Welcome to the InfoSci Platform
Reference Hub2
Knowledge Discovery and Multimodal Inputs for Driving an Intelligent Wheelchair

Knowledge Discovery and Multimodal Inputs for Driving an Intelligent Wheelchair

Brígida Mónica Faria, Luís Paulo Reis, Nuno Lau
Copyright: © 2011 |Volume: 2 |Issue: 4 |Pages: 17
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781613508183|DOI: 10.4018/jkdb.2011100102
Cite Article Cite Article

MLA

Faria, Brígida Mónica, et al. "Knowledge Discovery and Multimodal Inputs for Driving an Intelligent Wheelchair." IJKDB vol.2, no.4 2011: pp.18-34. http://doi.org/10.4018/jkdb.2011100102

APA

Faria, B. M., Reis, L. P., & Lau, N. (2011). Knowledge Discovery and Multimodal Inputs for Driving an Intelligent Wheelchair. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 2(4), 18-34. http://doi.org/10.4018/jkdb.2011100102

Chicago

Faria, Brígida Mónica, Luís Paulo Reis, and Nuno Lau. "Knowledge Discovery and Multimodal Inputs for Driving an Intelligent Wheelchair," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 2, no.4: 18-34. http://doi.org/10.4018/jkdb.2011100102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Cerebral Palsy is defined as a group of permanent disorders in the development of movement and posture. The motor disorders in cerebral palsy are associated with deficits of perception, cognition, communication, and behaviour, which can affect autonomy and independence. The interface between the user and an intelligent wheelchair can be done with several input devices such as joysticks, microphones, and brain computer interfaces (BCI). BCI enables interaction between users and hardware systems through the recognition of brainwave activity. The current BCI systems have very low accuracy on the recognition of facial expressions and thoughts, making it difficult to use these devices to enable safe and robust commands of complex devices like an Intelligent Wheelchair. This paper presents an approach to expand the use of a brain computer interface for driving an intelligent wheelchair by patients suffering from cerebral palsy. The ability with the joystick, head movements, and voice inputs were tested, and the best possibility for driving the wheelchair is given to a specific user. Experiments were performed using 30 individuals suffering from IV and V degrees of cerebral palsy on the Gross Motor Function (GMF) measure. The results show that the pre-processing and variable selection methods are effective to improve the results of a commercial BCI product by 57%. With the developed system, it is also possible for users to perform a circuit in a simulated environment using just facial expressions and thoughts.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.