David J. Reinkensmeyer

Review of control strategies for robotic movement training after neurologic injury

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.

Retraining the injured spinal cord.

The present review presents a series of concepts that may be useful in developing rehabilitative strategies to enhance recovery of posture and locomotion following spinal cord injury. First, the loss of supraspinal input results in a marked change in the functional efficacy of the remaining synapses and neurons of intraspinal and peripheral afferent (dorsal root ganglion) origin. Second, following a complete transection the lumbrosacral spinal cord can recover greater levels of motor performance if it has been exposed to the afferent and intraspinal activation patterns that are associated with standing and stepping. Third, the spinal cord can more readily reacquire the ability to stand and step following spinal cord transection with repetitive exposure to standing and stepping. Fourth, robotic assistive devices can be used to guide the kinematics of the limbs and thus expose the spinal cord to the new normal activity patterns associated with a particular motor task following spinal cord injury. In addition, such robotic assistive devices can provide immediate quantification of the limb kinematics. Fifth, the behavioural and physiological effects of spinal cord transection are reflected in adaptations in most, if not all, neurotransmitter systems in the lumbosacral spinal cord. Evidence is presented that both the GABAergic and glycinergic inhibitory systems are up‐regulated following complete spinal cord transection and that step training results in some aspects of these transmitter systems being down‐regulated towards control levels. These concepts and observations demonstrate that (a) the spinal cord can interpret complex afferent information and generate the appropriate motor task; and (b) motor ability can be defined to a large degree by training.

Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study.

Background and purposeProviding active assistance to complete desired arm movements is a common technique in upper extremity rehabilitation after stroke. Such active assistance may improve recovery by affecting somatosensory input, motor planning, spasticity or soft tissue properties, but it is labor intensive and has not been validated in controlled trials. The purpose of this study was to investigate the effects of robotically administered active-assistive exercise and compare those with free reaching voluntary exercise in improving arm movement ability after chronic stroke.MethodsNineteen individuals at least one year post-stroke were randomized into one of two groups. One group performed 24 sessions of active-assistive reaching exercise with a simple robotic device, while a second group performed a task-matched amount of unassisted reaching. The main outcome measures were range and speed of supported arm movement, range, straightness and smoothness of unsupported reaching, and the Rancho Los Amigos Functional Test of Upper Extremity Function.Results and discussionThere were significant improvements with training for range of motion and velocity of supported reaching, straightness of unsupported reaching, and functional movement ability. These improvements were not significantly different between the two training groups. The group that performed unassisted reaching exercise improved the smoothness of their reaching movements more than the robot-assisted group.ConclusionImprovements with both forms of exercise confirmed that repeated, task-related voluntary activation of the damaged motor system is a key stimulus to motor recovery following chronic stroke. Robotically assisting in reaching successfully improved arm movement ability, although it did not provide any detectable, additional value beyond the movement practice that occurred concurrently with it. The inability to detect any additional value of robot-assisted reaching may have been due to this pilot studys limited sample size, the specific diagnoses of the participants, or the inclusion of only individuals with chronic stroke.

Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation

Based on evidence from recent experiments in motor learning and neurorehabilitation, we hypothesize that three desirable features for a controller for robot-aided movement training following stroke are high mechanical compliance, the ability to assist patients in completing desired movements, and the ability to provide only the minimum assistance necessary. This paper presents a novel controller that successfully exhibits these characteristics. The controller uses a standard model-based, adaptive control approach in order to learn the patients abilities and assist in completing movements while remaining compliant. Assistance-as-needed is achieved by adding a novel force reducing term to the adaptive control law, which decays the force output from the robot when errors in task execution are small. Several tests are presented using the upper extremity robotic therapy device named Pneu-WREX to evaluate the performance of the adaptive, ldquoassist-as-neededrdquo controller with people who have suffered a stroke. The results of these experiments illustrate the ldquoslackingrdquo behavior of human motor control: given the opportunity, the human patient will reduce his or her output, letting the robotic device do the work for it. The experiments also demonstrate how including the ldquoassist-as-neededrdquo modification in the controller increases participation from the motor system.

Web-based telerehabilitation for the upper extremity after stroke

Stroke is a leading cause of disability in the United States and yet little technology is currently available for individuals with stroke to practice and monitor rehabilitation therapy on their own. This paper provides a detailed design description of a telerehabilitation system for arm and hand therapy following stroke. The system consists of a Web-based library of status tests, therapy games, and progress charts, and can be used with a variety of input devices, including a low-cost force-feedback joystick capable of assisting or resisting in movement. Data from home-based usage by a chronic stroke subject are presented that demonstrate the feasibility of using the system to direct a therapy program, mechanically assist in movement, and track improvements in movement ability.

A Randomized Controlled Trial of Gravity-Supported, Computer-Enhanced Arm Exercise for Individuals With Severe Hemiparesis

Background/Objective. The authors previously developed a passive instrumented arm orthosis (Therapy Wilmington Robotic Exoskeleton [T-WREX]) that enables individuals with hemiparesis to exercise the arm by playing computer games in a gravity-supported environment. The purpose of this study was to compare semiautonomous training with T-WREX and conventional semiautonomous exercises that used a tabletop for gravity support. Methods. Twenty-eight chronic stroke survivors with moderate/severe hemiparesis were randomly assigned to experimental (T-WREX) or control (tabletop exercise) treatment. A blinded rater assessed arm movement before and after twenty-four 1-hour treatment sessions and at 6-month follow-up. Subjects also rated subjective treatment preferences after a single-session crossover treatment. Results. All subjects significantly improved ( P ≤ .05) upper extremity motor control (Fugl-Meyer), active reaching range of motion (ROM), and self-reported quality and amount of arm use (Motor Activity Log). Improvements were sustained at 6 months. The T-WREX group maintained gains on the Fugl-Meyer significantly better than controls at 6 months (improvement of 3.6 ± 3.9 vs 1.5 ± 2.7 points, mean ± SD; P = .04). Subjects also reported a preference for T-WREX training. Conclusion . Gravity-supported arm exercise, using the T-WREX or tabletop support, can improve arm movement ability after chronic severe hemiparesis with brief one-on-one assistance from a therapist (approximately 4 minutes per session). The 3-dimensional weight support, instant visual movement feedback, and simple virtual reality software provided by T-WREX were associated with modest sustained gains at 6-month follow-up when compared with the conventional approach.

Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification

When adapting to novel dynamic environments the nervous system learns to anticipate the imposed forces by forming an internal model of the environmental dynamics in a process driven by movement error reduction. Here, we tested the hypothesis that motor learning could be accelerated by transiently amplifying the environmental dynamics. A novel dynamic environment was created during treadmill stepping by applying a perpendicular viscous force field to the leg through a robotic device. The environmental dynamics were amplified by an amount determined by a computational learning model fit on a per-subject basis. On average, subjects significantly reduced the time required to predict the applied force field by approximately 26% when the field was transiently amplified. However, this reduction was not as great as that predicted by the model, likely due to nonstationarities in the learning parameters. We conclude that motor learning of a novel dynamic environment can be accelerated by exploiting the error-based learning mechanism of internal model formation, but that nonlinearities in adaptive response may limit the feasible acceleration. These results support an approach to movement training devices that amplify rather than reduce movement errors, and provide a computational framework for both implementing the approach and understanding its limitations.

A Robot and Control Algorithm That Can Synchronously Assist in Naturalistic Motion During Body-Weight-Supported Gait Training Following Neurologic Injury

Locomotor training using body weight support on a treadmill and manual assistance is a promising rehabilitation technique following neurological injuries, such as spinal cord injury (SCI) and stroke. Previous robots that automate this technique impose constraints on naturalistic walking due to their kinematic structure, and are typically operated in a stiff mode, limiting the ability of the patient or human trainer to influence the stepping pattern. We developed a pneumatic gait training robot that allows for a full range of natural motion of the legs and pelvis during treadmill walking, and provides compliant assistance. However, we observed an unexpected consequence of the devices compliance: unimpaired and SCI individuals invariably began walking out-of-phase with the device. Thus, the robot perturbed rather than assisted stepping. To address this problem, we developed a novel algorithm that synchronizes the device in real-time to the actual motion of the individual by sensing the state error and adjusting the replay timing to reduce this error. This paper describes data from experiments with individuals with SCI that demonstrate the effectiveness of the synchronization algorithm, and the potential of the device for relieving the trainers of strenuous work while maintaining naturalistic stepping.

Tools for understanding and optimizing robotic gait training

This article reviews several tools we have developed to improve the understanding of locomotor training following spinal cord injury (SCI), with a view toward implementing locomotor training with robotic devices. We have developed (1) a small-scale robotic device that allows testing of locomotor training techniques in rodent models, (2) an instrumentation system that measures the forces and motions used by experienced human therapists as they manually assist leg movement during locomotor training, (3) a powerful, lightweight leg robot that allows investigation of motor adaptation during stepping in response to force-field perturbations, and (4) computational models for locomotor training. Results from the initial use of these tools suggest that an optimal gait-training robot will minimize disruptive sensory input, facilitate appropriate sensory input and gait mechanics, and intelligently grade and time its assistance. Currently, we are developing a pneumatic robot designed to meet these specifications as it assists leg and pelvic motion of people with SCI.

Spinal Cord-Transected Mice Learn to Step in Response to Quipazine Treatment and Robotic Training

In the present study, concurrent treatment with robotic step training and a serotonin agonist, quipazine, generated significant recovery of locomotor function in complete spinal cord-transected mice (T7–T9) that otherwise could not step. The extent of recovery achieved when these treatments were combined exceeded that obtained when either treatment was applied independently. We quantitatively analyzed the stepping characteristics of spinal mice after alternatively administering no training, manual training, robotic training, quipazine treatment, or a combination of robotic training with quipazine treatment, to examine the mechanisms by which training and quipazine treatment promote functional recovery. Using fast Fourier transform and principal components analysis, significant improvements in the step rhythm, step shape consistency, and number of weight-bearing steps were observed in robotically trained compared with manually trained or nontrained mice. In contrast, manual training had no effect on stepping performance, yielding no improvement compared with nontrained mice. Daily bolus quipazine treatment acutely improved the step shape consistency and number of steps executed by both robotically trained and nontrained mice, but these improvements did not persist after quipazine was withdrawn. At the dosage used (0.5 mg/kg body weight), quipazine appeared to facilitate, rather than directly generate, stepping, by enabling the spinal cord neural circuitry to process specific patterns of sensory information associated with weight-bearing stepping. Via this mechanism, quipazine treatment enhanced kinematically appropriate robotic training. When administered intermittently during an extended period of robotic training, quipazine revealed training-induced stepping improvements that were masked in the absence of the pharmacological treatment.