EOG based wheelchair control for quadriplegics

Kumar, Satish; Vinod, Vibin Mammen

doi:10.1109/iciiecs.2015.7193165

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…has been developed to assist individuals with locked-in state symptoms. These include robotic mobility aids, commonly known as robotic wheelchairs [9]- [11], [21]- [25], speech recognition system [26]- [28], advanced robotic controllers [29], [30] and indoor position tracking systems [31]. Additionally, eye-indication systems [32], eye-writing system [33], hospital alarm system [34], virtual keyboard interfaces [6], [35], [36], and enhanced security systems [6] further exemplify the breadth of these rehabilitative advancements.…”

Section: Related Workmentioning

confidence: 99%

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Kosnacova,

Horvath,

Stremy

et al. 2024

IEEE Access

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Millions of individuals worldwide suffer from a motor disability, impeding their ability to perform daily tasks independently. Modern technologies, such as electrooculography (EOG), offer tools to enhance their quality of life. EOG, an electrophysiological method, detects DC potentials influenced by eye movements, making it particularly advantageous for individuals with motor impairments who retain ocular mobility. This article describes pilot experiments that includes the basic design of an electrodes configuration for peripheral solutions, their optimization, and the addition of sensors to measure other vital physiological parameters such as photoplethysmography (PPG) and electrocardiography (ECG) on demand. We tested all the EOG methodologies and connections that our device should contain, using the Mindmedia Nexus-10 MKII for EOG, the Analog Devices MAXM86146 optical biosensor module for PPG signals, the Texas Instruments ADS1299 biopotential circuit for ECG. The evaluation and filtering of the collected data were carried out in the MATLAB environment. Finally, we present a hardware concept based on a new approach that, unlike standard EOG, incorporates three large-area conductive fabric electrodes in a headband for practical and comfortable long-term non-manual human-machine interface (HMI). Our measurements are crucial for monitoring the patients' health status, mood variations, and can help prevent complications or speed up treatment. The primary goal of this research was to present our innovative concept and confirm its feasibility, bringing us closer to the realization of a fully developed and functional device.

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Section: Related Workmentioning

confidence: 99%

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Kosnacova,

Horvath,

Stremy

et al. 2024

IEEE Access

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show abstract

“…The reason for this is due to their availability and ease of acquisition. Some of the bio-signals that are typically used as an interface between machines and humans include the Electrooculogram (EOG) [13]- [16] Electroencephalogram (EEG) [5], [17]- [19] and the electromyogram (EMG) signal [18], [20]- [24]. Electrooculography (EOG) is a method of measuring the eyes' resting potential.…”

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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Brain–computer interface for amyotrophic lateral sclerosis patients using deep learning network

Ramakrishnan

Mavaluru

Ramkumar

et al. 2020

Neural Comput & Applic

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EOG based wheelchair control for quadriplegics

Cited by 8 publications

References 8 publications

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

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

Brain–computer interface for amyotrophic lateral sclerosis patients using deep learning network

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