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

Lassaad Zaway (), Nader Ben Amor, Jalel Ktari, Mohamed Jallouli, Larbi Chrifi Alaoui and Laurent Delahoche
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Lassaad Zaway: Laboratory of Computer Embedded System, National School of Engineering of Sfax, University of Sfax, Sfax 3029, Tunisia
Nader Ben Amor: Laboratory of Computer Embedded System, National School of Engineering of Sfax, University of Sfax, Sfax 3029, Tunisia
Jalel Ktari: Laboratory of Computer Embedded System, National School of Engineering of Sfax, University of Sfax, Sfax 3029, Tunisia
Mohamed Jallouli: Laboratory of Computer Embedded System, National School of Engineering of Sfax, University of Sfax, Sfax 3029, Tunisia
Larbi Chrifi Alaoui: Laboratory of Innovative Technologies, University of Picardie Jules Verne, 80000 Amiens, France
Laurent Delahoche: Laboratory of Innovative Technologies, University of Picardie Jules Verne, 80000 Amiens, France

Future Internet, 2024, vol. 16, issue 5, 1-16

Abstract: 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.

Keywords: Electroencephalogram (EEG); facial expressions; Long Short-Term Memory (LSTM); fusion data; convolutional neural network (CNN); control wheelchairs (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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