Polymorphic Control Framework for Automated and Individualized Robot-Assisted Rehabilitation

Robots were introduced in the field of upper limb neurorehabilitation to relieve the therapist from physical labor, and to provide high-intensity therapy to the patient. A variety of control methods were developed that incorporate patients' physiological and biomechanical states to adapt the provided assistance automatically. Higher level states, such as selected type of assistance, chosen task characteristics, defined session goals, and given patient impairments, are often neglected or modeled into tight requirements, low-dimensional study designs, and narrow inclusion criteria so that presented solutions cannot be transferred to other tasks, robotic devices or target groups. In this work, we present the design of a modular high-level control framework based on invariant states covering all decision layers in therapy. We verified the functionality of our framework on the assistance and task layer by outlaying the invariant states based on the characteristics of 20 examined state-of-the-art controllers. Then, we integrated four controllers on each layer and designed two algorithms that automatically selected suitable controllers. The framework was deployed on an arm rehabilitation robot and tested on one participant acting as a patient. We observed plausible system reactions to external changes by a second operator representing a therapist. We believe that this work will boost the development of novel controllers and selection algorithms in cooperative decision-making on layers other than assistance, and eases transferability and integration of existing solutions on lower layers into arbitrary robotic systems.