Motorized wheelchair control using electrooculogram and head gear

Bardhan, Jayetri; Suma, Pompeo; Jyothirmayi, M

doi:10.1109/inventive.2016.7830190

Cited by 6 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies investigated the control of motorized wheelchairs, the vast majority being on multimodal interfaces for their control: providing guidance with eye-tracking systems [ 25 ], electrooculogram (EOG) signals [ 26 , 27 ], head movements [ 28 ], applying voice-control commands [ 29 ], adopting autonomous traveling, and applying machine learning on the data obtained by the sensors integrated into the wheelchair [ 30 , 31 ]. These studies are mostly solutions for individuals with hand impairments, who do not possess the required capabilities to use the motorized wheelchair-integrated joystick, and needing additional technology to assist their maneuvering.…”

Section: Introductionmentioning

confidence: 99%

An Augmented Reality Serious Game for Learning Intelligent Wheelchair Control: Comparing Configuration and Tracking Methods

Maio

Marques²,

Alves³

et al. 2022

Sensors

View full text Add to dashboard Cite

This work proposes an augmented reality serious game (ARSG) for supporting individuals with motor disabilities while controlling robotic wheelchairs. A racing track was used as the game narrative; this included restriction areas, static and dynamic virtual objects, as well as obstacles and signs. To experience the game, a prior configuration of the environment, made through a smartphone or a computer, was required. Furthermore, a visualization tool was developed to exhibit user performance while using the ARSG. Two user studies were conducted with 10 and 20 participants, respectively, to compare (1) how different devices enable configuring the ARSG, and (2) different tracking capabilities, i.e., methods used to place virtual content on the real-world environment while the user interacts with the game and controls the wheelchair in the physical space: C1—motion tracking using cloud anchors; C2—offline motion tracking. Results suggest that configuring the environment with the computer is more efficient and accurate, in contrast to the smartphone, which is characterized as more engaging. In addition, condition C1 stood out as more accurate and robust, while condition C2 appeared to be easier to use.

show abstract

Section: Introductionmentioning

confidence: 99%

An Augmented Reality Serious Game for Learning Intelligent Wheelchair Control: Comparing Configuration and Tracking Methods

Maio

Marques²,

Alves³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, he can create other wheelchair or robotic arm commands using eye blinks or eyebrow-raising movements. Bardhan et al [18] proposed a system for automated wheelchair control, which combines EOG signal captured by Ag/AgCl electrodes and EEG signal captured by a headgear. Both signals are integrated by a microcontroller, which is responsible for controlling the movement of the wheelchair.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

Antoniou

Bozios

Christou

et al. 2021

Sensors

View full text Add to dashboard Cite

Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model’s prediction. The categories of the proposed random forests brain–computer interface (RF-BCI) are defined according to the position of the subject’s eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects’ EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology.

show abstract