Moving Robotic hand with the help of EOG signals

ABSTRACT

In this project (HMI) Human Machine Interface has been studied extensively to control electromechanical rehabilitation aids using bios EEG, EOG, and EMG, etc.

The various bios EOG signals have been studied in-depth due to the occurrence of a definite signal pattern. Persons suffering from extremely limited peripheral mobility like paraplegia or quadriplegia usually have the ability to coordinate eye movements.

The current focus on the development of a prototype robotic hand controlled by EOG signals. EOG signals were used to generate control signals for the movement of the robotic arm. in this project we developed the EOG signal acquisition system.

The acquired EOG signal was then processed to generate various control signals depending upon the duration and amplitude of the signal component. These control signals were then used to control the movements of the prototype robotic hand model.

Introduction of Moving robotic hand with the help of  EOG signals

A disabled person always wishes to lead a life like a normal human being. The term disability broadly describes impairment in a person’s ability to function, caused by changes in the various subsystems of the body, or to mental health. The degree of disability may range from mild to moderate, severe, or profound. A person may also have multiple disabilities.

A physical impairment is any impairment. which limits the physical function of gross motor, limbs, or bones ability. To overcome a disability, assistive technology can be made use of. It in turn is a generic term used for devices and modifications for a person or a society. It promotes people to perform tasks. They are had greater difficulty accomplishing, by providing enhancement or by changing methods of interaction with the technology.

Scope of the Project

The purpose of Moving the robotic hand with the help of  EOG signals is to expend the scale by which anyone can easily buy or use it. And can utilize the EOG signals for the rehabilitation of their body parts.

It’s also a multi-purpose technology we can also analyze and measure the movement of our eye, so in the future, the scope of this technology will be very wast and worth full.

Working of Moving robotic hand with the help of  EOG signals

With the improvement of technology, there is a vast development in the field of rehabilitation techniques. Researches are going on to develop reliable, low cost and easy to use devices. HCI (Human-Computer Interface) and HMI (Human Machine Interface) are the latest and most effective techniques, Out of all the rehabilitation techniques. Researches in these fields are being 3 carried out extensively.

The main objective of the HMI system. the conversion of signals is controlled some generated by humans through control of some electromechanical devices. While in the HCI system some keystrokes or cursor movements on the screen are controlled by using these signals. In HCI and HMI both biosignals and non-bio signals are used as a medium of control. The basic biosignals used in the Interface are given. Electromyography (EMG), Electroencephalography (EEG) and Electrooculography (EOG). HMI is commonly used by motor-impaired patients to control a wheelchair. Rehabilitation devices are broadly classified into two categories; the first category includes all those devices which are biosignal and the second category includes non-bio signal based devices.

Non-bio signal rehabilitation aids provide 100% accuracy and require less training for patients. but the devices is the usage of these limited patients with partial or complete flexibility in their body parts. Bio signal based rehabilitation devices mainly use biosignals like EEG, EOG, or EMG as control signals. The advantage of using a biosignal approach is that when patients become completely paralyzed, the only resource available to them then is biosignals. However, it usually needs user training and has lesser accuracy than non-bios approaches. The biosignal approach usually requires user calibrations because biosignals produced by each individual are unique due to differences in individual physiological properties and skin conductance. 

Moving robotic hand use EOG based methods

Moving the robotic hand with the help of  EOG signals we use The Electrooculogram (EOG) is the electrical signal of the potential difference between the retina and the cornea of the eye. This difference is because of the fact that the occurrence of metabolic activities in the cornea region is higher than that in the retinal region. Usually, the cornea maintains a voltage of +0.40 to +1.0 millivolts which is higher than the retina. When the eyes are rolled upward or downward, positive or negative pulses are generated. The rolling angle increases, the amplitude of the pulse also increases and the width of the pulse is direct to the duration of the eyeball rolling process. The EOG is an electrical recording to the direction of the eye and makes the use of EOG for applications such as HCI very attractive. EOG-based techniques are very useful for patients with severe cerebral palsy or those born with a congenital brain disorder or those who have suffered severe brain trauma signals. 

Basically (EOG) is measuring the cornea retinal standing potential that exists between the front and the back of the human eye. The resulting signal is called the electrooculogram. EOG signals produce a potential difference between the movement of our eye movement. These signals will run the robotic arm with the help of eye movement.

The major components of this system are as follows 

1. Electrodes
2. Amplifier Circuit
3. Adriano UNO
4. Robotic hand

Electrodes

Electrodes and input cables Disposable pre-gelled Ag/AgCl electrodes were used to acquire EOG signals from the body. Since the EOG signal amplitude range was in microvolts, they were very much susceptible to various noise sources. To overcome the effects of RF noise and electromagnetic interference, shielded wires were used to connect Ag/AgCl electrodes and data acquisition circuits.

Amplifier Circuit

In general, the EOG signal amplitude and bandwidth vary in the range of 50-3500 µV USB-4704 is a 12-bit analog-to-digital converter. Hence, care had to be taken to design a biopotential which would amplify the signal in such a way that the digitization of the EOG signals results in minimum quantization error. To ascertain this, the gain of the biopotential amplifier should be adjusted so that 100 µV EOG signal is amplified above 1.4 mV. Also, the characteristics of AD620 the gain of a bio-potential amplifier is increased, there was a subsequent increase in the common-mode rejection ratio (CMRR). The facts into consideration, the biopotential amplifier was designed with a gain of 1500. Circuit diagram of the vertical EOG signal acquisition system. The same circuit can be used to acquire vertical EOG signals.

EOG classification algorithm

From the EOG signals obtained, which are mentioned, it is clear that each eye movement follows a unique pattern. There are different algorithms for classifying EOG signals. Among them, the most common algorithms compare the obtained EOG signal’s amplitude with a predefined threshold. When signal amplitude exceeds the threshold value, it is considered to be a valid signal for recognition. This method is very simple and easy to implement but is not without drawbacks. Generally, the EOG signal is very sensitive. It fluctuates with respect to the head movement or even with the slightest displacements in electrodeposition. So these algorithms produce erroneous outputs in these conditions. This can be overcome to an extent by a new algorithm, in which, along with the threshold the amplitude duration of the signal in which it keeps the amplitude above the threshold, was also calculated.

The flowchart of the detection algorithm, which describes the sequence of steps to identify horizontal EOG signals (right and left eye movements). The same algorithm was used to classify up and down movement, introducing a slight modification made to classify between up and down the action.

From EOG signal analysis done on voluntary candidates, it was found that the peak of amplified EOG outputs is around 0.5V for the right movement and -0.5V for left movement. It also keeps the amplitude high for duration 500ms to 900ms.

Flow chart for detecting right-left eye movements

Hardware list

ELECTRODES AMPLIFIER CIRCUIT ARDUINO UNO WIRES AND ICs ROBOTIC HAND

Application

• The project’s main application is through a computer interface that connects to the motion in the human body. This interface helps the user to signal through eye movement patterns to operate the robotic hand. And also rehabilitation of the human hand.

Advantages and disadvantages of EOG signal

EOG approach has both advantages and disadvantages when compared to other methods for determining eye movement.

Advantages of EOG signals

1. EOG based recording techniques are simple and cheaper than other methods and can be recorded with minimal discomfort.
2. EOG can be utilized as an influential visualizing parameter to assist the ophthalmologists to diagnose the ophthalmic syndromes in a better way.
3. EOG can be employed in modeling ophthalmic instruments that are capable of accompanying disease diagnosis as well as for therapeutic purposes.
4. In general, fully or partially differentially abled persons have a dominant vision which can be used as a residual influential tool in developing their rudimentary works through human-machine interfacing.

Disadvantages of EOG signal

1. Additional difficulties arise owing to muscle artifacts and the basic nonlinearity of the method.
2. EOG signals are subject-specific. Signal amplitude and duration vary from person to person.
3. EOG signal amplitude is of microvolt range and highly susceptible to noise.
4. EOG signals are very much sensitive and therefore fluctuate with head movements.

CONCLUSION

Moving the robotic hand with the help of  EOG signal use, an EOG signal acquisition system has been designed and implemented. Hence it is very much useful for the implementation of rehabilitation aids. As a part of this project, and EOG based Human Machine Interface has also been developed. This HMI was able to generate control signals with eye movements. These control signals were used to control a wireless prototype robotic hand model by various eye movements. This EOG based HMI control system for the robotic hand will be a good assistive technique for people suffering from extremely limited peripheral mobility. From the application point of view, control signals generated can be used to control HCI systems or other communication devices. As a whole, great prospects lie ahead for the current project which can be implemented with some further modifications.

The usage of this device is simple and will take only a short time to learn. We have implemented a microcontroller-based system instead of the conventional computer-based system. Our proposed system is capable of transferring data wirelessly and the proposed method can also be used by single eyed-individual. The device was successful in discriminating between the eye movements. Moving a robotic hand will help handicapped or disabled people with severe nervous system injuries and disorders to live more independently. It will definitely help them to a good extent to interact with their environment.

Moving robotic hand with the help of  EOG signals trying to realize a robotic servant for a paralyzed person. And the most important thing for a person with disabilities is to live like a normal man. To a certain extend. it can be accomplished by this project. More modifications can also be implemented by making use of different combinations of eye movements. Then it is possible to make the robots do some more operations. Further modifications in this will surely make some rapid growth in the field of robotics.