Optimal exoskeleton design and effective human-in-the-loop control frameworks for rehabilitation robotics

Attention, since they decrease the cost of repetitive movement therapies, enable quantitative measurement of the patient progress and promise development of more e ective rehabilitation protocols. The goal of this dissertation is to provide systematic frameworks for optimal design of rehabilitation robots and e ective delivery of therapeutic exercises. The design framework is built upon identification and categorization of the design requirements, and satisfaction of them through several design stages. In particular, type selection is performed to ensure imperative design requirements of safety, ergonomy and wearability, optimal dimensional synthesis is undertaken to maximize global kinematic and dynamic performance defined over the singularity-free workspace volume, while workspace optimization is performed to utilize maximum singularity-free device workspace computed via Grassmann line theory. Then, humanin- the-loop controllers that ensure coupled stability of the human-robot system are implemented in the robot task space using appropriate error metrics. The design framework is demonstrated on a forearm-wrist exoskeleton, since forearm and wrist rotations are critical in performing activities of daily living and recovery of these joints is essential for achieving functional independence of patients. In particular, a non-symmetric 3RPS-R mechanism is selected as the underlying kinematics type and the performance improvements due to workspace and multi-criteria optimizations are experimentally characterized as 27 % larger workspace volume, 32 % higher position control bandwidth and 17 % increase in kinematic isotropy when compared to a similar device in the literature. The exoskeleton is also shown to feature high passive back-driveability and accurate sti ness rendering capability, even under open-loop impedance control. Local controllers to accommodate for each stage of rehabilitation therapies are designed for the forearm-wrist exoskeleton in SO(3): trajectory tracking controllers are designed for early stages of rehabilitation when severely injured patients are kept passive, impedance controllers are designed to render virtual tunnels implementing forbidden regions in the device workspace and allowing for haptic interactions with virtual environments, and passive contour tracking controllers are implemented to allow for rehabilitation exercises that emphasize coordination and synchronization of multi degrees-of-freedom movements, while leaving the exact timing along the desired contour to the patient. These local controllers are incorporated into a multi-lateral shared controller architecture, which allows for patients to train with online virtual dynamic tasks in collaboration with a therapist. Utilizing this control architecture not only enables the shift of control authority of each agent so that therapists can guide or evaluate movements of patients or share the control with them, but also enables the implementation of remote and group therapies, as well as remote assessments. The proposed control framework to deliver e ective robotic therapies can ensure active involvement of patients through online modification of the task parameters, while simultaneously guaranteeing their safety. In particular, utilizing passive velocity field control and extending it with a method for online generation of velocity fields for parametric curves, temporal, spatial and assistive aspects of a desired task can be seamlessly modified online, while ensuring passivity with respect to externally applied forces. Through human subject experiments, this control framework is shown to be e ective in delivering evidence-based rehabilitation therapies, providing assistance as-needed, preventing slacking behavior of patients, and delivering repetitive therapies without exact repetition. Lastly, to guide design of e ective rehabilitation treatment protocols, a set of healthy human subject experiments are conducted in order to identify underlying principles of adaptation mechanism of human motor control system. In these catch-trial based experiments, equivalent transfer functions are utilized during execution of rhythmic dynamic tasks. Statistical evidence suggests that i) force feedback is the dominant factor that guides human adaptation while performing fast rhythmic dynamic tasks rather than the visual feedback and ii) as the e ort required to perform the task increases, the rate of adaptation decreases; indicating a fundamental trade-o between task performance and level of force feedback provided

Brain computer interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies

Encapsulating and representing the knowledge on the evaluation of an engineering system

This paper proposes a cross-disciplinary methodology for a fundamental question in product development: How can the innovation patterns during the evolution of an engineering system (ES) be encapsulated, so that it can later be mined through data mining analysis methods? Reverse engineering answers the question of which components a developed engineering system consists of, and how the components interact to make the working product. TRIZ answers the question of which problem-solving principles can be, or have been employed in developing that system, in comparison to its earlier versions, or with respect to similar systems. While these two methodologies have been very popular, to the best of our knowledge, there does not yet exist a methodology that reverseengineers and encapsulates and represents the information regarding the complete product development process in abstract terms. This paper suggests such a methodology, that consists of mathematical formalism, graph visualization, and database representation. The proposed approach is demonstrated by analyzing the design and development process for a prototype wrist-rehabilitation robot

Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities (BBA tabanlı üst uzuv rehabilitasyon sisteminin sonsal olasılık değerleri kullanılarak kontrolü)

In this paper, an electroencephalogram (EEG) based Brain-Computer Interface (BCI) is integrated with a robotic system designed to target rehabilitation therapies of stroke patients such that patients can control the rehabilitation robot by imagining movements of their right arm. In particular, the power density of frequency bands are used as features from the EEG signals recorded during the experiments and they are classified by Linear Discriminant Analysis (LDA). As one of the novel contributions of this study, the posterior probabilities extracted from the classifier are directly used as the continuous-valued outputs, instead of the discrete classification output commonly used by BCI systems, to control the speed of the therapeutic movements performed by the robotic system. Adjusting the exercise speed of patients online, as proposed in this study, according to the instantaneous levels of motor imagery during the movement, has the potential to increase efficacy of robot assisted therapies by ensuring active involvement of patients. The proposed BCI-based robotic rehabilitation system has been successfully implemented on physical setups in our laboratory and sample experimental data are presented

Resting-state EEG correlates of motor learning performance in a force-field adaptation task

Recent BCI-based stroke rehabilitation studies focus on exploiting information obtained from sensorimotor EEG activity. In the present study, to extend this focus beyond sensorimotor rhythms, we investigate associative brain areas that are also related with motor learning skills. Based on experimental data from twenty-one healthy subjects, resting-state EEG recorded prior to the experiment was used to predict motor learning performance during a force-field adaptation task in which subjects performed center-out reaching movements disturbed by an external force-field. A broad resting-state beta-power configuration was found to be predictive of motor adaptation rate. Our findings suggest that resting EEG beta-power is an indicator of subjects' ability to learn new motor skills and adapt to different sensorimotor states. This information can be further exploited in a novel BCI-based stroke rehabilitation approach we propose

Pre-movement contralateral EEG low beta power is modulated with motor adaptation learning

Various neuroimaging studies aim to understand the complex nature of human motor behavior. There exists a variety of experimental approaches to study neurophysiological correlates of performance during different motor tasks. As distinct from studies based on visuomotor learning, we investigate changes in electroencephalographic (EEG) activity during an actual physical motor adaptation learning experiment. Based on statistical analysis of EEG signals collected during a force-field adaptation task performed with the dominant hand, we observe a modulation of pre-movement upper alpha (10-12 Hz) and lower beta (13-16 Hz) powers over the contralateral region. This modulation is observed to be stronger in lower beta range and, through a regression analysis, is shown to be related with motor adaptation performance on a subject-specific level

Online generation of velocity fields for passive contour following

A new approach to online generation of velocity fields for parametric curves is presented for implementation in passive velocity field controllers (PVFC) that enable human-in-the-loop contour following tasks. In particular, a feedback stabilized closest point tracking algorithm is utilized for real-time determination of the contour error and online construction of the velocity field. The algorithm augments the system dynamics with a new state, and implements a uniformly asymptotically stable controller to update this new state to continual track the closest point to the robot end-effector. Thanks to its feedback-stabilized core, the algorithm is immune to initialization errors, and robust against drift and numerical noise. Furthermore, requiring simple evaluations of the curve and its unit tangents, the approach is computationally efficient. Applicability and effectiveness of the approach to implement passive contour following tasks have been demonstrated through simulations and experiments with a two degrees-of-freedom haptic interface

Slacking prevention during assistive contour following tasks with guaranteed coupled stability

Passive velocity field control is advantageous to deliver human-in-the-loop contour tracking rehabilitation exercises, since patients can be allowed to proceed with their preferred pace, while assistance can still be provided as determined by the therapist with ensured coupled stability. We introduce a framework based on passive velocity field control for robot assisted rehabilitation that includes prevention mechanisms against undesired slacking behavior of patients. This framework not only provides systematic approaches to prevent slacking, but also can do so while ensuring coupled stability of the overall robot patient system, a property that cannot be assured with any of the other ad-hoc slacking prevention methods. In particular, the proposed approach enables seamless on-line modification of the task difficulty, speed of contour following, and the level of assistance, while preserving passivity of the system with respect to external forces. The proposed slacking prevention schemes encourage active participation of the patients in rehabilitation protocols with even increased number of repetitions and thanks to flexibility introduced by the controller, render delivery of "repetitive tasks without repeating the same task" possible. Experiments with an haptic interface are included to demonstrate the passivity of the proposed control framework and preliminary human subject experiments with healthy volunteers are presented to validate feasibility and usability of the proposed approaches

Housekeeping with multiple autonomous robots: representation, reasoning and execution

We formalize actions and change in a housekeeping domain with multiple cleaning robots, and commonsense knowledge about this domain, in the action language C+. Geometric reasoning is lifted to high-level representation by embedding motion planning in the domain description using external predicates. With such a formalization of the domain, a plan can be computed using the causal reasoner CCALC for each robot to tidy some part of the house. We introduce a planning and monitoring algorithm for safe execution of these plans, so that it can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. We illustrate the applicability of this algorithm with a simulation of a housekeeping domain