A wheelchair control system using human-machine interaction: single-modal and multimodal approaches

Shahin, MK; Tharwat, A; Gaber, T; Hassanien, AE

Abstract

Recent research studies showed that brain-controlled systems/devices are breakthrough technology. Such devices can provide disabled people with the power to control the movement of the wheelchair using different signals (e.g. EEG signals, head movements, and facial expressions). With this technology, disabled people can remotely steer a wheelchair, a computer, or a tablet. This paper introduces a simple, low-cost human-machine interface system to help chaired people to control their wheelchair using several control sources. To achieve this paper’s aim, a laptop was installed on a wheelchair in front of the sitting person, and the 14-electrode Emotiv EPOC headset was used to collect the person’s head impressions from the skull surface. The superficially picked-up signals, containing the brain thoughts, head gestures, and facial emotions, were electrically encoded and then wirelessly sent to a personal computer to be interpreted and then translated into useful control instructions. Using these signals, two wheelchair control modes were proposed: automatic (using single-modal and multimodal approaches) and manual control. The automatic mode controller was accomplished using a software controller (Arduino), whereas a simple hardware controller was used for the manual mode. The proposed solution was designed using wheelchair, Emotiv EPOC EEG headset, Arduino microcontroller, and Processing language. It was then tested by totally chaired volunteers under different levels of trajectories. The results showed that the person’s thoughts can be used to seamlessly control his/her wheelchair and the proposed system can be configured to suit many levels and degrees of disability.

Citation

Shahin, M., Tharwat, A., Gaber, T., & Hassanien, A. (2017). A wheelchair control system using human-machine interaction: single-modal and multimodal approaches. Journal of Intelligent Systems, 28(1), https://doi.org/10.1515/jisys-2017-0085

• Journal Article Type: Article
• Online Publication Date: Jun 12, 2017
• Publication Date: Jun 12, 2017
• Deposit Date: Sep 5, 2019
• Journal: Journal of Intelligent Systems
• Print ISSN: 0334-1860
• Electronic ISSN: 2191-026X
• Publisher: De Gruyter
• Volume: 28
• Issue: 1
• DOI: https://doi.org/10.1515/jisys-2017-0085
• Publisher URL: https://doi.org/10.1515/jisys-2017-0085
• Related Public URLs: https://www.degruyter.com/view/j/jisys