Eye Gaze Controlled Wheelchair

B, Anagha Dwajan; Sowmya, M; S, Bhavani; N, Priya Reddy; Usha, M.

doi:10.17577/ijertv9is050476

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The corresponding control commands were sent when the subject blinked in time with the button flash to choose the desired button. Other than the threshold mechanism that has been used in some studies to distinguish different eye movements [34]- [38], [40], [41], machine learning and deep learning algorithms have also been embraced for the same [42]- [45]. Machine learning has different algorithms that are used for classification.…”

Section: Saakshi and Manojmentioning

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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Section: Saakshi and Manojmentioning

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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“…The gaze ratio is the ratio of the white pixels between each side in both eyes. This ratio allows determining the area at which the person is looking [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Wheelchair Control System based Eye Gaze

Amer¹,

Ramadan²,

Kamh³

et al. 2021

IJACSA

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The inability to control the limbs is the main reason that affects the daily activities of the disabled which causes social restrictions and isolation. More studies were performed to help disabilities for easy communication with the outside world and others. Various techniques are designed to help the disabled in carrying out daily activities easily. Among these technologies is the Smart Wheelchair. This research aims to develop a smart eye-controlled wheelchair whose movement depends on eye movement tracking. The proposed Wheelchair is simple in design and easy to use with low cost compared with previous Wheelchairs. The eye movement was detected through a camera fixed on the chair. The user's gaze direction is obtained from the captured image after some processing and analysis. The order is sent to the Arduino Uno board which controls the wheelchair movement. The Wheelchair performance was checked using different volunteers and its accuracy reached 94.4% with a very short response time compared with the other existing chairs.

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Eye Gaze Controlled Wheelchair

Cited by 2 publications

References 6 publications

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

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

Wheelchair Control System based Eye Gaze

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