A novel approach for Human Computer Interface based on eye movements for disabled people

In this article an electro-oculogram (EOG) based Human Computer Interface (HCI) will be presented, in order to control the mouse cursor on the screen of a computer or laptop. Electromyography (EMG) is the domain that employs the activation and deactivation (onset and cessation) of the muscles. EOG is the sub-domain of EMG field that focuses on the human eye’s movements. The EOG bio-signals can be recorded using Ag/AgCl electrodes coupled on the user’s skin and fed into a data acquisition device - an analog-to-digital converter (ADC) in order to be transmitted, filtered and processed on a computer or laptop. We acquired the EOG bio-signals with a 24-bit, 4 channel, 51200 samples/s per channel ADC, made by the National Instruments (N.I.), model NI-9234 industrial ADC, using only 3 recording channels and electrodes. After processing, the program running on the computer or laptop can be used to realize commands or control different applications according to the recorded bio-signals. In our case, this was done, using Artificial Neural Network (ANN) toolbox of MATLAB®. This HCI can be used by perfectly healthy or even by disabled people. In the case of disabled people, these systems can be used to control any electronic device connected to the computer or control the device itself. Applications of this type of HCIs can be Internet browsing, mail writing, word file editing, etc. This system is meant to offer a new way of computer control - other than the existing standard communication and/or control possibilities (like keyboard and/or mouse).

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Efficient feature for classification of eye movements using electrooculography signals

Thermal Science ◽

10.2298/tsci151005038p ◽

2016 ◽

Vol 20 (suppl. 2) ◽

pp. 563-572 ◽

Cited By ~ 3

Author(s):

Pornchai Phukpattaranont ◽

Siriwadee Aungsakul ◽

Angkoon Phinyomark ◽

Chusak Limsakul

Keyword(s):

Eye Movements ◽

Computer Interface ◽

Human Computer Interface ◽

Scatter Plot ◽

Mean Values ◽

Maximum Peak ◽

The Mean ◽

The Difference ◽

Feature Values

Electrooculography (EOG) signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal signals has been presented and evaluated. There are the maximum peak and valley amplitude values, the maximum peak and valley position values, and slope, which are derived from both vertical and horizontal signals. In the experiments, EOG signals obtained from five healthy subjects with ten directional eye movements were employed: up, down, right, left, up-right, up-left, down-right down-left clockwise and counterclockwise. The mean feature values and their standard deviations have been reported. The difference between the mean values of the proposed feature from different eye movements can be clearly seen. Using the scatter plot, the differences in features can be also clearly observed. Results show that classification accuracy can approach 100% with a simple distinction feature rule. The proposed features can be useful for various advanced human-computer interface applications in future researches.

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Development of robust electrooculography (EOG)-based human-computer interface controlled by eight-directional eye movements

International Journal of the Physical Sciences ◽

10.5897/ijps11.1486 ◽

2012 ◽

Vol 7 (14) ◽

Author(s):

Siriwadee Aungsakun

Keyword(s):

Eye Movements ◽

Computer Interface ◽

Human Computer Interface

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High performance computation of human computer interface for neurodegenerative individuals using eye movements and deep learning technique

The Journal of Supercomputing ◽

10.1007/s11227-021-03932-z ◽

2021 ◽

Author(s):

Jayabrabu Ramakrishnan ◽

Rajesh Doss ◽

Thangam Palaniswamy ◽

Raddad Faqihi ◽

Dowlath Fathima ◽

...

Keyword(s):

Deep Learning ◽

Eye Movements ◽

High Performance ◽

Computer Interface ◽

Human Computer Interface ◽

Learning Technique ◽

High Performance Computation

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Human Computer Interface for Wheelchair Movement

Baghdad Science Journal ◽

10.21123/bsj.14.2.437-447 ◽

2017 ◽

Vol 14 (2) ◽

pp. 437-447

Author(s):

Baghdad Science Journal

Keyword(s):

Real Time ◽

Physical Disability ◽

Pulse Width Modulation ◽

Fast Response ◽

Computer Interface ◽

Human Computer Interface ◽

Human Face ◽

Disabled People ◽

The Disabled ◽

Promising Solution

This paper aims to develop a technique for helping disabled people elderly with physical disability, such as those who are unable to move hands and cannot speak howover, by using a computer vision; real time video and interaction between human and computer where these combinations provide a promising solution to assist the disabled people. The main objective of the work is to design a project as a wheelchair which contains two wheel drives. This project is based on real time video for detecting and tracking human face. The proposed design is multi speed based on pulse width modulation(PWM), technique. This project is a fast response to detect and track face direction with four operations movement (left, right, forward and stop). These operations are based on a code written in MATLAB environment and Arduino IDE environment. The proposed system uses an ATmega328microcontroller (Arduino UNO board).

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