Nizar Tarabay

Title: Low Cost Assistive and Obstacle Detection System Applied to a Sip and Puff Wheelchair

Project Statement

Patients with tetraplegia (also known as quadriplegia) suffer from the loss of sensation and lack of control of their four limbs. Many systems such as the sip and puff wheelchair were developed to assist tetraplegic patients and help accommodate their mobility needs. A high end tetraplegia wheelchair (although not affordable to all) can allow for acceptable mobility whereby the patient can smoothly maneuver the wheelchair. On the other hand, a low cost sip and puff (S&P) wheelchair has limited adaptability and mobility. Low cost models are reported to cause respiratory fatigue due to the large number of commands (needed for navigation) which the patients have to feed the system (sipping and puffing). These low end models are also difficult to operate particularly in non- standard environments (sharp curves, circular curves, etc.). This project aims at enhancing the low cost S&P wheelchairs while keeping it an affordable choice for all patients. The project goals will be achieved by adding an array of obstacle detection sensors and integrating a fuzzy assistive system.

Approach

Ultrasonic sensors deployed on the S&P wheelchair help in creating a clear image of the surrounding environment. These sensors help in reducing the number of commands triggered by the patient who will still maintain complete command of the system. Moreover, an assistive system will further enhance the mobility and adaptability considerations by using a Fuzzy Logic controller. The rule base is developed through experiments that will provide the user with the best control results. Previous research by Al Halabi (Assistive Navigation of a Sip and Puff Controlled Wheelchair Using Obstacle Detectors) showed positive simulation results whereby combining ultrasonic sensors and fuzzy logic controllers reduced the number of commands needed to navigate the wheelchair by 10% to 40% (depending on the complexity of the environment). Moreover, the overall smoothness, precision, and stability of the system were enhanced by the Fuzzy Logic implementation (as compared to the human driving algorithms). MyRIO, the real-time embedded evaluation board made by National Instruments, will be the system controller, and the code will be developed using LabVIEW.