Joint Trajectory Generation and High-level Control for Patient-tailored Robotic Gait Rehabilitation

In the last two decades, several robotic systems have been developed aiming to improve the gait rehabilitation process, thanks to the evident advantages that these systems have over traditional, manual therapy approaches. Robotic systems are able to perform motion training in an intensive, precise, repetitive and reproducible way, and allow a clearer parameterization of the therapy. However, it is not yet clear what the most efficient way to utilize these devices is, in order to maximize the rehabilitation outcome.

This dissertation presents a group of novel methods which were developed with the objective of offering more individualized therapies based on the specific condition of each patient, aiming as well to improve the overall rehabilitation experience for both patient and therapist. A novel methodology for gait pattern generation is proposed, which offers estimated hip and knee joint trajectories corresponding to healthy walking, and allows the therapist to adapt easily and graphically the reference trajectories used by the system's controllers in order to fit better the patient's needs and disabilities. Additionally, a set of methods involving the motion control of an over-ground gait rehabilitation system is also presented. The presented control strategies include the motion controllers for the system's hip and knee joints, mobile platform, and pelvic mechanism, as well as some proposed methods for "assist as needed" therapy. Two robot-patient synchronization approaches are also included in this work, together with a novel algorithm for online hip trajectory adaptation developed to reduce obstructive forces applied to the patient during therapy with compliant robotic systems. Finally, a prototype graphical user interface for the therapist is also presented.