Real-time fuzzy trajectory generation for robotic rehabilitation therapy

This paper proposes a method for the design of a real-time fuzzy trajectory generator for the robotic rehabilitation of patients with upper limb dysfunction due to neurological diseases. The system utilizes a fuzzy-logic schema to introduce compliance into the human-robot interaction, and to allow the emulation of a wide variety of therapy techniques. This approach also allows for the fine-tuning of system dynamics using linguistic variables. The rule base for the system is trained using a fuzzy clustering approach based on experimental data gathered during traditional therapy sessions. The trajectory generator will be packaged as a platform-independent solution to facilitate the rehabilitation of patients using multiple manipulator configurations.