software sections. The device hardware mainly consist of
three parts namely, (i) Heart beat sensor, (ii) Seizure detector, (iii) Processor and (iv) Wireless
transceiver
(i) Heart beat sensor: The heart beat of the patient is to be monitored. For this purpose, a PCI
DIO 32 HS is used. PCI DIO 32 HS measures heart beat by sensing the difference in absorbance
of infrared radiation by blood during systolic and diastolic activities of heart. The volume of
blood flowing through arteries varies widely during each heart beat. Hence if infrared radiation is
incident on it, the absorbance of IR also varies according to the heart beat. These variations are
sensed using a photo detector to determine the heart
beat.
The PCI DIO 32 HS designed here works using reflective principle. The IR source emits IR
radiation which is reflected in accordance with the flow of blood. The reflected rays are detected
using a photo detector.
A sensor is placed on a thin part of the patient's anatomy, usually a fingertip or earlobe, and light
of infrared wavelength is made incident on the body. Changing absorbance of the infrared is
measured, allowing determination of the absorbance’s due to the pulsing arterial blood alone,
excluding venous blood, skin, bone, muscle, fat, and (in most cases) fingernail polish. The circuit
of PCI DIO 32 HS consists of a trans-resistance amplifier, voltage follower, difference amplifier,
and filter. All these stages are cascaded together to from the complete circuit of PCI DIO 32 HS.
The circuit works in 5 V supply. In order to get perfect amplification sans noise, ultra low offset
operational amplifier OP07 and FET input operational amplifier LF 356N is selected. A trans
resistance amplifier is used in the first stage to convert the photodiode current to voltage. The
major design of this sensor is its output voltage and the output frequency. The output frequency
is band limited to 15 Hz using filters. Low pass first order butterworth filter is used. Low pass
filter is designed at 15 Hz upper cut off frequency with a gain of 1.5. A high pass first order
butter worth filter with lower cut off frequency of 0.5 Hz is cascaded with the low pass to
remove the dc voltage. An amplifier is set at the output of the PCI DIO 32 HS in order to raise
the output signal level to +5V (approx). Amplifier with amplification factor of 50 is designed.
Typical output of the sensor is shown on the graph below. Normal heart beat is 72 beats per
minute. That is the frequency of the signal is 1.2 Hz for a healthy person. The output amplitude
varies from 70mV to 120mV.
(ii) Seizure detector: Seizures are involuntary muscular movements which occur during
epilepsy. Muscular movements are sensed using MEMS (micro electro mechanical sensor)
accelerometer. A 3D accelerometer is used to sense the muscular movements. The SCXI-1530 is
a low cost, low power, complete 3-axis accelerometer with signal conditioned voltage outputs,
which is all on a single monolithic IC. The SCXI-1530 is a complete acceleration measurement
system on a single monolithic IC. The SCXI-1530 has a measurement range of ±3 g. The sensor
is a polysilicon surface-micro
machined structure built on top of a silicon wafer. Polysilicon springs suspend the structure over
the surface of the wafer and provide a resistance against acceleration forces. Deflection of the
structure is measured using a differential capacitor that consists of independent fixed plates and
plates
attached to the moving mass. The fixed plates are driven by 180° out-of-phase square waves.
Acceleration deflects the beam and unbalances the differential capacitor, resulting in an output
square wave whose amplitude is proportional to acceleration. Phase-sensitive demodulation
techniques are then used to rectify the signal and determine the direction of the acceleration.
The demodulator’s output is amplified and brought off-chip through a 32 k resistor. The signal
bandwidth of the device is set by adding a capacitor. This filtering improves measurement
resolution and helps prevent aliasing.
Performance of the project was affected due to the non availability of 3 axis accelerometer.
Hence here I have used a single axis accelerometer MMA1260EG from FREESCALE
semiconductor for detecting the muscular convulsions. It has a sensitivity of 1.5g. The output is
filtered using a n RC low pass filter with values R=1 K ad C=0.10.1μf. The output of the
sensor during typical seizure is shown on the graph. The output of MEMS is given to a 10 bit
analog to digital converter for digitizing the output.
(iii)Processor: The signals from sensors are processed using PIC18F4620 microcontroller. The
microcontroller requires a 10 bit ADC and a comparator circuit for processing the signals from
the sensor. PIC 18F 4620 includes built in ADC and comparator. The processor is clocked at
4MHz. The frequency of normal heart beat rate is 1.2 HZ. approximately. Or the time period of
the heart beat signal is 0.83 secs. The algorithm detects the sudden decrease in pulse width which
is one of the aura of epilepsy. As soon as the variations in the heart beat are detected, the
algorithm checks for the typical seizure waveform from the mems sensor. When these two
signals coincide, the software takes the decision as an epileptic seizure and generates control
signals.
(iv) Wireless transceiver: The device uses DATEx protocol for communication. The DATEx
Wireless Networking Protocol is a simple protocol designed for low data rate, short distance, and
low-cost networks. Fundamentally based on wireless personal area networks (WPANs), the
DATEx protocol provides an easy-to-use alternative for wireless communication. In particular, it
targets smaller applications that have relatively small network sizes, with few hops between
nodes, using Microchip’s MRF24J40 2.4 GHz transceiver for compliant networks. The DATEx
protocol is based on the MAC and PHY layers specification, and is tailored for simple network
development in the 2.4 GHz band. The protocol provides the features to find form and join a
network, as well as discovering nodes on the network and route to them. The card uses PCB
trace antenna. The device uses line of sight communication. The range is approx. 200ft. The
wireless transmitter and receiver hardware consist of a motherboard with PIC 18F4620
microcontroller. The motherboard consists of a daughter card with microchip MRF24J40 2.4
Ghz transceiver. The board is designed to work at 9V to 3.3 V DC. The DATEx protocol stack
can be installed on the board and the required application can be programmed into it. A peer to
peer network is formed using the transceivers.
One node is programmed as the network coordinator and the other as an end device. The
coordinator is set as the transmitter and the en device as the receiver. The long address is
assigned for the network and the short address to the nodes.
The device is designed in such a way that it searches for a network as soon as the module is
switched on. The coordinator assigns the address to the end devices and forms the network if
one is not detected.
The MRF24J40 is an compliant transceiver supporting DATEx, ZigBee™ and other proprietary
protocols. The MRF24J40 integrates wireless RF, PHY layer baseband and MAC layer
architectures that can be combined with a simple microprocessor to apply low data rate to a
multitude of applications The MRF24J40 device integrates a receiver, transmitter, VCO and PLL
into a single integrated circuit. It uses advanced radio architecture to minimize external part
count and power consumption. It mainly consists of TX/RX FIFOs, a CSMA-CA controller,
super frame Constructor, receive frame filter, security engine and digital signal processing
module. The MRF24J40 is fabricated by advanced 0.18 μm CMOS process and is offered in a
40-pin QFN 6x6 mm2 package.
The MRF24J40 consists of four major functional blocks:
1. An SPI interface that serves as a communication channel between the host controller and the
MRF24J40.
2. Control registers which are used to control and monitor the MRF24J40.
3. The MAC (Medium Access Control) module that implements compliant MAC logic.
4. The PHY (Physical Layer) driver that encodes and decodes the analog data. The device also
contains other support blocks, such as the on-chip voltage regulator, security module and system
control logic.
4. Design of software
The processing unit utilizes the logic implemented in the software for accurate detection of
seizures. The software checks the input signal from the PCI DIO 32 HS from the patient’s body
continuously and measures the pulse width of the signal. This width is converted into heart beat
rate by the
software. If there is any abnormalities in heart beat, it can be detected as a change in the pulse
width .As soon as the logic detects a change it triggers the vibrator and the system waits for the
response. The patient has to press a button on his wearable unit. If the patient is unable to do so
due to
occurrence of seizure, then response signal from MEM sensor which senses the muscular
convulsions is captured and analyzed. If there are signals of muscular convulsions the software
concludes that the patient has seizure and warning message is transmitted using the wireless
transmitter. The
seizure detection algorithm from the MEMS signals is to check only the sudden abnormality
occurring in the human body. This algorithm helps to avoid situations where heart beat rises due
to excessive physical work or due to tension etc. The algorithm uses the averaging technique to
determine
abnormalities accurately.
P=(P+N)/2
where p=previous heart beat rate
N=next heart beat rate.
For a person suffering from epilepsy, in the pre ictal stage the heart beat varies abruptly and
hence the value of P also changes. This change in the value of P is detected and the program is
made to wait for the signal from the second sensor which senses the muscular convulsions. If
muscular convulsions are detected from the second sensor, it triggers the transmitter on which
transmits a coded signal which is received by the receiver. The software section contains the
following major functional modules:
1. Heart beat rate calculations
2. Seizure detection from MEMS signal
3. Communication control
4. Overall supervision
5. Implementation
The system requires a heart beat sensor, muscular convulsion sensor, a transmitter, receiver,
mobile messaging device, alarm device and automatic vehicle control system. All the above said
parts are integrated together to a processor to form the device. The epilepsy prediction system
can be practically implemented by incorporating the following components:
a) Heart beat sensor: A pulse oxy meter is used as a heart beat sensor. The implementation of
PCI DIO 32 HS is by cascading several stages as shown in the figure 4. A high pass filter is
designed with lower cut off frequency of 15 Hz. .the high pass filter is cascaded with a low pass
filter designed to an upper cut off frequency of 0.5 Hz. The amplifier at the final stage raises the
voltage from mV level to the required voltage range. An amplification factor of 50 is given to it.
b) Convulsions sensor: An accelerometer is used as a convulsion sensor. Muscular convulsions
are detected using single axis mems IC MMA1260EG. The sensitivity of the sensor is set to
1.55g. The circuit is implemented as shown in the circuit diagram. The output of the sensor is
filtered out sing a low pass RC filter externally. The value of R is selected as 1K and C as
0.1μf.
c) Processing unit: The processing unit contains PIC 18F4620 microcontroller which is clocked
at 40 MHz.. PIC18F4620 have 64 Kbytes of Flash memory. The microcontroller has inbuilt 10
bit ADC which is used to digitize the output from MEMS module. It also includes a comparator
which is used to process the heart beat waveforms from the pulse oxy meter. The incoming
signal is processed using logics implemented in the software which runs the device. The
processing unit continuously checks for symptoms in the incoming signal. As soon as it detects
any abnormality, it triggers a warning
vibrator and the wireless transmitter.
d) Wireless Transmitter and receiver: Wireless transceiver consist of a board consisting of
MRF24J40 IC The transmitter transmits a coded signal which is decoded by a receiver to
generate control signals. The control signal activates an alarm device, mobile messaging device
and automatic vehicle control system appropriately. Apart from the above important blocks, a
buzzer circuit and a DC to DC convertor blocks are also implemented.
e) Enclosure design: The device is a wearable one (on the wrist). Hence the
enclosure is designed suiting to that purpose. The enclosure can be designed in the form of a
watch.
6. Software tools used:
1. MPLAB Integrated development Environment
2. Microchip C18 compiler
3. Keil Integrated development environment
7. Testing
(i) Testing of PCI DIO 32 HS: The PCI DIO 32 HS was tested by wounding the probe of the
device on the index finger of a person and the output were viewed on a DSO. The output is
shown in the graph given below. The PCI DIO 32 HS successfully detected the heart beat
waveform from the patient’s index finger. The out put frequency was 1.2 Hz . And the voltage
level was in the range of 100 to 120 mV.
(ii) Testing of MEMS sensor: The MEMS sensor is connected to the body of the patient using
straps. Typical epileptic seizure waveform is shown in the
figure below. This stage is not yet fully tested and testing is under way.
(iii)Testing of software: The inputs from the sensors were provided to the PIC controller in
which the software was programmed. Wave forms describing different conditions of the patient
were given as input and tested .
(iv)Testing of communication module The transceiver is directly connected to the
microcontroller in which the software was programmed. As soon as the software detected the
epileptic symptom, the transmitter was triggered. Using Zena network analyzer, the network was
detected at a frequency of 2.4G Hz. A peer to peer single node network was formed which
transmitted the message to the receiver node. The system designed here processes the heart beat
continuously and abnormalities are detected accurately. The device transmits the signal only
when seizures of epilepsy are detected. The performance of the device is not restricted by
movement of the patient. By using this device the patient can move freely without worries.
8. Problems encountered
We have encountered many problems as noted below:
1. Non availability of 3 axis accelerometer: We could not procure the 3 axis accelerometer and
hence testing is only performed with a single axis accelerometer. However, the system gives
better results only if a 3 axis accelerometer is used in for detecting muscle contractions.
2. Noise and temperature effect on the sensor outputs: Major problems were encountered due to
noise picked up by the sensors. Use of shielded cable and grounding solved the problems to a
satisfactory level. Heating effect of active components like op amps also created problems like
drifting and thermal noise. This was solved by operating the op amps at a lower voltage.
3. Problem with suitable wearable enclosure: A suitable wearable enclosure is not designed.
Compact PCB must be designed to fit all the components inside a wearable enclosure.
9. Advantages and benefits
The benefit of the project is that a lightweight, rugged, lowcost, wearable (on the wrist) device is
developed which helps a victim of epilepsy to do all sorts of activities like others do.
The device will be extremely cost effective since it uses simple sensors and technology for the
detection.
The sensors are small in size and can be firmly attached to the body.
Batteries can last long as the device consumes only little energy.
The device doesn’t restrict the movement of the patient.
The system is easily expandable paving the way to incorporate much more sophisticated
devices like ECG detector in the future.
Stand alone application.
10. Improvements
The system is easily expandable to incorporate GPS system and to capture and transmit various
patient parameters like ECG , body temperature etc.
11. Conclusion
A light weight, rugged, cost-effective wearable device is developed which helps millions of
victims of epilepsy around the globe. With the device in possession an epilepsy victim can move
around freely like normal people sans worries.
Visual Aid System for Blind Persons using proximity sensors,Bluetooth and LABVIEW
Benefits of Virtual Instrumentation:
The very concept of Virtual Instrumentation has made the job of design testing much easier.
Now instead of managing every hardware component, ensuring their functionality and the
final interconnections in the whole design, one can simply design the module on virtual
instrumentation software such as LabVIEW and vary the various design parameters to get the
optimum results making it highly user friendly. Also one can eliminate the need of costly
devices as they can be simulated and the errors can be easily identified and rectified. Virtual
Instrumentation not only makes the designing process simpler but also cheaper. Also as the
concept of virtual instrumentation is based on standard commercial technologies it easily
serves the masses. Thus Virtual Instrumentation enables students, engineers and scientists to
build powerful applications for increasing productivity and performance throughput.
The Challenge:
Developing an automated system for blind persons using proximity sensors aimed to enhance
their understanding of the surrounding through their hearing capacity.
The Solution:
We are putting forth a solution for blind people inspired by echolocation used by bats for
detecting surroundings. Bats produce high frequency sonar waves which after getting echoed
back from various obstacles give information about obstacles ahead. Here we introduce a
signal manipulating control system that aids visually challenged people to know their
surrounding using its smart sensing and feedback mechanism. Proximity sensors are designed
to emit infrared rays and receive the bouncing back rays from the obstacles. The received
wave is converted to voltage and compared with known templates which were stored in
device memory when the device was initially tested at known distances from the obstacles.
Then appropriate directing signal is conveyed via Bluetooth technology to a mobile device
and then is translated in real time into sound signals to guide the person accordingly, for
avoiding obstacles.
Introduction:
There are millions of visually challenged people across the globe dependent on others for
guiding them through pathways. This system is designed to help such people by giving them
an idea of their surrounding using the basic reflection techniques. This thought is inspired by
echolocation used by bats for detecting surroundings. Bats produce high frequency sonar
waves which after getting echoed back from various obstacles give information about
obstacles ahead. Here we introduce a signal manipulating control system that aids visually
challenged people to know their surrounding using its smart sensing and feedback
mechanism. Proximity sensors are designed to emit infrared rays and receive the bouncing
back rays from the obstacles. The output DC Voltage obtained from receiver ADC is fed into
another channel of NI 9201 I/P module. We are making use LABVIEW 8.2 with RT and
FPGA modules. Where there is no object, there is no feedback of echoed rays. The positive
signals are compared with known templates which were stored in device memory when the
device was initially tested at known distances from the obstacles. Then appropriate directing
signal is conveyed via Bluetooth technology to a mobile device and then is translated in real
time into sound frequencies to guide person accordingly for avoiding obstacles.
System Implementation:
The software of the system is basically divided into two Modules: Calibration and
Acquisition Modules.
1) Acquisition Module:
The system setup comprises of a Compactrio with an analog input and output modules. This
enables us in building our application in real-time with high determinism. We have used
Honeywell’s ultrasonic proximity sensors for obstacle distance acquisition.
In order to detect the obstacles we introduce the concept of ultrasonic proximity sensors.
Besides having a detection range of 20 meters, this Ultrasonic Proximity Sensor-946
(Honeywell) has an inbuilt waveform generator of 30 kHz. On detection, these sensors
produce a DC voltage inversely proportional to distance of the obstacle from it, by
programmatically introducing a negative slope for voltage output. This voltage produced is
fed into the Compactrio 9201 (Analog Input Module) to be monitored in LabVIEW.
2) Calibration Module:
This module is used to digitize the voltage input into four levels. The decision of movement
is taken by the output from this module. The output of the receiver is scaled to values 0 or 1
depending on the voltage.
Software Implementation:
We make use of LabVIEW RT and LabVIEW FPGA in our system development. Depending
upon the measurements from Proximity sensors, we can easily visualize the four basic
conditions on the front panel. We can also measure the distance of obstacles from the two
sensors which is calibrated with the output voltage of the sensors. There will be four basic
signals of: move left, move right, move back and no command. Hence, LabVIEW monitors
data from the proximity sensors. After monitoring and comparing data these sensors, it
generates a control signal through the Compactrio analog output module to actuate the voice
signal by earphone connected to mobile using Bluetooth technology. This enables us to
develop a Real-time data acquisition system that is highly deterministic and possess
standalone characteristics.
Figure1:Front panel of VI
Figure2:Block Diagram of VI
System Setup:
The input design consists of 2 proximity sensors transmitter receiver pair. The tranciever
transmits infrared rays which are then received/ absorbed by the receiver after getting reflected
from the obstacles. Now an ADC is used to convert the analog input voltage from the receiver to
a digital value. The digital value is then compared with the stored values in the memory. Finally a
2 bit binary value is computed after the comparisons. This value now is used to select the desired
sound track for the guidance of the person.
The selected sound track is played in a mobile by interfacing through GSM module.
The VI shows the sound track selection procedure. The input of the receivers are polled for after a
certain interval of time. If there is no obstacle there is no audio input to the person, it means he
can go straight.
Theory and Result:
The proximity sensors sense the obstacle distance, and if it measures it to be above a
specified requirement, it gives signal to device to avoid obstacle. Thus the blind person would
come to know of his/her proximity by the device only and hence would intend to avoid it
without the help of others.
Future scope:
The design implemented is a basic model with many simplifying assumptions because of lack of
the available resources. The system can be designed to a level to give a complete picture of the
surrounding using an array of proximity sensors and interfacing the output through Braille code.
References:
1) www.ni.com/India
2) www.honeywell.com
3) www.howstuffoworks.com
4) Computer based electronic Measurement: An introductory electronics laboratory
Workbook; Based on LabVIEW and virtual bench -A. Bruce Buckman
5) Labview Basics 8.1 Training CD1 and CD2
The very concept of Virtual Instrumentation has made the job of design testing much easier.
Now instead of managing every hardware component, ensuring their functionality and the
final interconnections in the whole design, one can simply design the module on virtual
instrumentation software such as LabVIEW and vary the various design parameters to get the
optimum results making it highly user friendly. Also one can eliminate the need of costly
devices as they can be simulated and the errors can be easily identified and rectified. Virtual
Instrumentation not only makes the designing process simpler but also cheaper. Also as the
concept of virtual instrumentation is based on standard commercial technologies it easily
serves the masses. Thus Virtual Instrumentation enables students, engineers and scientists to
build powerful applications for increasing productivity and performance throughput.
The Challenge:
Developing an automated system for blind persons using proximity sensors aimed to enhance
their understanding of the surrounding through their hearing capacity.
The Solution:
We are putting forth a solution for blind people inspired by echolocation used by bats for
detecting surroundings. Bats produce high frequency sonar waves which after getting echoed
back from various obstacles give information about obstacles ahead. Here we introduce a
signal manipulating control system that aids visually challenged people to know their
surrounding using its smart sensing and feedback mechanism. Proximity sensors are designed
to emit infrared rays and receive the bouncing back rays from the obstacles. The received
wave is converted to voltage and compared with known templates which were stored in
device memory when the device was initially tested at known distances from the obstacles.
Then appropriate directing signal is conveyed via Bluetooth technology to a mobile device
and then is translated in real time into sound signals to guide the person accordingly, for
avoiding obstacles.
Introduction:
There are millions of visually challenged people across the globe dependent on others for
guiding them through pathways. This system is designed to help such people by giving them
an idea of their surrounding using the basic reflection techniques. This thought is inspired by
echolocation used by bats for detecting surroundings. Bats produce high frequency sonar
waves which after getting echoed back from various obstacles give information about
obstacles ahead. Here we introduce a signal manipulating control system that aids visually
challenged people to know their surrounding using its smart sensing and feedback
mechanism. Proximity sensors are designed to emit infrared rays and receive the bouncing
back rays from the obstacles. The output DC Voltage obtained from receiver ADC is fed into
another channel of NI 9201 I/P module. We are making use LABVIEW 8.2 with RT and
FPGA modules. Where there is no object, there is no feedback of echoed rays. The positive
signals are compared with known templates which were stored in device memory when the
device was initially tested at known distances from the obstacles. Then appropriate directing
signal is conveyed via Bluetooth technology to a mobile device and then is translated in real
time into sound frequencies to guide person accordingly for avoiding obstacles.
System Implementation:
The software of the system is basically divided into two Modules: Calibration and
Acquisition Modules.
1) Acquisition Module:
The system setup comprises of a Compactrio with an analog input and output modules. This
enables us in building our application in real-time with high determinism. We have used
Honeywell’s ultrasonic proximity sensors for obstacle distance acquisition.
In order to detect the obstacles we introduce the concept of ultrasonic proximity sensors.
Besides having a detection range of 20 meters, this Ultrasonic Proximity Sensor-946
(Honeywell) has an inbuilt waveform generator of 30 kHz. On detection, these sensors
produce a DC voltage inversely proportional to distance of the obstacle from it, by
programmatically introducing a negative slope for voltage output. This voltage produced is
fed into the Compactrio 9201 (Analog Input Module) to be monitored in LabVIEW.
2) Calibration Module:
This module is used to digitize the voltage input into four levels. The decision of movement
is taken by the output from this module. The output of the receiver is scaled to values 0 or 1
depending on the voltage.
Software Implementation:
We make use of LabVIEW RT and LabVIEW FPGA in our system development. Depending
upon the measurements from Proximity sensors, we can easily visualize the four basic
conditions on the front panel. We can also measure the distance of obstacles from the two
sensors which is calibrated with the output voltage of the sensors. There will be four basic
signals of: move left, move right, move back and no command. Hence, LabVIEW monitors
data from the proximity sensors. After monitoring and comparing data these sensors, it
generates a control signal through the Compactrio analog output module to actuate the voice
signal by earphone connected to mobile using Bluetooth technology. This enables us to
develop a Real-time data acquisition system that is highly deterministic and possess
standalone characteristics.
Figure1:Front panel of VI
Figure2:Block Diagram of VI
System Setup:
The input design consists of 2 proximity sensors transmitter receiver pair. The tranciever
transmits infrared rays which are then received/ absorbed by the receiver after getting reflected
from the obstacles. Now an ADC is used to convert the analog input voltage from the receiver to
a digital value. The digital value is then compared with the stored values in the memory. Finally a
2 bit binary value is computed after the comparisons. This value now is used to select the desired
sound track for the guidance of the person.
The selected sound track is played in a mobile by interfacing through GSM module.
The VI shows the sound track selection procedure. The input of the receivers are polled for after a
certain interval of time. If there is no obstacle there is no audio input to the person, it means he
can go straight.
Theory and Result:
The proximity sensors sense the obstacle distance, and if it measures it to be above a
specified requirement, it gives signal to device to avoid obstacle. Thus the blind person would
come to know of his/her proximity by the device only and hence would intend to avoid it
without the help of others.
Future scope:
The design implemented is a basic model with many simplifying assumptions because of lack of
the available resources. The system can be designed to a level to give a complete picture of the
surrounding using an array of proximity sensors and interfacing the output through Braille code.
References:
1) www.ni.com/India
2) www.honeywell.com
3) www.howstuffoworks.com
4) Computer based electronic Measurement: An introductory electronics laboratory
Workbook; Based on LabVIEW and virtual bench -A. Bruce Buckman
5) Labview Basics 8.1 Training CD1 and CD2
Computerized color blindness using LABVIEW
Abstract:
These days human race is facing
lots of physical and mental problems.
Now a day’s diagnosis of problems at
their earlier stage is very important for
providing complete cure to them. Here
we present a fully automated,
computerized technique for diagnosing
human visual problems. We have
utilized ‘LabVIEW’ for its precision and
accuracy. Visual diagnosis includes
color blindness – inability to perceive
differences between some of the colors
that others can be distinguished, because
of eye, nerve or brain damage are due to
exposure to certain chemicals.
Other tests include visual acuity
–quantitative measure of the ability to
identify the black symbols on a white
background at a standardized distance as
the size of the symbol is varied. It is
based on the sharpness of the retinal
focus and the sensitivity of interpretative
faculty of the brain. Visual field test
performed to analyze a patient’s visual
field. The test may be performed by a
technician using an automated machine.
Other names for this test may include
permity, tangent screen exam, etc… This
virtual instrument for screening vision
problem is ideal for mass screening in
education institutions, clinics, etc., for
the early detection of color blindness.
Keywords:
Color blindness; Visual acuity; Visual
field test; Ishihara test; Lab VIEW
8.5 .
Introduction:
The early diagnosis of the problem is
very vital for providing complete cure to
the problem. Now for year’s technology
is playing a most vital role in diagnosing
problems related to medical sciences.
This paper proposes a methodology for
automatically diagnosing the human eye
for colorblindness. Color blindness, a
color vision deficiency, is the inability to
perceive differences between some of
the colors that others can distinguish.
National Semiconductors LABVIEW 8.5
(Virtual Laboratory) software is used for
its precision and reliability. In addition
to diagnosis of colorblindness the
scheme is also capable of testing human
eye for visual acuity test. The scheme is
completely automated and computerized
providing erroneous results after
diagnosis.
Visual acuity (VA) is acuteness
or clearness of vision, especially form
vision, which is dependent on the
sharpness of the retinal focus within the
eye and the sensitivity of the
interpretative faculty of the brain. Many
people think anyone labeled as
"colorblind" only sees black and white -
like watching a black and white movie
or television. This is a big misconception
and not true. It is extremely rare to be
totally color blind (monochromes -
complete absence of any color
sensation). There are many different
types and degrees of colorblindness -
more correctly called color vision
deficiencies. Color blindness results
from an absence or malfunction of
certain color-sensitive cells in the retina.
The retina is the nerve layer at the back
of the eye that converts light into nerve
signals that are sent to the brain. A
person with color blindness has trouble
COMPUTERIZED COLOR BLINDNESS TEST USING Lab VIEW
seeing red, green, blue, or mixtures of
these colors. As the proposed
methodology uses the concept of
combination of RGB colors, it is capable
of providing more accurate results.
Color Blindness – A hereditary
syndrome:
Color blindness may be a hereditary
condition or caused by disease of the
optic nerve or retina. Acquired color
vision problems only affect the eye with
the disease and may become
progressively worse over time. Patients
with a color vision defect caused by
disease usually have trouble
discriminating blues and yellows.
Inherited color blindness is most
common, affects both eyes, and does not
worsen over time. This type is found in
about 8% of males and 0.4% of females.
These color problems are linked to the X
chromosome and are almost always
passed from a mother to her son.
Inherited color blindness can be
congenital (from birth), or it can
commence in childhood or adulthood.
Depending on the mutation, it
can be stationary, that is, remain the
same throughout a person's lifetime, or
progressive. As progressive phenotypes
involve deterioration of the retina and
other parts of the eye, certain forms of
color blindness can progress to legal
blindness, i.e., an acuity of 6/60 or
worse, and often leave a person with
complete blindness. Color blindness
always pertains to the cone
photoreceptors in our retina as the cones
are capable of detecting the color
frequencies of light we perceive. There
are 3 types of cones, each responsible for
detecting either red, green or blue.
Detection and Diagnosis: Color
vision deficiency is most commonly
detected with special colored charts
called the Ishihara Test Plates. On each
plate is a number composed of colored
dots. While holding the chart under
good lighting, the patient is asked to
identify the number. Once the color
defect is identified, more detailed color
vision tests may be performed.
COMPUTERIZED COLOR BLINDNESS TEST USING Lab VIEW
LabVIEW:
We have utilized National
Instruments LAB VIEV 8.5 for in this
paper because of its easy user interfacing
ability which is important for medical
oriented applications. LabVIEW is a
graphical programming language that
uses icons instead of lines of text to
create applications. In contrast to textbased
programming languages, where
instructions determine program
execution, LabVIEW uses dataflow
programming, where the flow of data
determines execution. The user interface
is known as the front panel. Then the
code is added using graphical
representations of functions to control
the front panel objects.
Algorithm:
STEP 1: Generate the background of
random number display with all the Red
Green and Blue combinations.
STEP 2: Generate random number that
has to be displayed in the respective
either in Red, Green or Blue
background. It differs in every case.
STEP 3: Continuous check will begin
on clicking the start button. Once the
start button is clicked the count of
respective display also starts.
STEP 4: Stop button is clicked if the
random number is visible in the RGB
background. Once stop is clicked stop
the count of the respective display.
STEP 5: Above steps are repeated for
other RGB combinations.
STEP 6: Report is generated.
Tab controls:
Tab1 is for red green combination .In
the first tab we have red background
with some green color number. The
intensity of the green color in the first
tab increases linearly with respect to
time as soon as the subject clicks the
start button. At particular value of count,
the subject (person under test) is able to
see the number inside the display. That
value is the threshold for the subject.
Then we have to compare this value with
the normal one. Based on the amount of
deviation, we need to calculate the
percentage of color vision deficiency.
That has to be reported to the user in
addition to the other color combination
deficiencies. Same logic is for all other
RGB combination. TAB 5(GREEN
BLUECOMBINATION), TAB 7(blue
red combination).
Working and Testing:
The logic behind this colorblindness
test is implemented within the while
loop. It consist of 9 different cases with
3 colors (Red, blue and green), with each
color dominating in 3 cases respectively.
Selection of a particular case is achieved
by using the tab control. RGB values
encoded in 24 bits per pixel (bpp) are
specified using three 8-bit unsigned
integers (0 through 255) representing the
intensities of red, green, and blue. This
representation is the current mainstream
standard representation for the so-called
true color and common color
interchange in image file formats such as
JPEG or TIFF.
It allows more than 16 million
different combinations (hence the term
millions of colors some systems use for
this mode), many of them
COMPUTERIZED COLOR BLINDNESS TEST USING Lab VIEW
indistinguishable to the human eye. The
main function of this program is that it
converts RGB colors to some decimal
values. The number displayed in the
front panel is randomly generated using
the number icon. The same number will
not appear when switched from one case
to another again to the former case.
Random numbers from zero to one are
generated by the random icon which is
manipulated by a block and the same
number is converted to decimal and
displayed inside the new picture frame,
which is in the main frame. Start and
stop icons are used for start /stop the
random counter and quit button for
aborting the entire coding.
Inference:
These tests have been
implemented to many people of different
age groups and test. Their visual acuity
is measured in terms of percentage and
tabulated. The normal count for each
combination is also identified by
checking the visual acuity of person with
normal vision.
Table 1: Inferred results
COMBINATIONS NORMAL COUNT
RED GREEN 48 -- 50
GREEN BLUE 39 --- 42
BLUE RED 49 --- 52
Any deviation from the above count
obtained from the survey can be
conclude that the person is affected by
color blindness. There will be a
deviation in normal count for the persons
with defects like myopia, hypermetropia,
presbyopia etc
Advantages:
The functional model,
organization and a programming
architecture of LAB VIEW are the
essential part of a virtual instrumentation
and this helps us to drive the program
easily. This makes us possible to
perform the function of an instrument in
any local or remote area with less
expense. The unique advantage of
virtual instrumentation is the facility to
develop a wide range of applications
with a minimum inventory of hardware.
The design flexibility is provided by the
sophisticated (and yet simple to use)
graphical programming language.
This proposal is very simple, and
easy for the patient to identify whether
they are deficient to red/green (or) blue
color blindness. This scheme is entirely
computerized with LAB VIEW, thus it is
very accurate and cheap. Manipulation
can be done for the betterment under
certain circumstances. This method of
color blindness test employ only very
less technicians when compared to other
laser test where sophistication increases
due to complexity in manufacture also
due to manual working. Here
simultaneously the visual acuity test can
also be performed. The test can be done
for different color combinations at the
same time with the patient before the
computer.
Conclusion:
The proposed methodology is
automated and computerized scheme
capable of giving most accurate results
possible. Our scheme will be a
replacement for the age old method of
colorblindness identification special
colored charts called the Ishihara Test
COMPUTERIZED COLOR BLINDNESS TEST USING Lab VIEW
Plates. The scheme also helps to
diagnose the individual for visual acuity
and field vision tests which are also
common among tests conducted world
over. We have tested and identified that
the proposed scheme is accurate and
ideal for mass screening in schools,
colleges, factories etc and sure be a
important tool for early identification
thereby leading to complete eradication
of colorblindness from our society. the
scheme is also used for performing
visual acuity and field effect tests. This
virtual instrument for screening vision
problem is ideal for mass screening in
education institutions, clinics, etc., for
the early detection of color blindness
These days human race is facing
lots of physical and mental problems.
Now a day’s diagnosis of problems at
their earlier stage is very important for
providing complete cure to them. Here
we present a fully automated,
computerized technique for diagnosing
human visual problems. We have
utilized ‘LabVIEW’ for its precision and
accuracy. Visual diagnosis includes
color blindness – inability to perceive
differences between some of the colors
that others can be distinguished, because
of eye, nerve or brain damage are due to
exposure to certain chemicals.
Other tests include visual acuity
–quantitative measure of the ability to
identify the black symbols on a white
background at a standardized distance as
the size of the symbol is varied. It is
based on the sharpness of the retinal
focus and the sensitivity of interpretative
faculty of the brain. Visual field test
performed to analyze a patient’s visual
field. The test may be performed by a
technician using an automated machine.
Other names for this test may include
permity, tangent screen exam, etc… This
virtual instrument for screening vision
problem is ideal for mass screening in
education institutions, clinics, etc., for
the early detection of color blindness.
Keywords:
Color blindness; Visual acuity; Visual
field test; Ishihara test; Lab VIEW
8.5 .
Introduction:
The early diagnosis of the problem is
very vital for providing complete cure to
the problem. Now for year’s technology
is playing a most vital role in diagnosing
problems related to medical sciences.
This paper proposes a methodology for
automatically diagnosing the human eye
for colorblindness. Color blindness, a
color vision deficiency, is the inability to
perceive differences between some of
the colors that others can distinguish.
National Semiconductors LABVIEW 8.5
(Virtual Laboratory) software is used for
its precision and reliability. In addition
to diagnosis of colorblindness the
scheme is also capable of testing human
eye for visual acuity test. The scheme is
completely automated and computerized
providing erroneous results after
diagnosis.
Visual acuity (VA) is acuteness
or clearness of vision, especially form
vision, which is dependent on the
sharpness of the retinal focus within the
eye and the sensitivity of the
interpretative faculty of the brain. Many
people think anyone labeled as
"colorblind" only sees black and white -
like watching a black and white movie
or television. This is a big misconception
and not true. It is extremely rare to be
totally color blind (monochromes -
complete absence of any color
sensation). There are many different
types and degrees of colorblindness -
more correctly called color vision
deficiencies. Color blindness results
from an absence or malfunction of
certain color-sensitive cells in the retina.
The retina is the nerve layer at the back
of the eye that converts light into nerve
signals that are sent to the brain. A
person with color blindness has trouble
COMPUTERIZED COLOR BLINDNESS TEST USING Lab VIEW
seeing red, green, blue, or mixtures of
these colors. As the proposed
methodology uses the concept of
combination of RGB colors, it is capable
of providing more accurate results.
Color Blindness – A hereditary
syndrome:
Color blindness may be a hereditary
condition or caused by disease of the
optic nerve or retina. Acquired color
vision problems only affect the eye with
the disease and may become
progressively worse over time. Patients
with a color vision defect caused by
disease usually have trouble
discriminating blues and yellows.
Inherited color blindness is most
common, affects both eyes, and does not
worsen over time. This type is found in
about 8% of males and 0.4% of females.
These color problems are linked to the X
chromosome and are almost always
passed from a mother to her son.
Inherited color blindness can be
congenital (from birth), or it can
commence in childhood or adulthood.
Depending on the mutation, it
can be stationary, that is, remain the
same throughout a person's lifetime, or
progressive. As progressive phenotypes
involve deterioration of the retina and
other parts of the eye, certain forms of
color blindness can progress to legal
blindness, i.e., an acuity of 6/60 or
worse, and often leave a person with
complete blindness. Color blindness
always pertains to the cone
photoreceptors in our retina as the cones
are capable of detecting the color
frequencies of light we perceive. There
are 3 types of cones, each responsible for
detecting either red, green or blue.
Detection and Diagnosis: Color
vision deficiency is most commonly
detected with special colored charts
called the Ishihara Test Plates. On each
plate is a number composed of colored
dots. While holding the chart under
good lighting, the patient is asked to
identify the number. Once the color
defect is identified, more detailed color
vision tests may be performed.
COMPUTERIZED COLOR BLINDNESS TEST USING Lab VIEW
LabVIEW:
We have utilized National
Instruments LAB VIEV 8.5 for in this
paper because of its easy user interfacing
ability which is important for medical
oriented applications. LabVIEW is a
graphical programming language that
uses icons instead of lines of text to
create applications. In contrast to textbased
programming languages, where
instructions determine program
execution, LabVIEW uses dataflow
programming, where the flow of data
determines execution. The user interface
is known as the front panel. Then the
code is added using graphical
representations of functions to control
the front panel objects.
Algorithm:
STEP 1: Generate the background of
random number display with all the Red
Green and Blue combinations.
STEP 2: Generate random number that
has to be displayed in the respective
either in Red, Green or Blue
background. It differs in every case.
STEP 3: Continuous check will begin
on clicking the start button. Once the
start button is clicked the count of
respective display also starts.
STEP 4: Stop button is clicked if the
random number is visible in the RGB
background. Once stop is clicked stop
the count of the respective display.
STEP 5: Above steps are repeated for
other RGB combinations.
STEP 6: Report is generated.
Tab controls:
Tab1 is for red green combination .In
the first tab we have red background
with some green color number. The
intensity of the green color in the first
tab increases linearly with respect to
time as soon as the subject clicks the
start button. At particular value of count,
the subject (person under test) is able to
see the number inside the display. That
value is the threshold for the subject.
Then we have to compare this value with
the normal one. Based on the amount of
deviation, we need to calculate the
percentage of color vision deficiency.
That has to be reported to the user in
addition to the other color combination
deficiencies. Same logic is for all other
RGB combination. TAB 5(GREEN
BLUECOMBINATION), TAB 7(blue
red combination).
Working and Testing:
The logic behind this colorblindness
test is implemented within the while
loop. It consist of 9 different cases with
3 colors (Red, blue and green), with each
color dominating in 3 cases respectively.
Selection of a particular case is achieved
by using the tab control. RGB values
encoded in 24 bits per pixel (bpp) are
specified using three 8-bit unsigned
integers (0 through 255) representing the
intensities of red, green, and blue. This
representation is the current mainstream
standard representation for the so-called
true color and common color
interchange in image file formats such as
JPEG or TIFF.
It allows more than 16 million
different combinations (hence the term
millions of colors some systems use for
this mode), many of them
COMPUTERIZED COLOR BLINDNESS TEST USING Lab VIEW
indistinguishable to the human eye. The
main function of this program is that it
converts RGB colors to some decimal
values. The number displayed in the
front panel is randomly generated using
the number icon. The same number will
not appear when switched from one case
to another again to the former case.
Random numbers from zero to one are
generated by the random icon which is
manipulated by a block and the same
number is converted to decimal and
displayed inside the new picture frame,
which is in the main frame. Start and
stop icons are used for start /stop the
random counter and quit button for
aborting the entire coding.
Inference:
These tests have been
implemented to many people of different
age groups and test. Their visual acuity
is measured in terms of percentage and
tabulated. The normal count for each
combination is also identified by
checking the visual acuity of person with
normal vision.
Table 1: Inferred results
COMBINATIONS NORMAL COUNT
RED GREEN 48 -- 50
GREEN BLUE 39 --- 42
BLUE RED 49 --- 52
Any deviation from the above count
obtained from the survey can be
conclude that the person is affected by
color blindness. There will be a
deviation in normal count for the persons
with defects like myopia, hypermetropia,
presbyopia etc
Advantages:
The functional model,
organization and a programming
architecture of LAB VIEW are the
essential part of a virtual instrumentation
and this helps us to drive the program
easily. This makes us possible to
perform the function of an instrument in
any local or remote area with less
expense. The unique advantage of
virtual instrumentation is the facility to
develop a wide range of applications
with a minimum inventory of hardware.
The design flexibility is provided by the
sophisticated (and yet simple to use)
graphical programming language.
This proposal is very simple, and
easy for the patient to identify whether
they are deficient to red/green (or) blue
color blindness. This scheme is entirely
computerized with LAB VIEW, thus it is
very accurate and cheap. Manipulation
can be done for the betterment under
certain circumstances. This method of
color blindness test employ only very
less technicians when compared to other
laser test where sophistication increases
due to complexity in manufacture also
due to manual working. Here
simultaneously the visual acuity test can
also be performed. The test can be done
for different color combinations at the
same time with the patient before the
computer.
Conclusion:
The proposed methodology is
automated and computerized scheme
capable of giving most accurate results
possible. Our scheme will be a
replacement for the age old method of
colorblindness identification special
colored charts called the Ishihara Test
COMPUTERIZED COLOR BLINDNESS TEST USING Lab VIEW
Plates. The scheme also helps to
diagnose the individual for visual acuity
and field vision tests which are also
common among tests conducted world
over. We have tested and identified that
the proposed scheme is accurate and
ideal for mass screening in schools,
colleges, factories etc and sure be a
important tool for early identification
thereby leading to complete eradication
of colorblindness from our society. the
scheme is also used for performing
visual acuity and field effect tests. This
virtual instrument for screening vision
problem is ideal for mass screening in
education institutions, clinics, etc., for
the early detection of color blindness
Automated filling stations
BENEFITS OF VI
VI stands for Virtual Instrument. In one ways or the other Virtual Instrument can be called as a soft
computing tool. In simple terms it can do operations like addition to more complex fourier transforms,
PID controls etx.VI is typically an algorithm to implement stuff which are usually done by the hardware.
By adopting VI’s we can reduce cost of building an dedicated hardware for a particular operation. It also
gives the modularity so that we can add and remove codes whenever the situation demands.
HARDWARE USED
NI VISION MODULE 8.5, LabVIEW 8.5, IMAQ DRIVERS
PROBLEM IDENTIFIED
The problem with conventional filling system is that they are unable to fill variant capacities. This is
simply due to the fact that the system is unable to recognize the different volumes .
Empower the system to fill different sized bottles
Reduce operational costs.
Build a fully automated and safe pneumatic filling system.
SOLUTION
This can be overcome by using state of the art image processing techniques. Image processing in
Industrial control system is called Machine Vision system. The Machine Vision System gives the system
“the ability to see” and initiate accordingly.
BRIEF
The problem with conventional filling system is that they are unable to fill variant capacities. This is
simply due to the fact that the system is unable to recognize the different volumes .This can be overcome
by using state of the art image processing techniques. Image processing in Industrial control system is
called Machine Vision system. The Machine Vision System gives the system “the ability to see” and
initiate accordingly. This project involves the usage of Vision systems with LABVIEW and industry
standard controllers such as programmable logic controllers ( PLC ).The programmable logic controller
will be hooked to the LABVIEW through the OPC server , LABVIEW will act as an Human Machine
interface (HMI) .The advantage of the system is that they can be directly incorporated into any existing
sequential controller networkers .Other advantages include reduced erection and maintenance costs.
INTRODUCTION
The problem with conventional food packaging industry is that they are unable to sense the different size
of the quantity and pack accordingly .This tends to increase the operational costs and maintenance costs.
With the advent of Machine Vision Systems it becomes easy to solve these complex industrial problems .
Let’s take an example. Any soft drink company has filling stations but they have different filling stations
for their 200ml, 500ml, and 2 liter bottles. Erection of these filling stations and maintenance costs are
complex and expensive.
SYSTEM SETUP
In this project to solve the problem we will be using
• LABVIEW
• Conveyor Belt Assembly - with 24Vdc motor and rubber belt
• Standard Webcam - 30fps,320x240 pixel.
• Fiber Optic Sensor - 50mm max sensing distance.
• Pneumatic system - single acting, double acting cylinders and directional control valves .
• Power supply / Relay module – 24Vdc 5 Amps PS and 24vdc coil SPDT relays .
METHODOLOGY
When the bottle comes in-front of the proximity sensor .the proximity sensor picks the bottle up. This
signal is transferred to the PLC .The PLC will immediately stop the conveyor belt .Once the conveyor
belt is stopped the camera takes the snapshot of the bottle and sends the signal to the local station where
the bottles dimensions are estimated .These dimensions are compared with the standard one given by the
operator and the volume that needs to be filled is estimated. Since the flow is maintained a constant, the
volume to be filled in the bottle will only be a function of time . The camera constantly records the height
of the liquid in bottle when it reaches a point , the local computer sends a OFF signal to the PLC. The
PLC closes the valve and switches on the conveyor belt . This procedure is carried out repeatedly for
different sized bottles.
Check for
bottle
Stop Conveyor Belt
Process Image
Calculate bottle dimensions -
diameter
Trigger camera to take picture
Send signal to PLC
PLC controls filling valve
Conveyor is switched ON
Wait for bottle to
come in position
FRONTPANEL
The above frontpanel will be the base for further development.The front panel is now configured to a
open loop control for testing purposes but as we hook it up to the conveyor belt and pneumatic system it
will be totally automated.
The conveyor belt speed is a trigger from the fiber optic sensor to trigger the camera to take a image.This
will vary when its put in the automated system ,the system will take care of the speed of the conveyor belt
for the specific production number.
The Image browser shows a 500ml bottle and clamp feature which senses the distance.
BLOCK DIAGRAM
The block diagram is configured with a Express VI .The VISION code consists of brightness adjustement
and contrast adjustment. This is followed by edge detectors and clamps.The values are forwarded to
LabVIEW from where OPC Server communication and logic operations take place.
TIME AND MONEY SAVED
The image processing technique was developed overnight ( 4 hours ) .Cost of the system is reduced
considerably by using very commonly available equipment like webcam .Power consumption is reduced
by operating one single system for different capacity.
CONCLUSION
The system developed is highly flexible and reliable for the above-mentioned system.
It also ensures these advantages :
• Ability to fill different bottles
• Fully automated filling system
• Can be incorporated into any sequential controlled networks .
VI stands for Virtual Instrument. In one ways or the other Virtual Instrument can be called as a soft
computing tool. In simple terms it can do operations like addition to more complex fourier transforms,
PID controls etx.VI is typically an algorithm to implement stuff which are usually done by the hardware.
By adopting VI’s we can reduce cost of building an dedicated hardware for a particular operation. It also
gives the modularity so that we can add and remove codes whenever the situation demands.
HARDWARE USED
NI VISION MODULE 8.5, LabVIEW 8.5, IMAQ DRIVERS
PROBLEM IDENTIFIED
The problem with conventional filling system is that they are unable to fill variant capacities. This is
simply due to the fact that the system is unable to recognize the different volumes .
Empower the system to fill different sized bottles
Reduce operational costs.
Build a fully automated and safe pneumatic filling system.
SOLUTION
This can be overcome by using state of the art image processing techniques. Image processing in
Industrial control system is called Machine Vision system. The Machine Vision System gives the system
“the ability to see” and initiate accordingly.
BRIEF
The problem with conventional filling system is that they are unable to fill variant capacities. This is
simply due to the fact that the system is unable to recognize the different volumes .This can be overcome
by using state of the art image processing techniques. Image processing in Industrial control system is
called Machine Vision system. The Machine Vision System gives the system “the ability to see” and
initiate accordingly. This project involves the usage of Vision systems with LABVIEW and industry
standard controllers such as programmable logic controllers ( PLC ).The programmable logic controller
will be hooked to the LABVIEW through the OPC server , LABVIEW will act as an Human Machine
interface (HMI) .The advantage of the system is that they can be directly incorporated into any existing
sequential controller networkers .Other advantages include reduced erection and maintenance costs.
INTRODUCTION
The problem with conventional food packaging industry is that they are unable to sense the different size
of the quantity and pack accordingly .This tends to increase the operational costs and maintenance costs.
With the advent of Machine Vision Systems it becomes easy to solve these complex industrial problems .
Let’s take an example. Any soft drink company has filling stations but they have different filling stations
for their 200ml, 500ml, and 2 liter bottles. Erection of these filling stations and maintenance costs are
complex and expensive.
SYSTEM SETUP
In this project to solve the problem we will be using
• LABVIEW
• Conveyor Belt Assembly - with 24Vdc motor and rubber belt
• Standard Webcam - 30fps,320x240 pixel.
• Fiber Optic Sensor - 50mm max sensing distance.
• Pneumatic system - single acting, double acting cylinders and directional control valves .
• Power supply / Relay module – 24Vdc 5 Amps PS and 24vdc coil SPDT relays .
METHODOLOGY
When the bottle comes in-front of the proximity sensor .the proximity sensor picks the bottle up. This
signal is transferred to the PLC .The PLC will immediately stop the conveyor belt .Once the conveyor
belt is stopped the camera takes the snapshot of the bottle and sends the signal to the local station where
the bottles dimensions are estimated .These dimensions are compared with the standard one given by the
operator and the volume that needs to be filled is estimated. Since the flow is maintained a constant, the
volume to be filled in the bottle will only be a function of time . The camera constantly records the height
of the liquid in bottle when it reaches a point , the local computer sends a OFF signal to the PLC. The
PLC closes the valve and switches on the conveyor belt . This procedure is carried out repeatedly for
different sized bottles.
Check for
bottle
Stop Conveyor Belt
Process Image
Calculate bottle dimensions -
diameter
Trigger camera to take picture
Send signal to PLC
PLC controls filling valve
Conveyor is switched ON
Wait for bottle to
come in position
FRONTPANEL
The above frontpanel will be the base for further development.The front panel is now configured to a
open loop control for testing purposes but as we hook it up to the conveyor belt and pneumatic system it
will be totally automated.
The conveyor belt speed is a trigger from the fiber optic sensor to trigger the camera to take a image.This
will vary when its put in the automated system ,the system will take care of the speed of the conveyor belt
for the specific production number.
The Image browser shows a 500ml bottle and clamp feature which senses the distance.
BLOCK DIAGRAM
The block diagram is configured with a Express VI .The VISION code consists of brightness adjustement
and contrast adjustment. This is followed by edge detectors and clamps.The values are forwarded to
LabVIEW from where OPC Server communication and logic operations take place.
TIME AND MONEY SAVED
The image processing technique was developed overnight ( 4 hours ) .Cost of the system is reduced
considerably by using very commonly available equipment like webcam .Power consumption is reduced
by operating one single system for different capacity.
CONCLUSION
The system developed is highly flexible and reliable for the above-mentioned system.
It also ensures these advantages :
• Ability to fill different bottles
• Fully automated filling system
• Can be incorporated into any sequential controlled networks .
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