Analysis of different EOG-based eye movement strength levels for wheelchair control

Noor, Nurul Muthmainnah Mohd; Ahmad, Salmiah

doi:10.1504/ijbet.2013.055043

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…La señal EOG es adquirida utilizando electrodos superficiales desechables de Ag/AgCl, recurriendo a la derivación vertical [3], y preamplificada utilizando un INA128 con una ganancia de 150. Para obtener una señal libre de ruido requerido se utilizó un filtro activo pasa-bajo de orden 2 (UAF42) con una frecuencia de corte de 30 Hz, dado que la información requerida se encuentra en las bajas frecuencias [4]. Posteriormente, la señal es amplificada y colocada sobre un nivel de offset de 1.5 V con el objeto de ser enviada al microntrolador, encargado de digitalizar la señal EOG a través de su módulo conversor análogo-digital con el fin de determinar mediante un par de funciones el nivel de sueño de una persona según unos umbrales definidos empíricamente.…”

Section: Palabras Claves --Eog Amplificador De Instrumentación Somnunclassified

Design and implementation of sleep monitoring system using electrooculographs signals

Villamizar

Suárez

Villanueva

et al. 2014

2014 Pan American Health Care Exchanges (PAHCE)

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Recently the somnolence in the drivers has been one of the main causes of car accidents resulting in severe injuries, deaths and significant economic losses. The main purpose of this research is the development of a sleepiness monitoring device for drivers who works for long hours. The proposed monitoring system is composed for a conditioning and acquisition stage for electrooculographs signals (EOG) and control stage dedicated to send and activation alarm. The acquired signals were visualized in a user interface developed in Matlab which also make the signal processing to detect de retina rest potentials for posterior analysis. The EOG signals were reliable enough to establish the threshold levels for the activation of the alarm.En la actualidad, la somnolencia en los conductores ha sido una de las principales causas de lo accidentes de tránsito ocasionando lesiones físicas severas, muertes y pérdidas económicas significativas. El presente proyecto está enfocado al desarrollo de un dispositivo de monitoreo de sueño para conductores de automóviles, los cuales pueden presentar un estado de somnolencia cuando son sometidos a extensas horas de trabajo. El sistema de monitoreo propuesto está conformado por una etapa de adquisición y acondicionamiento de Señales Electrooculográficas (EOG), y una etapa de control, dedicada principalmente a la activación de una alerta. Las señales EOG fueron visualizadas en una interfaz de usuario, desarrollada en un lenguaje de alto nivel como Matlab, permitiendo el procesamiento digital de los potenciales de descanso de la retina para su posterior análisis. Se obtuvo una señal EOG confiable proveniente de la etapa de acondicionamiento, la cual permitió establecer los niveles de umbrales para la activación de la alarma.

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Section: Palabras Claves --Eog Amplificador De Instrumentación Somnunclassified

Design and implementation of sleep monitoring system using electrooculographs signals

Villamizar

Suárez

Villanueva

et al. 2014

2014 Pan American Health Care Exchanges (PAHCE)

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“…This has been achieved by [28]- [30], where different devices, such as lights, fans, etc., were switched on/off when the eye was moved in the right, left, upward, or downward direction. EOG signals have also been used for mouse control, thus aiding patients with computer use [15], [31], [32]. The participants used eye movement in different directions to perform and select an operation, while they used intentional blinking to navigate the application.…”

Section: Introductionmentioning

confidence: 99%

Development of a Virtual Environment-Based Electrooculogram Control System for Safe Electric Wheelchair Mobility for Individuals with Severe Physical Disabilities

Ogenga,

Njeri,

Muguro

2023

Journal of Robotics and Control

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Conventional wheelchairs are predominantly manual or joystick-operated electric wheelchairs. However, operating these wheelchairs can be difficult or impossible for individuals with severe physical disabilities. Due to losing control of their physical limbs, they depend on an attendant for assistance. As a remedy, bio-signals may be used as a control mechanism since they are readily available and can be acquired from any body part. This research proposes to use EOG signals to vail a control mechanism and test it in a virtual and actual electric wheelchair. The main contribution of the study is an investigation of the use of EOG to control an electric wheelchair in a virtual environment to determine safe control parameters for wheelchair use in complex environments. A customized data acquisition circuit was developed to acquire single-channel EOG signals using wet electrodes. The acquired signal was filtered and processed using feature extraction and classification techniques in MATLAB software. Two customized control environments were developed in Unity 3D, one with equally partitioned sections and the other with sections decreasing in size as the robot wheelchair approaches the target. Twenty-two test subjects (mean age 24.5, std 1.5) participated in the study, controlling the robot wheelchair in real-time with non or least instances of collision and oversteering. The system achieved an accuracy of 96.5% with a response time of 0.7s, translating to an ITR of 70.6 bits/min. Overall, the participants managed to navigate the virtual environment with a completion time of 101.94s ± 19.71 and 109.07s ± 13.25 for the male and female participants, respectively. In the scene with decreasing section sizes, 72% and 54% instances of collision and oversteering were reported, respectively, highlighting the need to consider the complexity of the control environment and the sufficiency of the participants' control skills to ensure safety in operations. The results confirm the usefulness of EOG as a control interface, with little or no need for recalibration. It provides a promising avenue for individuals with severe physical disabilities to operate wheelchairs independently in complex environments, enhancing their quality of life.

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“…Measurement of resting potential that is generated by electric dipole created with difference in potential across negative and positive cornea is called Electrooculography [12][13][14]. For the programming of third party applications such as wheel chair movement [11] these signals are essential but are considered as artifacts in EEG signals. [15,18].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Artifacts Detection Techniques for Electro-Encephalography (EEG) Signals

Roy¹,

Shukla²

2015

IJMUE

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The EEG signals are the prime sources to diagnose and manipulate Epilepsy, state of coma and numerous studies. The EEG signals in the active brains constitute various body activities controlled or out of human consciousness. There exist considerable researches that focus to minimize the artifact values in the EEG domain. This paper is the evaluation of detection methods to study their efficiency and constraints of experimental limitations.

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Analysis of different EOG-based eye movement strength levels for wheelchair control

Cited by 9 publications

References 8 publications

Design and implementation of sleep monitoring system using electrooculographs signals

Design and implementation of sleep monitoring system using electrooculographs signals

Development of a Virtual Environment-Based Electrooculogram Control System for Safe Electric Wheelchair Mobility for Individuals with Severe Physical Disabilities

A Survey on Artifacts Detection Techniques for Electro-Encephalography (EEG) Signals

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