Controllable Workspace Generation for a Cable-Driven Rehabilitation Robot using Neural Network and based on patient's Anthropometric Parameters

This paper presents the mathematical modeling and simulation of a cable-driven robotic device that can be used in gait rehabilitation of patients with lower extremity disabilities. A parallel cable robot is considered to assist a model of human body during walking. First, a proper pattern of walking is considered and kinematic and dynamic equations are solved to obtain tensions in cables for entire cycle of walking. By exploiting a numerical procedure, the workspace of the robot are explored to find suspension points of the cables in which the model remain in controllable workspace of the robot. Remaining of the model in controllable workspace means that cables always remain in tension and robot can effectively engaged in rehabilitation. The optimum locations are determined based on minimum cable tensions (energy consumption) and a Neural Network is trained to quickly determine suspension points based on anthropometric parameters of patient. The simulation results show the effectiveness of the method in tracking of the desired trajectory of walking. The results of this study can be used for development and fabrication of an efficient cable driven rehabilitation system.