Controlling a Wheelchair by use of EOG Signal (original) (raw)
Related papers
An EOG-Based Human-Machine Interface for Wheelchair Control
IEEE transactions on bio-medical engineering, 2017
Non-manual human-machine interfaces (HMIs) have been studied for wheelchair control with the aim of helping severely paralyzed individuals regain some mobility. The challenge is to rapidly, accurately and sufficiently produce control commands, such as left and right turns, forward and backward motions, acceleration, deceleration, and stopping. In this paper, a novel electrooculogram (EOG)-based HMI is proposed for wheelchair control. Thirteen flashing buttons are presented in the graphical user interface (GUI), and each of the buttons corresponds to a command. These buttons flash on a one-by-one manner in a pre-defined sequence. The user can select a button by blinking in sync with its flashes. The algorithm detects the eye blinks from a channel of vertical EOG data and determines the user's target button based on the synchronization between the detected blinks and the button's flashes. For healthy subjects/patients with spinal cord injuries (SCIs), the proposed HMI achieved...
Analysis of different level of EOG signal from eye movement for wheelchair control
2013
This paper is aimed to analyze different levels of eye movement signals strength using Electrooculography (EOG). The eye movement that is known to be a significant communication tool for a tetraplegia, can be defined as a paralysis that is caused by serious injuries or illness to a human that lead to a partial or total loss of their lower limb and torso. A person who has such paralysis is highly dependent on an assistant and a wheelchair for movement. It is not always the case where the helper is with the patient all the time, therefore independence is encouraged among the wheelchair users. The signal from the eye muscles that is called electrooculogram is generated at different eye movements' directions and levels. The eye movement signals are acquired using g.USBamp from G.TEC Medical Engineering GMBH by using Ag/AgCl electrodes. The data is then passed to MATLAB/SIMULINK software for data analysis. Different directions and strength level of eye movement are fed to a virtual wheelchair model developed in MSC.Visual Nastran 4D software to study the effect of the signals on the distance and rotation travelled by the wheelchair. Simulation exercises has verified that different strength of eye movement signals levels that have been processed could be manipulated for helping tetraplegia in their mobility using the wheelchair.
International Journal of Computer Applications
The use of vital signals as a connection interface between humans and computers has recently attracted a great deal of attention. The electro-oculogram (EOG) signal, which is due to eye potential, is one of these signals. More advanced, EOGbased Human-Machine Interfaces (HMIs) are widely investigated and considered to be a noble interface option for disabled people. Artificial neural networks were utilized in this study to detect eye movement from the EOG signal. Neural networks can detect and classify biological signals with nonlinear dynamics, including EOG signals, due to their ability to learn nonlinear dynamics and their pervasive approximation. In this study, two fundamentally distinct networks, MLP and ART, were used to detect sequential and random eye movements for controlling wheelchair. The results indicate that the MLP network could indeed detect consecutive eye movements with an accuracy of over 90%, although the accuracy of this network detection in the case of random movements is relatively poor. In the field of random eye movements, the greatest results are obtained using the ART2AE network, which allows having a diagnostic accuracy of over 70%.
Wheelchair Motion Control Guide Using Eye Movement Based on EEG Signals.
International Journal of Engineering Sciences & Research Technology, 2013
The design and implementation of an Autonomous Movement Robot based on a Wheelchair based on EOG signal is to help a disable or handicapped person. These EOG electrodes are placed at right and left of eye and other pair of electrodes are at top and right of the eye. These electrodes used to response after gazing of one target point for a particular time period. After gazing of point, the wheelchair used to move to a target position. So, it produce delay during eye gaze. To overcome this delay, EEG amplifier are used. These EEG signals are placed to capture brain waves. These brain waves are controlled by microcontroller and it produce analog waves. To convert analog waves to digital output Analog to digital converter is used. Object Sensor is used to avoid obstacles in its path respectively. The main contribution of the work is the combination of several technologies and techniques that came from different areas such as mechanical, electronic engineering. Driver circuit with relay are used to move wheelchair automatically. ZigBee is used for long transmission of wheelchair. Accelerometer and interfacing circuit are done by using head movement. DLOA algorithm used to avoid obstacles while reaching destination point. The target coordinates of the destination place using EEG, to reduce delay for auto navigation process.
Design of an Autonomous Mobile Wheel Chair for Disabled Using Electrooculogram (EOG) Signals
This paper discusses the implementation of a simple, effective and low cost design of a microcontroller based wheelchair using the EOG signal collected from muscles those are responsible for the movement of the human eye. This was an exploratory research that was carried out to allow a disabled person to control the wheelchair by using only the movement of his eyes. Electrooculography (EOG) is a technique for sensing and recording the activation signal of the muscles and was used to collect and evaluate the myoelectric signal generated by the eye muscles during different movements. The main purpose of the work is to design a cost-effective, easily affordable and accessible wheel chair for the disabled general masses where advanced attachments like on board computer, digital cameras, sophisticated sensors etc. are not being used, rather concentration has been paid on designing a more simple, practical but effective system using an electronically controlled differential drive structure with only two wheels. A low cost microcontroller (ATMEGA 32) serves as the brain of the system for all types of control purposes.
A EOG-based switch and its application for “start/stop” control of a wheelchair
Neurocomputing, 2018
Biological signals, including electroencephalography (EEG) and electrooculography (EOG), are often used to develop switches, which represent a class of typical asynchronous human-computer interfaces (HCIs) in which control and idle states need to be distinguished based on a criterion. Determining a satisfactory criterion for rapid and accurate discrimination between control and idle states remains a challenging issue, as EEG signals are highly noisy and nonstationary, and EOG signals are highly affected by unintended/spontaneous eye movements. Therefore, most existing EEG-or EOG-based switches are characterized by disadvantages of long response times (RTs) or high false positive rates (FPRs). The primary contribution of this work is the development of a novel EOG-based switch design, in which a visual trigger mechanism is introduced to guide the users' blinks and to assist in detecting blinks. Specifically, the graphical user interface (GUI) includes a switch button that flashes once per 1.2 s. The user is instructed to blink synchronously with the flashes of the switch button to issue an on/off command while a single-channel EOG signal is collected. A waveform detection algorithm is applied to the ongoing EOG
GUIDANCE FOR WHEELCHAIR USING ELECTRO -OCULOGRAM (EOG
IRJMETS Publication, 2021
In this paper, we propose a new method to move the wheelchair by using electro-oculogram. As mobility is valued immensely by people therefore the main aim of our project is to help paralytic subjects and wheelchair users with difficulty in movements to move freely without the assistance of others. Paralysis causes the paralytic subjects to depend on someone for even basic movements. It may lead to depression and other psychological disorders and can even cause lower self-esteem. There are traditional methods for assistance in mobility like conventional wheelchairs which require manual force by hand to move the wheelchair thus causing difficulties but using EOG controlled wheelchair reduces the need of assistance from another person. EOG is a method through which the electrical potential of the eye can be sensed by eyeball movements. This project uses the EOG output, tracking the eyeball movement and then sending the signals to Arduino board in wheelchair. The computed information determines the movement of the wheelchair. And an IBEACON BLE Bluetooth tracking system is also enabled for the caretaker to track the subject's location.
Implementation of Wheelchair Controller using Eyeball Movement for Paralytic People.
International Journal of Engineering Sciences & Research Technology, 2013
This paper delivers a new method to guide and control the wheelchair for eyeball movement. In this method we use sensor based eyeball tracking system to control powered wheelchair. Eyeball sensor will generate distinct range of values for each position of eyeball (i.e. left, right, straight). Thi concept can be used for multiple applications, but this paper focuses the application to mobile and communication aid for paralytic people. The proposed system involves two stages; first eyeball tracking and second sending of control signals to the arduino controlled wheelchair. Abstract This paper delivers a new method to guide and control the wheelchair for disabled people based on their eyeball movement. In this method we use sensor based eyeball tracking system to control powered wheelchair. Eyeball sensor will generate distinct range of values for each position of eyeball (i.e. left, right, straight). Thi concept can be used for multiple applications, but this paper focuses the application to mobile and communication aid for paralytic people. The proposed system involves two stages; first eyeball tracking and second sending of no controlled wheelchair.
EMG-based hands-free wheelchair control with EOG attention shift detection
… and Biomimetics, 2007. …, 2007
This paper presents a novel hands-free control system for an electric-powered wheelchair, which is based on EMG (Electromyography) signals recorded from eyebrow muscle activity. By using a simple CyberLink [1] device, one-dimensional continuous EMG signals are obtained, analysed, and then translated into multi-directional control commands (forward, left, right, etc.) for the wheelchair that supports multi-directional control. At the same time, EOG (Electrooculography) signals detected from eye movements are used to adjust wheelchair speed. The system also allows a user to choose either control state or non-control state so that any non-intended muscle activity can be ignored during the non-control state. The system is reliable, easy to set up, and easy to use.
Analysis of different EOG-based eye movement strength levels for wheelchair control
International Journal of Biomedical Engineering and Technology, 2013
This paper is aimed to analyse different levels of eye movement signals strength using Electrooculography (EOG). The eye movement is a potential communication tool for a tetraplegia, which is defined as a paralysis that is caused by serious injuries or illness to a human that lead to a partial or total loss of their lower limb and torso. The signal from the eye muscles that is called electrooculogram is generated at different eye movements' directions and levels using g.USBamp from G.TEC Medical Engineering by using Ag/AgCl electrodes. Different directions and strength levels of eye movement are fed to Matlab-Simulink environment that was integrated with a virtual wheelchair model to study the effect of the EOG signals on the distance and rotation travelled by the wheelchair. Simulation results show that the EOG signals with different strengths acquired can be used as a reference input for wheelchair control.