Background Human-administered clinical scales are the accepted standard for quantifying motor performance of stroke subjects. Though widely accepted, these measurement tools are limited by interrater and intrarater reliability and are time consuming to apply. In contrast, robot-based measures are highly repeatable, have high resolution, and could potentially reduce assessment time. Although robotic and other objective metrics have proliferated in the literature, they are not as well established as clinical scales and their relationship to clinical scales is mostly unknown. Objective Test the performance of linear regression models to estimate clinical scores for the upper extremity from systematic robot-based metrics. Methods Twenty kinematic and kinetic metrics derived from movement data recorded with the shoulder-and-elbow InMotion2 robot (Interactive Motion Technologies, Inc), a commercial version of the MIT-Manus. Kinematic metrics were aggregated into macro-metrics and micrometrics and collected from 111 chronic stroke subjects. Multiple linear regression models were developed to calculate Fugl-Meyer Assessment, Motor Status Score, Motor Power, and Modified Ashworth Scale from these robot-based metrics. Results Best performance–complexity trade-off was achieved by the Motor Status Score model with 8 kinematic macro-metrics (R = .71 for training; R = .72 for validation). Models including kinematic micro-metrics did not achieve significantly higher performance. Performances of the Modified Ashworth Scale models were consistently low (R = .35–.42 for training; R = .08–.17 for validation). Conclusions The authors identified a set of kinetic and kinematic macro-metrics that may be used for fast outcome evaluations. These metrics represent a first step toward the development of unified, automated measures of therapy outcome.

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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.

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.

Abstract-Quantitative assessment of digit range of motion (ROM) is often needed for monitoring effectiveness of rehabilitative treatments and assessing patients' functional impairment. The objective of this research was to investigate the feasibility of using the Humanware Humanglove, a 20-position sensors glove, to measure fingers' ROM, with particular regard to measurement repeatability. With this aim, we performed a series of tests on six normal subjects. Data analysis was based on statistical parameters and on the intraclass correlation coefficient (ICC). Sources of errors that could affect measurement repeatability were also analyzed. The results demonstrate that, in principle, the glove could be used as goniometric device. The main advantage yielded by its use is reduction in the time needed to perform the whole measurement process, while maintaining process repeatability comparable to that achieved by traditional means of assessment. It also allows for dynamic and simultaneous recording of hand-joint movements. Future work will investigate accuracy of measurements.