Smart assistive technology: Intelligent controller design to mitigate tremors due to Multiple-Sclerosis in controlling electric wheelchairs

Many wheelchair users suffer from Multiple Sclerosis causing tremors in their limbs, which leads to incorrect control signals to the wheelchair joystick. In this paper, we design and simulate a Neuro-Fuzzy based joystick controller for an electric wheelchair to minimize tremors as well as adapt to a patient's peculiar controlling commands. The controls of an electronic wheelchair are designed to execute the directional commands given by the user. The purpose of the fuzzy controller is to modify the wheelchair user's erratic joystick movements to control the wheelchair smoothly and accurately. The learning and adaptation system is designed using Pseudo-Outer Product (POP) neural learning to modify the membership functions of the fuzzy logic control system for optimizing the controller to the tremor characteristics of a specific user.