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

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 solu...

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Bibliographic Details
Published in:Future internet 2024-05, Vol.16 (5), p.158
Main Authors: Zaway, Lassaad, Ben Amor, Nader, Ktari, Jalel, Jallouli, Mohamed, Chrifi Alaoui, Larbi, Delahoche, Laurent
Format: Article
Language:English
Subjects:
Access control
Algorithms
Analysis
Artificial intelligence
Blinking
Brain research
Computational linguistics
Computer vision
Control systems
control wheelchairs
convolutional neural network (CNN)
Deep learning
Disabled persons
Electroencephalogram (EEG)
Electroencephalography
Embedded systems
Engineering Sciences
facial expressions
fusion data
Human-computer interface
Interfaces
Internet
Language processing
Long Short-Term Memory (LSTM)
Machine vision
Methods
Natural language interfaces
Neural networks
People with disabilities
Privacy
Sensors
Signal processing
Spectrum analysis
Steering
Wavelet transforms
Webcams
Wheelchairs
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Summary: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.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi16050158