A Polymorphic Control Framework for Robot-Assisted Neurorehabilitation

Abstract

The leading cause of long-term disability and acute hospitalization in high-income countries is stroke, with more than 13.7 million new cases worldwide each year. Stroke often results in paralysis of the limbs. To recover from lost functionality, long-term, intensive neurorehabilitation addressing individualized tasks and active participation of the patient is needed. To relieve the therapists from parts of the physical workload, to increase training efficiency beyond the therapist’s capabilities, and to allow quantitative, continuous measurements of the executed intervention, robots were introduced in the field almost two decades ago. In an ideal setting, therapists and robots collaborate to provide quality-assured intensive therapy for multiple patients. Recent robotic hardware such as the ANYexo 2.0 has the potential to achieve such a collaboration through its versatility, speed, and strength. To exploit the potential of the hardware, a multitude of assistive controllers have been developed. Recent controllers attempt to achieve partially unsupervised therapy by adapting the provided assistance automatically. However, the strong focus on automation of the assistance layer has resulted in a variety of narrow-purpose solutions addressing very specific tasks. Higher layer states such as the selected type of assistance, the chosen task characteristics, the defined session goals, and the given patient impairments have often been neglected or modeled into tight requirements, low-dimensional study designs, and narrow inclusion criteria. Consequently, the presented controllers are hardly transferable to other tasks, robotic devices, or patient and therapist target groups. Due to the versatility of activities performed with the arms and hands, and due to the versatility of comorbidities among patients, narrow-purpose solutions and associated studies lose their significance in the clinical setting. Thus, the overarching objective of this thesis was to develop a unified control framework that is capable of integrating existing and novel specialized control methods, incorporating patients' physiological and biomechanical states, and that allows for automatic or on-demand adjustment of assistance through therapeutic input. This work makes three contributions: Firstly, a polymorphic control framework based on invariant states was developed, which encompasses all decision layers in therapy. The functionality of the framework was verified on the assistance and task layers. Secondly, with the newly established control framework as a basis, we designed layer-specific controllers that can adapt to the broad context of higher layers such as therapy goals, the patient's range of motion, and activities of daily living. In this way, we developed a method for rectifying robotic recordings according to the context in which the data was acquired. This enables therapists to make scores comparable and interpretable between sessions, exercises and tasks. Furthermore, a method was constructed to enable safe haptic interaction with the patient's torso and head, which can adapt to the patient's dynamic body movements. Moreover, we developed methods for generating target positions and trajectories based on a selected therapy goal, and adhere to constraints from the exercise, the patient, and the robot. Thirdly, an observational study was conducted to analyze emerging therapeutic interaction strategies with the objective of tailoring parameters and analysis tools towards the individual preferences of the therapist in order to exploit the increased interaction possibilities that resulted from the layer-specific controllers. Furthermore, we investigated alternative interface options beyond the screen and mouse. We developed an intuitive user interface, ARMStick, which was designed to represent a miniature human arm that can be manipulated by therapists intuitively. Our polymorphic control framework represents a significant advance in the field of software and control for current upper-limb exoskeletons. Unlike previous systems, it addresses the challenges in a holistic manner, taking into account all therapeutic decision layers. This foundation work is intended to facilitate the development of novel controllers and selection algorithms for cooperative decision-making on layers other than assistance. Ultimately, it should facilitate the transferability and integration of existing solutions on lower layers into arbitrary robotic systems.