Arm Exoskeleton for Rehabilitation Following Stroke by Learning Algorithm Prediction

Abstract

Stroke is a major cause of disability in worldwide and also one of the causes of death after coronary heart disease. Many devices had been designed for hand motor function rehabilitation that a stroke survivor can use for bilateral movement practice. This paper presents an arm motor function rehabilitation device where it is designed to predict the position angle for the robotic arm. MATLAB software is used for real-time positioning that can be developed by SIMULINK block diagram and proof by the simulator in program code in order for devising to operate under the position demand. All the angular motions or feedback to the simulation mode from the attached optical encoders via the Data Acquisition Card (DAQ). The learning algorithm can directly determine the position of its joint and can therefore completely eliminate the need for any system modelling. The robotic arm shows a successful implementation of the learning algorithm in predicting the behavior for arm exoskeleton.