Development of an intelligent electrical walking rehabilitation system

Presents an intelligent electrical walking rehabilitation system for rehabilitation automation. A set of force sensors to measure trainee´s ground reaction force are arranged at the force plates. The gait phases are detected by a neural network which inputs the force measurements directly. To realize the system in practical applications, we represent the rehabilitation expert knowledge by fuzzy membership functions and rules. The velocity tuning commands to assist the rehabilitation process are generated through the fuzzy inference engine in each detection cycle. We have completed the integration of these techniques in experimental tests. The results demonstrate that the system is able to adapt to the trainee walking phase to increase the training effect