MOTORIZED WHEELCHAIR CONTROL USING
ELECTROOCULOGRAM AND HEAD GEAR
Jayetri Bardhan, Suma P, M Jyothirmayi
M.S. Ramaiah Institue of Technology, Electronics and Instrumentation Engineering,
Bangalore, India
Abstract—A significant portion of our society suffers from one or
the other kind of disabilities due to accidents or neurological
disorders (eg: paralysis) etc. Due to these disabilities, the physically
impaired are dependent on others for their everyday routine
activities. To assist these patients, rehabilitation devices are used.
These rehabilitation devices are controlled by generation of
biomedical signals for example electro-oculogram(EOG) and
Electroencephalography (EEG). EOG signal is captured by
Ag/AgCl electrodes and EEG signal is captured by headgear. These
signals are integrated by microcontroller which in turn controls the
movement of the wheel chair.
Keywords—Electro-oculogram
(EEG), headgear.
(EOG),
Electroencephalography
I. INTRODUCTION
With the rapid increase in technology in the field of medicine,
people suffering from paralysis and also those who are unable
to speak can lead their life independently by controlling
rehabilitative devices[1]. They are dependent on others for
their everyday routine activities. Even if they want to move
from one place to another, they need to seek others’ help.
Since they are unable to speak, they cannot use “speech
controlled wheelchairs” but they have the ability to coordinate
eye movements and control their thoughts. Bio-signals (e.g.
Electroencephalogram and Electro-oculogram etc.) can be
used as a medium of control[2]. Electro-oculogram is one of
the methods which corresponds to the potential difference
between the retina and the cornea of the eye based on the eye
movement of the user. Electrically, the eye is considered as a
spherical battery, with the positive terminal in front at the
cornea, and the negative terminal behind at the retina of the
eyeball. The potential between the front and back of the
eyeball is about 0.4-1.0 mV. By placing Ag/AgCl electrodes
around the eyes captures EOG signal and measures the
changes in potential as the cornea moves nearer or further
from the recording electrodes[3]. When the eye is looking
straight ahead, it is about the same distance from either
electrode, so the signal is essentially (zero + reference
voltage). When the front of the eyeball, i.e. the cornea, is
closer to the positive electrode, the corresponding electrode
records a positive difference in voltage[4].
Advances
in
detection
and
characterization
of
electroencephalographic signals from the brain have allowed
EEG monitoring to be useful in analysis of neurological
disorders. A headgear can be used to detect brain signals like
attention values.
This work helps them to control the movement of the
wheelchair by their eye movements and also by extracting
brain signals with the help of headgear[5].It integrates two
methods which provides a helping hand to the physically
challenged people.
The person suffering from extremely limited peripheral
mobility conditions for e.g. paraplegia (caused by spinal cord
injury) or quadriplegia (caused by illness or injury that results
in the partial or total loss of use of all four limbs) have the
ability to coordinate eye movements. So this work describes
the development of EOG signal along with extracting brain
signals with the help of headgear to control the movement of
their wheelchair.
Section II discusses the materials used for this work, section
III presents the proposed work, section IV describes the results
obtained in this work. Section V provides conclusion for the
whole work.
II. COMPONENTS REQUIRED
IC HA17324A (Instrumentation Amplifier), IC LM358P
(High pass filter and low pass filter), Pre-gelled Ag/AgCl
electrodes, Neurosky Mindwave headgear, bluetooth module
HC-05, L298(Motor Driver), 100 RPM 12V DC motor,
Arduino UNO microcontroller, two Renesas microcontrollers,
MAX232, Zigbee, 12V DC battery, Snap electrode wires,
wheels for wheelchair and passive components (e.g. capacitors
and resistors) are used in this work.
III. PROPOSED METHOD
A. EOG signal acquisition system
Disposable pre-gelled three Ag/AgCl electrodes will be used
to acquire vertical EOG signals by the movement of user’s
eyes. EOG signals acquired by up and down eye movements
were amplified using instrumentation amplifier, HA17324.
Followed by amplification, EOG signals are filtered to remove
unwanted signals. EOG signals are classified based on the
amplitude and time period. It is powered by 12V DC battery.
The brain signals used are Spontaneous EEG signals.
B. EEG signal acquisition system
The brain signals are acquired by Neurosky Mindwave
headgear. The headgear is switched ON. Fig.3 shows the flow
in which the headgear controls the wheelchair. There are
certain values that can be detected by the headgear for the
functions performed by the brain such as attention and
meditation. When such a function is performed, the headgear
acquires the EEG signals and transmits the digitized values to
an Arduino microcontroller via Bluetooth.
The obtained EOG and EEG signals are processed to generate
a control signal. Thus the microcontroller activates the motors
of the wheelchair which are used to rotate the wheels either
clockwise or anticlockwise.
C. Transmitting section
Two Renesas microcontrollers (R5F102AA) are used at the
transmitter and on the receiver section. Data acquired from
both the methods will be processed by the microcontroller
which makes final decision. The EOG and brain signals will
be passed to Renesas microcontroller which is then
transmitted through Zig-bee in the transmitter section[6]. Fig.1
and Fig.2 represents circuit diagram and block diagram
respectively. Instrumentation amplifier is designed using
HA17324A, and high and low pass filters are designed using
LM358P.
Fig.2 Block Diagram
D. Receiving section
The integrated signal will be received through Zig-bee in the
receiver section where it is then passed to Renesas
microcontroller which again is used to drive gear motors to
control direction of wheel chair. Table I shows the values for
the movement of the wheelchair either towards left, right,
forward or backward. When the user wants to go front, both
the wheels have to rotate in clockwise direction and
anticlockwise for backward movement. For the right
movement of the wheelchair, the right wheel has to rotate in
anticlockwise direction and the left wheel has to move forward
that is, rotate in the clockwise direction. For the left movement
of the wheelchair, the left wheel has to rotate in anticlockwise
direction and the right wheel has to rotate in clockwise
direction.
TABLE I: logic for programming for movement of the wheelchair either
towards left, right, front or backward
Fig.1 Circuit Diagram
,
Fig.4 Testing of Electrodes
Table II represents the output generated by the eye movements
and brain signals.
Table II: EOG and EEG output
Fig.3 Flowchart of EEG signal acquisition
IV. RESULT
A. Electro-oculogram output
Fig.1 shows the circuit diagram of the EOG signal extraction
and signal processing. Firstly the EOG signals extracted using
Ag/AgCl electrodes is fed to an instrumentation amplifier. It
provides high CMRR (around 100 dB) and can handle signals
in microvolt range. Differential amplifier in the
instrumentation amplifier allows subtraction of the common or
unwanted signal from the actual signal. High common mode
rejection ratio indicates better subtraction and it has a gain of
146.88. Since EOG signal has a frequency range between 0.140Hz, so the filter circuit consists of a second order high pass
filter followed by first and second order low pass filter. A
butterworth second order high pass filter consists of capacitor
in series which is used as an input to block the input's DC
offset. However, for this circuit that would be a bad idea since
the opamp's non-inverting input bias current (which is very
low but non-zero) would have nowhere to flow except into the
capacitor. To avoid this we need to add a resistor from the
non-inverting input to ground. This second order high pass
filter has a gain of 2.09 and a cutoff frequency of =0.156 Hz.
The output of this is fed to a first order butterworth low pass
=53.07
filter. This has a gain of 2 and a cutoff frequency of
Hz. The output of this is fed to a second order butterworth low
pass filter. This has a gain of 2 and a cut-off frequency
= 40 Hz. A total gain of 1227.97 is obtained and a
of
frequency range of 0.156 Hz to 40Hz is obtained.
Eye movement
Output
Wheelchair
movement
Up
0.58V to 0.65V
Front
Down
0.50V to 0.56V
Right
Attention values of
headgear
>= 50
Left
Fig.5. For downward movement
C. Control Signal generation
The brain signals (after programming using Arduino) and eye
signals are integrated using Renesas microcontroller. A
control signal is generated by the microcontroller. Zigbee is
used for transmission. The receiver Renesas microcontroller
controls the movement of the wheelchair. DC motors are
connected to the base of the prototype wheelchair which drive
the wheels either clockwise or anticlockwise, based on the
user’s commands. The wheelchair moves forward if the
patient looks up, it moves towards left if the patient looks
towards down, and the headgear is programmed so that the
wheelchair moves towards right and left if the patient is
concentrating (attention value>50).
Fig.6: For upward movement
Fig.5 and Fig.6 shows the output obtained for upward and
downward movement of the eyes.
B. Electroencephalography output
The headgear can be programmed using two values obtained
as the headgear output: attention and meditation. For this
application only attention value is considered. If value of
attention is between 50 to 100, then the person being tested is
concentrating. The headgear is programmed using Arduino
microcontroller considering this parameter. The following
were some of the readings obtained based on which the
microcontroller was programmed (Fig 7 ).
Attention
Attention value
100
80
V. CONCLUSION
In this paper the method to acquire the signals from the
movement of the eyes and signals from the brain has been
presented. The system captures and analyzes the EOG signals
and EEG signals which enable handicapped people to control
the wheelchair all by themselves. This system may serve as an
assistive technique for people who suffer from extreme
peripheral mobility. The system is highly accurate as it
integrates two methods, by using headgear as a verification
technique.
The system has to be improved for future use. In this device,
the headgear needs to be re-programmed for different people
as each person has a separate attention or meditation
capability. Permanent electrodes can be designed and used
instead of the disposable electrodes. Further research can be
done on the usage of the headgear and the electrodes. This
concept could be extended to obtain horizontal movement of
eyes and programming the wheelchair accordingly. In the
future, further work needs to be done on controlling speed of
the wheelchair.
60
Attention
40
ACKNOWLEDGMENT
Series2
20
0
1
2
3
4
The authors would like to express their gratitude for the
logistical support provided by M.S.Ramaiah Institute of
Technology, Bangalore, during the completion of the study.
Time(seconds)--->
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Fig.7. Attention
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