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.

Persistence of motor adaptation during constrained, multi-joint, arm movements

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.

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.

Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment

An important goal in rehabilitation engineering is to develop technology that allows individuals with severe motor impairment to practice arm movement without continuous supervision from a rehabilitation therapist. This paper describes the development of such a system, called Therapy WREX or ("T-WREX"). The system consists of an orthosis that assists in arm movement across a large workspace, a grip sensor that detects hand grip pressure, and software that simulates functional activities. The arm orthosis is an instrumented, adult-sized version of the Wilmington Robotic Exoskeleton (WREX), which is a five degrees-of-freedom mechanism that passively counterbalances the weight of the arm using elastic bands. After providing a detailed design description of T-WREX, this paper describes two pilot studies of the system's capabilities. The first study demonstrated that individuals with chronic stroke whose arm function is compromised in a normal gravity environment can perform reaching and drawing movements while using T-WREX. The second study demonstrated that exercising the affected arm of five people with chronic stroke with T-WREX over an eight week period improved unassisted movement ability (mean change in Fugl-Meyer score was 5 points +/- 2 SD; mean change in range of motion of reaching was 10%, p < 0.001). These results demonstrate the feasibility of automating upper-extremity rehabilitation therapy for people with severe stroke using passive gravity assistance, a grip sensor, and simple virtual reality software.

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

Background and purpose

Providing 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.

Methods

Nineteen 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 discussion

There 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.

Conclusion

Improvements 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 study's limited sample size, the specific diagnoses of the participants, or the inclusion of only individuals with chronic stroke.

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.

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 standardized approach to the Fugl-Meyer assessment and its implications for clinical trials

BACKGROUND

Standardizing scoring reduces variability and increases accuracy. A detailed scoring and training method for the Fugl-Meyer motor assessment (FMA) is described and assessed, and implications for clinical trials considered.

METHODS

A standardized FMA scoring approach and training materials were assembled, including a manual, scoring sheets, and instructional video plus patient videos. Performance of this approach was evaluated for the upper extremity portion.

RESULTS

Inter- and intrarater reliability in 31 patients were excellent (intraclass correlation coefficient = 0.98-0.99), validity was excellent (r = 0.74-0.93, P < .0001), and minimal detectable change was low (3.2 points). Training required 1.5 hours and significantly reduced error and variance among 50 students, with arm FMA scores deviating from the answer key by 3.8 ± 6.2 points pretraining versus 0.9 ± 4.9 points posttraining. The current approach was implemented without incident into training for a phase II trial. Among 66 patients treated with robotic therapy, change in FMA was smaller (P ≤ .01) at the high and low ends of baseline FMA scores.

CONCLUSIONS

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.

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 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.

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 device's 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.

Robotics, motor learning, and neurologic recovery

Robotic devices are helping shed light on human motor control in health and injury. By using robots to apply novel force fields to the arm, investigators are gaining insight into how the nervous system models its external dynamic environment. The nervous system builds internal models gradually by experience and uses them in combination with impedance and feedback control strategies. Internal models are robust to environmental and neural noise, generalized across space, implemented in multiple brain regions, and developed in childhood. Robots are also being used to assist in repetitive movement practice following neurologic injury, providing insight into movement recovery. Robots can haptically assess sensorimotor performance, administer training, quantify amount of training, and improve motor recovery. In addition to providing insight into motor control, robotic paradigms may eventually enhance motor learning and rehabilitation beyond the levels possible with conventional training techniques.

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.

Motor Adaptation as a Greedy Optimization of Error and Effort

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.

Alterations in reaching after stroke and their relation to movement direction and impairment severity

OBJECTIVES

To examine the effects of stroke severity and target location on reaching (1) to identify regions in space that are difficult to reach, (2) to determine whether specific alterations in reaching are associated with particular clinical impairment levels, and (3) to characterize relationships between reaching alterations.

DESIGN

Participants reached toward a screen of 75 targets spanning an approximate range from +/-90 degrees side to side and from waist to head.

SETTING

Rehabilitation research center.

PARTICIPANTS

Sixteen chronic stroke patients with a wide range in residual arm function and 4 control subjects.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Chedoke-McMaster Stroke Arm Assessment, distance, velocity, smoothness, straightness, and direction of the hand path during each reach. Hand position trajectories were recorded with an electromagnetic sensor.

RESULTS

Reaches performed with the impaired arms showed significant degradation in all performance measures. Although only modestly dependent on the target location, these features correlated strongly with impairment level, as well as with each other. Reaching distance showed the strongest correlations with the other parameters.

CONCLUSIONS

Stroke alters a broad array of features of reaching, yet largely the same degree of movement control is preserved across a range of target locations. The only consistently problematic task is to reach far out from the torso, independent of the movement direction. Thus, active range of motion (AROM), rather than control over a specific subset of movement directions, is a logical focus for therapy. In addition, measuring AROM is a simple clinical measure that yields much information.

Robotic devices for movement therapy after stroke: current status and challenges to clinical acceptance

Robotic devices for movement therapy are moving closer to becoming commercially available tools for aiding in stroke rehabilitation. Robotic technology offers a range of functions that will augment current clinical practice by leveraging therapists' time, cost effectively extending therapy programs, providing new measures of impairment, and offering new therapy protocols. In this article, we review work from several research laboratories that supports the clinical value of stroke therapy systems. A commercialization effort based on these results is described. We also discuss challenges to achieving clinical acceptance and practical implementation of these devices.

Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed

Background

A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning.

Methods

Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability.

Results

We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human.

Conclusion

The assist-as-needed algorithm proposed here can limit error during the learning of a dynamic motor task. The algorithm encourages learning by decreasing its assistance as a function of the ongoing progression of movement error. This type of algorithm is well suited for helping people learn dynamic tasks for which large kinematic errors are dangerous or discouraging, and thus may prove useful for robot-assisted movement training of walking or reaching following neurologic injury.

Impedance control and internal model formation when reaching in a randomly varying dynamical environment

We investigated the effects of trial-to-trial, random variation in environmental forces on the motor adaptation of human subjects during reaching. Novel sequences of dynamic environments were applied to subjects' hands by a robot. Subjects reached first in a "mean field" having a constant gain relating force and velocity, then in a "noise field," having a gain that varied randomly between reaches according to a normal distribution with a mean identical to that of the mean field. The unpredictable nature of the noise field did not degrade adaptation as quantified by final kinematic error and rate of adaptation. To achieve this performance, the nervous system used a dual strategy. It increased the impedance of the arm as evidenced by a significant reduction in aftereffect size following removal of the noise field. Simultaneously, it formed an internal model of the mean of the random environment, as evidenced by a minimization of trajectory error on trials for which the noise field gain was close to the mean field gain. We conclude that the human motor system is capable of predicting and compensating for the dynamics of an environment that varies substantially and randomly from trial to trial, while simultaneously increasing the arm's impedance to minimize the consequence of errors in the prediction.

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.

Comparison of error-amplification and haptic-guidance training techniques for learning of a timing-based motor task by healthy individuals

Performance errors drive motor learning for many tasks. Some researchers have suggested that reducing performance errors with haptic guidance can benefit learning by demonstrating correct movements, while others have suggested that artificially increasing errors will force faster and more complete learning. This study compared the effect of these two techniques--haptic guidance and error amplification--as healthy subjects learned to play a computerized pinball-like game. The game required learning to press a button using wrist movement at the correct time to make a flipper hit a falling ball to a randomly positioned target. Errors were decreased or increased using a robotic device that retarded or accelerated wrist movement, based on sensed movement initiation timing errors. After training with either error amplification or haptic guidance, subjects significantly reduced their timing errors and generalized learning to untrained targets. However, for a subset of more skilled subjects, training with amplified errors produced significantly greater learning than training with the reduced errors associated with haptic guidance, while for a subset of less skilled subjects, training with haptic guidance seemed to benefit learning more. These results suggest that both techniques help enhanced performance of a timing task, but learning is optimized if training subjects with the appropriate technique based on their baseline skill level.

Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does?

Robot-assisted movement training improves arm movement ability following acute and chronic stroke. Such training involves two interacting processes: the patient trying to move and the robot applying forces to the patient's arm. A fundamental principle of motor learning is that movement practice improves motor function; the role of applied robotic forces in improving motor function is still unclear. This article reviews our work addressing this question. Our pilot study using the Assisted Rehabilitation and Measurement (ARM) Guide, a linear robotic trainer, found that mechanically assisted reaching improved motor recovery similar to unassisted reaching practice. This finding is inconclusive because of the small sample size (n = 19), but suggest that future studies should carefully control the amount of voluntary movement practice delivered to justify the use of robotic forces. We are optimistic that robotic forces will ultimately show additional therapeutic benefits when coupled with movement practice. We justify this optimism here by comparing results from the ARM Guide and the Mirror Image Movement Enabler robotic trainer. This comparison suggests that requiring a patient to generate specific patterns of force before allowing movement is more effective than mechanically completing movements for the patient. We describe the engineering implementation of this "guided-force training" algorithm.

A Home-Based Telerehabilitation Program for Patients With Stroke

BACKGROUND

Although rehabilitation therapy is commonly provided after stroke, many patients do not derive maximal benefit because of access, cost, and compliance. A telerehabilitation-based program may overcome these barriers. We designed, then evaluated a home-based telerehabilitation system in patients with chronic hemiparetic stroke.

METHODS

Patients were 3 to 24 months poststroke with stable arm motor deficits. Each received 28 days of telerehabilitation using a system delivered to their home. Each day consisted of 1 structured hour focused on individualized exercises and games, stroke education, and an hour of free play.

RESULTS

Enrollees (n = 12) had baseline Fugl-Meyer (FM) scores of 39 ± 12 (mean ± SD). Compliance was excellent: participants engaged in therapy on 329/336 (97.9%) assigned days. Arm repetitions across the 28 days averaged 24,607 ± 9934 per participant. Arm motor status showed significant gains (FM change 4.8 ± 3.8 points, P = .0015), with half of the participants exceeding the minimal clinically important difference. Although scores on tests of computer literacy declined with age ( r = -0.92; P < .0001), neither the motor gains nor the amount of system use varied with computer literacy. Daily stroke education via the telerehabilitation system was associated with a 39% increase in stroke prevention knowledge ( P = .0007). Depression scores obtained in person correlated with scores obtained via the telerehabilitation system 16 days later ( r = 0.88; P = .0001). In-person blood pressure values closely matched those obtained via this system ( r = 0.99; P < .0001).

CONCLUSIONS

This home-based system was effective in providing telerehabilitation, education, and secondary stroke prevention to participants. Use of a computer-based interface offers many opportunities to monitor and improve the health of patients after stroke.

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

Locomotor ability in spinal rats is dependent on the amount of activity imposed on the hindlimbs during treadmill training

Studies have shown that treadmill training with body weight support is effective for enhancing locomotor recovery following a complete spinal cord transection (ST) in animals. However, there have been no studies that have investigated the extent that functional recovery in ST animals is dependent on the amount of activity imposed on the hindlimbs during training. In rats transected as neonates (P5), we used a robotic device to impose either a high or a low amount of hindlimb activity during treadmill training starting 23 days after transection. The rats were trained 5 days per week for 4 weeks. One group (n = 13) received 1000 steps/training session and a second group (n = 13) received 100 steps/training session. During training, the robotic device imposed the maximum amount of weight that each rat could bear on the hindlimbs, and counted the number of stepping movements during each session. After 4 weeks of training, the number of steps performed during treadmill testing was not significantly different between the two groups. However, the quality of stepping in the group that received 1000 steps/training session improved over a range of levels of weight bearing on the hindlimbs and at different treadmill speeds. In contrast, little improvement in the quality of stepping was observed in the group that received only 100 steps/training session. These findings indicate that the ability of the lumbar spinal cord to adjust to load- and speed-related sensory stimuli associated with stepping is dependent on the number of repetitions of the same activity that is imposed on the spinal circuits during treadmill training.

Feasibility of manual teach-and-replay and continuous impedance shaping for robotic locomotor training following spinal cord injury

Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject-specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot's impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients.

Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke

BACKGROUND

Practicing arm and gait movements with robotic assistance after neurologic injury can help patients improve their movement ability, but patients sometimes reduce their effort during training in response to the assistance. Reduced effort has been hypothesized to diminish clinical outcomes of robotic training. To better understand patient slacking, we studied the role of visual distraction and auditory feedback in modulating patient effort during a common robot-assisted tracking task.

METHODS

Fourteen participants with chronic left hemiparesis from stroke, five control participants with chronic right hemiparesis and fourteen non-impaired healthy control participants, tracked a visual target with their arms while receiving adaptive assistance from a robotic arm exoskeleton. We compared four practice conditions: the baseline tracking task alone; tracking while also performing a visual distracter task; tracking with the visual distracter and sound feedback; and tracking with sound feedback. For the distracter task, symbols were randomly displayed in the corners of the computer screen, and the participants were instructed to click a mouse button when a target symbol appeared. The sound feedback consisted of a repeating beep, with the frequency of repetition made to increase with increasing tracking error.

RESULTS

Participants with stroke halved their effort and doubled their tracking error when performing the visual distracter task with their left hemiparetic arm. With sound feedback, however, these participants increased their effort and decreased their tracking error close to their baseline levels, while also performing the distracter task successfully. These effects were significantly smaller for the participants who used their non-paretic arm and for the participants without stroke.

CONCLUSIONS

Visual distraction decreased participants effort during a standard robot-assisted movement training task. This effect was greater for the hemiparetic arm, suggesting that the increased demands associated with controlling an affected arm make the motor system more prone to slack when distracted. Providing an alternate sensory channel for feedback, i.e., auditory feedback of tracking error, enabled the participants to simultaneously perform the tracking task and distracter task effectively. Thus, incorporating real-time auditory feedback of performance errors might improve clinical outcomes of robotic therapy systems.