1. The document describes a human-machine interface system that uses electrooculography (EOG) signals measured from eye movements to control assistive devices. Electrodes placed around the eyes measure the small voltage changes corresponding to eye movements. 2. The EOG signals are amplified, filtered, and digitized before being fed into a microcontroller. Pattern recognition algorithms in the microcontroller are used to classify the eye movement and control eight different assistive devices. 3. The system provides an effective and low-cost means for individuals with disabilities to control devices through natural eye movements. Evaluation results demonstrated the system's ability to accurately detect different eye positions and directions of movement.

1. http://www.iaeme.com/IJMET/index.asp 53 editor@iaeme.com
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 02, February 2019, pp. 53-58, Article ID: IJMET_10_02_007
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=2
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
HUMAN-MACHINE INTERFACE OF
ELECTROOCULOGRAM BASED SYSTEM FOR
MOBILITY USING ASSISTIVE ROBOTICS
Mir Faisal Ahmed, Mrinmoy Kanti Deb
School of Electronics Engineering, Vellore Institute of Technology, Vellore-632014, India
Vinita Kumari
School of BioSciences and Technology, Vellore Institute of Technology, Vellore-632014,
India
J.Karthikeyan
School of Social Sciences and Languages, Vellore Institute of Technology, Vellore-632014,
India
ABSTRACT
The following empirical research undertakes the implementation of the signals
obtained via an Electrooculography (EOG) into the design for a simple, effective and
low-cost microcontroller system. The process was extended research, to demonstrate
and allow an incapacitated individual to control eight individual devices, by collecting
and evaluating the myoelectric signals rendered by the eye muscles during various
movements of the eye.
Keywords: Electrooculography, human-machine interface, motion artifact
Cite this Article: Mir Faisal Ahmed, Mrinmoy Kanti Deb, Vinita Kumari and
J.Karthikeyan, Human-Machine Interface of Electrooculogram Based System for
Mobility using Assistive Robotics, International Journal of Mechanical Engineering
and Technology, 10(2), 2019, pp. 53-58.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=2
1. INTRODUCTION
‘Electrooculography’ quantifies the resting potential of the retina. This is achieved by an
“Electro-oculography", a device that computes the voltage difference of the two electrodes
placed strategically on to the subject's face, to recognize the motion of the eye. In the current
scenario of technological advancement, where every field is now being digitized and
computer applications being extended to every area, human-machine interface has been
simplified extensively (more so, with the development of touch screen, digitizers, etc).
Assistive robotics appeals to the physically challenged individuals, as it bridges the gap
between the user interfaces, allowing for the establishment of communication with the device.

2. Human-Machine Interface of Electrooculogram Based System for Mobility using Assistive
Robotics
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These days, there are many system frameworks to control and guide autonomous robots.
There has been a steady increase in assistive robotics over the past few years, with the
development of Video-oculography (VOG), Infrared-oculography (IROG), Virtual reality-
based eye tracking, etc, which are based on eye position detection using a camera, other
advancements involve audio signal processing (voice and speech recognition), accessory
based control (ultrasonic sensor, joystick modules, etc.) while each system is designed
according to the extent of the person's degree of disability.
Over the years, biomechanics and neurophysiology have been applied in myology. This
field has been researched extensively, and from the data obtained from surface
Electromyography (EMG) signals, The EMG signals corresponding to the electrical impulses
generated by a muscle during contraction. These movements have been documented, and its
features have been abstracted for motion classification and detection and analysis of
movement patterns. Surface EMG signals exhibit complex behavior and have an invariant
sequence pattern, which is a direct expression of muscle activity. The following system design
focuses on the collection of EMG signals from the muscles involved in the control and
movement of the eye, which is then used as a feedback signal to control the systems
framework. Earlier Human-Machine interface (HMI) devices based on bio-signal feedbacks
have been employed using electromyography (EMG), electrooculography (EOG) and
electroencephalography (EEG) [1,2]. HMIs developed from these physiological signals are
applied and incorporated in Hands-free control of electronic devices. . With the recent
advancement of machine and computing ability of systems, multi-modality phenomena,
computer vision information, optical isolation, speech and voice modulation techniques have
been employed. Furthermore, bimodal control involving the integration of multiple
disciplinaries (such as computer vision & vision control, EMG & EEG signal incorporation,
etc.) was established [3, 7].
2. LITERATURE REVIEW
The Electrooculogram (EOG) signal is obtained from the polarization potential: “Corneal-
Retinal Potential” (CRP), which is generated in the interior complexion of the eye, as a result
of the metabolic activity of the retinal epithelium [4]. CRP is a consequence of successive
hyper-polarizations and de-polarisations of the nerve cells in the retina. The Electrooculogram
signal is obtained via a dual transport system, which comprises of a bi-channel signal
acquisition pathway (Horizontal and Vertical channels). The electrodes set up to receive the
electric potentials, and are placed on alternative ends (Lateral/Vertical placement), to collect
the possibilities rendered by the displacement of the eyeballs. The subsequently obtained
potential varies proportionally as a result of the movement of the eyeball in the conducive
environment of the suspension. Saccadic responses and the blinking motion of the eyelids are
natural motions, which produce a disruption of the electric impulse, resulting in the formation
of potential as a result of the fluctuation. This is also picked up by the EOG as a form of a
signal. For the experiment’s valuation, the parameters of the algorithm are fixed to values
common for all participant [5].
3. ELECTRO-OCULOGRAPH
The cornea of the human eye sustains a relatively positive charge in comparison to the
posterior end of the eye. This observed potential is unaffected with variation in light intensity
and is declared to be a resting potential. This potential is variable and therefore forms the
basis for Electrooculography. The source exhibits itself, as a mono-dipole, orienting from the
retina to the cornea (corneal-retinal potential: 0.4-1.0mV). As a result of the existence of a
pre-existing charge by the monopole, locomotion of the eyeball creates a dipole potential, of

3. Mir Faisal Ahmed, Mrinmoy Kanti Deb, Vinita Kumari and J.Karthikeyan
http://www.iaeme.com/IJMET/index.asp 55 editor@iaeme.com
which the signal is a measure off. The four basic eye movements: vergence, smooth pursuit,
saccades, and vestibular-ocular changes are generated as a consequence of the evolution of
the extraocular muscles [6]. Figure 1, depicts the potential obtained during the lateral eye
movement, which is collected by the placement of two electrodes on either side of the
temporal lobe. At rest, the eyes are at the same potential; therefore the potential existing as a
relevant product of the two is null. When the eye is displaced to the right, a varying potential
is obtained as the direction of the eye shifts from its origin, i.e. the electrode on the outer
surface of the right canthus, develops a positive charge in relation to the other electrode, and
the inverse occurs when the eye pivots to the left [8]. The device is calibrated by having the
subject consecutively gaze to the opposite extremes of the canthi, and recording the ensuant
readings of the EOG.
Figure 1 Electro-oculogram (EOG) signal generated during lateral orientation. Positive polarity
obtained at the electrode coinciding with the location of the eye. Achievable accuracy: ±2°; Maximum
rotation angle: ±65°; Signal magnitudes range from 6-19 µV
4. PROPOSED METHODOLOGY
To read the signals, and compute the voltage difference between the electrodes, we use an
instrumentation amplifier into our circuit. The instrumentation amp consists of voltage
followers, a non-inverting amp, and a differential amp. The integration of all resistors in
between each stage helps with tolerance errors: usually, resistors have 5-10% tolerance in
values, and the regular circuit would heavily rely on accuracy for good CMMR. The voltage
followers are for high input impedance. A non-inverting amplifier ensures high gain of the
signal and the differential amp takes the difference between the inputs, obtained from the
electrodes. These are designed to reduce as much common mode noise/interference as
possible Analog-to-Digital converters digitize the analog values for storing and analysis by a
computer system. Most ADC’s are unable to detect low voltage signals generated from the
Electrodes, and hence require the assistance of an amplifier. For instance, the project uses an

4. Human-Machine Interface of Electrooculogram Based System for Mobility using Assistive
Robotics
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Arduino UNO as an ADC, and is specifically a 10-bit ADC with 5V output, meaning that it
maps 0-5V input voltages to integer values between 0 and 1023. 10mV is the current reading
obtained from the electrodes, which is too small for the Arduino to analyze and configure;
therefore, we amplify the signal before providing the input to the Arduino. Noise and
interference, are created from random artifacts that disrupt the signals, producing an irregular
signal wave instead of a smooth signal. The most common noise interference is the 50-60Hz
powerline interference from the AC electromagnetic fields of power outlets. Power lines carry
AC high voltage from electrical generators to residential areas, where transformers step down
the voltage to the standard ~220V. The alternating voltage leads to the addition of a 60Hz
frequency with the original signal, which interferes with the signal acquisition. Eliminating
the 60Hz frequency was achieved via a low pass filter was designed to remove the
interference signals, since the bandwidth of the EOG obtained, was in the range of 0-10mV.
This filters out all the signals above 10Hz. 60Hz can corrupt our signals via capacitive
coupling and inductive coupling. Capacitive coupling occurs when air acts as a dielectric for
AC signals to be conducted between adjacent circuits. The coupling serves as an intermediate
for the AC signals while barricading the DC energy.
Figure 2 Eye position Electrogram signals, demonstrated using LED sequence (Number of variations
obtained: 8).
5. MATHEMATICAL MODEL
i. The system, consists of three electrodes, one attached to the left temple, another
connected onto the right temple, and the third electrode is grounded to the forehead.
This grounding stabilizes the signal, so there's less drift, and it also gets rid of some of
the 60Hz interference.

5. Mir Faisal Ahmed, Mrinmoy Kanti Deb, Vinita Kumari and J.Karthikeyan
http://www.iaeme.com/IJMET/index.asp 57 editor@iaeme.com
ii. The electrodes are then connected to an instrumentation amplifier, to generate the
voltage difference between. The Gain is calculated using the formula:
𝐺 = 1 + (
50𝐾𝑜ℎ𝑚
𝑅 𝑔
)
iii. Where Rg, is set to 100ohm to obtain a gain of 500
iv. After the amplification by the instrumentation amplifier, with a gain of the 500x
amplified voltage difference, there's a first-order RC low pass filter, which consists of
a resistor R_filter and capacitor C_filter. The low pass filter prevents anti-aliasing (by
Nyquist equation (𝑓𝑠 ≥ 2𝜔), it is required to sample at least 20Hz for an expected
10Hz bandwidth) and also cuts out all the unnecessary frequencies The RC system
works because capacitors allow high frequencies through easily but obstruct lower
frequencies and creating a voltage divider with the voltage across the capacitor results
in a filter that only allows lower frequencies through.
𝑖𝑚𝑝𝑒𝑑𝑎𝑛𝑐𝑒 (𝑍) =
1
(2 ∗ 𝜋 ∗ 𝑓)
v. The formula governs the cut-off for 3dB intensity:
𝑓𝑐 =
1
2 ∗ 𝜋 ∗ 𝑅 𝑐
vi. The Resistor(R) and Capacitor(C) values are set to cut off signals higher than ~10Hz
because the biological signal for EOGs is expected in that range.
vii. With the obtained filtered signal, the output is measured with an oscilloscope to see
the range of values from looking left and right (the two extremities), which were of
range 2-4V (instrumentation amp gain = 500x of ~4-8mV), when my target is 5V (full
range of the Arduino ADC). The non-inverting amp was adjusted to have a gain of
about 1.3 by changing R1 and R2 in the circuit.
𝑇ℎ𝑒 𝑔𝑎𝑖𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑎𝑚𝑝 = 1 +
𝑅2
𝑅1
viii. This signal is now supplied to the Arduino, which reads the values, and is then
calibrated initially, by recording the values obtained from the oscilloscope, and
programming the Arduino, to the corresponding voltage signals as shown in Figure 2.
Figure 3 Circuit Diagram for EOG System

6. Human-Machine Interface of Electrooculogram Based System for Mobility using Assistive
Robotics
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6. RESULT & CONCLUSION
Human-Machine Interfacing has developed considerably over the years, providing efficient
control capabilities to disabled individuals. Electrooculography is usually applied in research
and laboratory experiments but has shown many advantages in areas of sensitivity, accuracy,
and precision in determining the relative displacement/position of the eye. The procedure
allows for the recording of the data with minimal invasion and with minimal interference
obtained due to the subject's movement (motion artifact). While efficient in eliminating the
motion artifact signals, additional inconveniences arise alongside during the procuring of the
data: the muscle artifact is considerably difficult to be nullified adding on to the non-linearity
factor of the method.
One of the principal disadvantages is the fact that the corneo-retinal potential is a variable
quantity that varies diurnally, and therefore requires frequent calibration and recalibration.
EOG recording is a routinely applied diagnostic method, which provides an understanding of
the human oculomotor system. This has been simplified with the implementation of
microprocessors and computer algorithms, which has considerably increased the diagnostic
ability of the method [9].
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