Smoothness is characteristic of coordinated human movements, and stroke patients' movements seem to grow more smooth with recovery. We used a robotic therapy device to analyze five different measures of movement smoothness in the hemiparetic arm of 31 patients recovering from stroke. Four of the five metrics showed general increases in smoothness for the entire patient population. However, according to the fifth metric, the movements of patients with recent stroke grew less smooth over the course of therapy. This pattern was reproduced in a computer simulation of recovery based on submovement blending, suggesting that progressive blending of submovements underlies stroke recovery.

Synergies are thought to be the building blocks of vertebrate movements. The inability to execute synergies in properly timed and graded fashion precludes adequate functional motor performance. In humans with stroke, abnormal synergies are a sign of persistent neurological deficit and result in loss of independent joint control, which disrupts the kinematics of voluntary movements. This study aimed at characterizing training-related changes in synergies apparent from movement kinematics and, specifically, at assessing: 1) the extent to which they characterize recovery and 2) whether they follow a pattern of augmentation of existing abnormal synergies or, conversely, are characterized by a process of extinction of the abnormal synergies. We used a robotic therapy device to train and analyze paretic arm movements of 117 persons with chronic stroke. In a task for which they received no training, subjects were better able to draw circles by discharge. Comparison with performance at admission on kinematic robot-derived metrics showed that subjects were able to execute shoulder and elbow joint movements with significantly greater independence or, using the clinical description, with more isolated control. We argue that the changes we observed in the proposed metrics reflect changes in synergies. We show that they capture a significant portion of the recovery process, as measured by the clinical Fugl-Meyer scale. A process of "tuning" or augmentation of existing abnormal synergies, not extinction of the abnormal synergies, appears to underlie recovery.

No abstract

Abstract-Robotics and related technologies have begun to realize their promise to improve the delivery of rehabilitation therapy. However, the mechanism by which they enhance recovery remains unclear. Ultimately, recovery depends on biology, yet the details of the recovery process remain largely unknown; a deeper understanding is important to accelerate refinements of robotic therapy or suggest new approaches. Fortunately, robots provide an excellent instrument platform from which to study recovery at the behavioral level. This article reviews some initial insights about the process of upper-limb behavioral recovery that have emerged from our work. Evidence to date suggests that the form of therapy may be more important than its intensity: muscle strengthening offers no advantage over movement training. Passive movement is insufficient; active participation is required. Progressive training based on measures of movement coordination yields substantially improved outcomes. Together these results indicate that movement coordination rather than muscle activation may be the most appropriate focus for robotic therapy.

Despite a threefold increase in treatment interventions studies during the past 10 years, "best practice" for the rehabilitation of the paretic upper limb is still unclear. This review aims to lessen uncertainty in the management of the poststroke upper limb. Two separate searches of the scientific literature from 1966-2001 yielded 333 articles. Three referees, using strict inclusion and exclusion criteria, selected 68 relevant references. Cohort studies, randomized control trials, and systematic reviews were critically appraised. Mean randomized control trial quality (n = 33) was 17.1/27 (SD = 5.2, 95% CI = 15.2-19.0, range = 6-26). Mean quality of cohort studies (n = 29) was 11.8/27 (SD = 3.8, 95% CI = 10.4-13.2, range = 4-19). Quantitative syntheses were done using the Z-statistic. This systematic review indicated that sensorimotor training; motor learning training that includes the use of imagery, electrical stimulation alone, or combined with biofeedback; and engaging the client in repetitive, novel tasks can be effective in reducing motor impairment after stroke. Furthermore, careful handling, electrical stimulation, movement with elevation, strapping, and the avoidance of overhead pulleys could effectively reduce or prevent pain in the paretic upper limb. Rehabilitation specialists can use this research synthesis to guide their selection of effective treatment techniques for persons with impairments after stroke.

Submovements are hypothesized building blocks of human movement, discrete ballistic movements of which more complex movements are composed. Using a novel algorithm, submovements were extracted from the point-to-point movements of 41 persons recovering from stroke. Analysis of the extracted submovements showed that, over the course of therapy, patients' submovements tended to increase in peak speed and duration. The number of submovements employed to produce a given movement decreased. The time between the peaks of adjacent submovements decreased for inpatients (those less than 1 month post-stroke), but not for outpatients (those greater than 12 months post-stroke) as a group. Submovements became more overlapped for all patients, but more markedly for inpatients. The strength and consistency with which it quantified patients' recovery indicates that analysis of submovement overlap might be a useful tool for measuring learning or other changes in motor behavior in future human movement studies.

No abstract

Both the American Heart Association and the VA/DoD endorse upper-extremity robot-mediated rehabilitation therapy for stroke care. However, we do not know yet how to optimize therapy for a particular patient’s needs. Here, we explore whether we must train patients for each functional task that they must perform during their activities of daily living or alternatively capacitate patients to perform a class of tasks and have therapists assist them later in translating the observed gains into activities of daily living. The former implies that motor adaptation is a better model for motor recovery. The latter implies that motor learning (which allows for generalization) is a better model for motor recovery. We quantified trained and untrained movements performed by 158 recovering stroke patients via 13 metrics, including movement smoothness and submovements. Improvements were observed both in trained and untrained movements suggesting that generalization occurred. Our findings suggest that, as motor recovery progresses, an internal representation of the task is rebuilt by the brain in a process that better resembles motor learning than motor adaptation. Our findings highlight possible improvements for therapeutic algorithms design, suggesting sparse-activity-set training should suffice over exhaustive sets of task specific training.