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, nonrobotic 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.

-Ivaldi. Persistence of motor adaptation during constrained, multi-joint, arm movements. J Neurophysiol 84: [853][854][855][856][857][858][859][860][861][862] 2000. We studied the stability of changes in motor performance associated with adaptation to a novel dynamic environment during goal-directed movements of the dominant arm. Eleven normal, human subjects made targeted reaching movements in the horizontal plane while holding the handle of a two-joint robotic manipulator. This robot was programmed to generate a novel viscous force field that perturbed the limb perpendicular to the desired direction of movement. Following adaptation to this force field, we sought to determine the relative role of kinematic errors and dynamic criteria in promoting recovery from the adapted state. In particular, we compared kinematic and dynamic measures of performance when kinematic errors were allowed to occur after removal of the viscous fields, or prevented by imposing a simulated, mechanical "channel" on movements. Hand forces recorded at the handle revealed that when kinematic errors were prevented from occurring by the application of the channel, recovery from adaptation to the novel field was much slower compared with when kinematic aftereffects were allowed to take place. In particular, when kinematic errors were prevented, subjects persisted in generating large forces that were unnecessary to generate an accurate reach. The magnitude of these forces decreased slowly over time, at a much slower rate than when subjects were allowed to make kinematic errors. This finding provides strong experimental evidence that both kinematic and dynamic criteria influence motor adaptation, and that kinematic-dependent factors play a dominant role in the rapid loss of adaptation after restoring the original dynamics.

Abstract-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 patient's 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, "assist-as-needed" controller with people who have suffered a stroke. The results of these experiments illustrate the "slacking" 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 "assist-as-needed" modification in the controller increases participation from the motor system.

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.

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.

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.

Motor adaptation to a novel dynamic environment is primarily thought of as a process in which the nervous system learns to anticipate the environmental forces to eliminate kinematic error. Here we show that motor adaptation can more generally be modeled as a process in which the motor system greedily minimizes a cost function that is the weighted sum of kinematic error and effort. The learning dynamics predicted by this minimization process are a linear, auto-regressive equation with only one state, which has been identified previously as providing a good fit to data from force-field-type experiments. Thus we provide a new theoretical result that shows how these previously identified learning dynamics can be viewed as arising from an optimization of error and effort. We also show that the coefficients of the learning dynamics must fall within a specific range for the optimization model to be valid and verify with experimental data from walking in a force field that they indeed fall in this range. Finally, we attempted to falsify the model by performing experiments in two conditions (repeated exposure to a force field, exposure to force fields of different strengths) for which the single-state, auto-regressive equation might be expected to not fit the data well. We found however that the equation adequately captured the pattern of errors and thus conclude that motor adaptation to a force field can be approximated as an optimization of effort and error for a range of experimental conditions.

Training with the current method improved accuracy, and reduced variance, of FMA scoring; the 20% FMA variance reduction with training would decrease sample size requirements from 137 to 88 in a theoretical trial aiming to detect a 7-point FMA difference. Minimal detectable change was much smaller than FMA minimal clinically important difference. The variation in FMA gains in relation to baseline FMA suggests that future trials consider a sliding outcome approach when FMA is an outcome measure. The current training approach may be useful for assessing motor outcomes in restorative stroke trials.