Wheelchair Navigation System using EEG Signal and 2D Map for Disabled and Elderly People

Ngo, Ba-Viet; Nguyen, Thanh-Hai; Ngo, Van-Thuyen; Tran, Dang-Khoa; Nguyen, Truong-Duy

doi:10.1109/gtsd50082.2020.9303149

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…These techniques prove their effectiveness. The authors of [23,24] suggested a research strategy for producing 3D channel spectrograms that combines three different time-frequency representations (spectrograms, gamma-tone spectrograms, and continuous wavelet transform). Applications such as the automatic identification of phoneme classes for phone attribute extraction and the diagnosis of speech impairments in cochlear implant users have been successful.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG

Zaway,

Ben Amor,

Ktari

et al. 2024

Future Internet

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Even though Electric Powered Wheelchairs (EPWs) are a useful tool for meeting the needs of people with disabilities, some disabled people find it difficult to use regular EPWs that are joystick-controlled. Smart wheelchairs that use Brain–Computer Interface (BCI) technology present an efficient solution to this problem. This article presents a cutting-edge intelligent control wheelchair that is intended to improve user involvement and security. The suggested method combines facial expression analysis via a camera with EEG signal processing using the EMOTIV Insight EEG dataset. The system generates control commands by identifying specific EEG patterns linked to facial expressions such as eye blinking, winking left and right, and smiling. Simultaneously, the system uses computer vision algorithms and inertial measurements to analyze gaze direction in order to establish the user’s intended steering. The outcomes of the experiments prove that the proposed system is reliable and efficient in meeting the various requirements of people, presenting a positive development in the field of smart wheelchair technology.

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Section: Related Workmentioning

confidence: 99%

Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG

Zaway,

Ben Amor,

Ktari

et al. 2024

Future Internet

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“…Therefore, the EEG signals are transferred to the signal pre-processing block for filtering and scaling before being sent to the feature extraction block. For the control of the wheelchair, the EEG signals after pre-processing are sent to the classification block for classifying input signals to produce control commands [36][37][38]. It means that the user can use the control commands for selecting one of destinations on the environmental map to reach.…”

Section: System Architecturementioning

confidence: 99%

“…Figure 13 shows the green real path of the wheelchair, which was controlled by the user during reaching the target. In particular, the discontinuous green path is the desired path in the real environment that the wheelchair needs to follow to reach the target, while the red path of the wheelchair is the path controlled by self-control mode using EEG signals [38] to go straight, turn left and right during reaching the destination. With the experiment using the self-control, the wheelchair moved according to the red path and then turned to the undesired direction shown by the red path and blue dash-dot ellipse.…”

Section: Wheelchair Movement To Reach Map-based Desired Targetmentioning

confidence: 99%

A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

Ngo¹,

Hải²

2022

Applied Sciences

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A semi-automatic wheelchair allows disabled people to possibly control in an indoor environment with obstacles and targets. The paper proposes an EEG-based control system for the wheelchair based on a grid map designed to allow disabled people to reach any preset destination. In particular, the grid map is constructed by dividing it into grid cells that may contain free spaces or obstacles. The map with the grid cells is simulated to find the optimal paths to the target positions using a Deep Q-Networks (DQNs) model with the Parametric Rectified Linear Unit (PReLU) activation function, in which a novel algorithm for finding the optimal path planning by converting wheelchair actions is applied using the output parameters of the DQNs. For the wheelchair movement in one real indoor environment corresponding to the virtual 2D grid map, the initial position of the wheelchair will be determined based on natural landmarks and a graphical user interface designed for on-screen display can support disabled people in selecting the desired destination from a list of predefined locations using Electroencephalogram (EEG) signals by blinking eyes. Therefore, one user can easily and safely control the wheelchair using an EEG system to reach the desired target when the wheelchair position and destination are determined in the indoor environment. As a result, a grid map was developed and experiments for the semi-automatic wheelchair control were performed in real indoor environments to illustrate the effectiveness of the proposed method. In addition, the system is a platform to develop different types of controls depending on the types of user disabilities and different environmental maps built.

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A Measurement System for Improving Riding Comfortability of Omnidirectional Wheelchair Robot

Matsuo,

Barolli

2024

Lecture Notes on Data Engineering and Communications Technologies

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Wheelchair Navigation System using EEG Signal and 2D Map for Disabled and Elderly People

Cited by 7 publications

References 20 publications

Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG

Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG

A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals

A Measurement System for Improving Riding Comfortability of Omnidirectional Wheelchair Robot

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