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.

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.

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.

There is no "magic bullet" in rehabilitation. In the absence of direct neural transplants, neurological rehabilitation is an arduous process. We have pioneered the clinical application of robotics in stroke rehabilitation and have shown evidence of the positive impact of targeted exercise on stroke recovery. In this article, we will review results obtained in the initial clinical trials with 96 stroke patients at the Burke Rehabilitation Hospital. We will provide evidence that robot-aided training enhances recovery, that this enhanced recovery is sustained in the long term, and that this recovery is not due to a general physiological improvement--in fact, it appears to be limb and muscle group specific. An evidence-based approach must now segue into a more scientific approach to stroke rehabilitation. Given the length of the required protocols and patients' variability and limited census, the practical limitations of the evidence-based approach are self-evident and extend trials for years. Each patient and lesion is unique in stroke rehabilitation, so there is no reason to believe that a "one-size-fits-all" optimal treatment exists. To optimize therapy for individual patients, we need science-based models. In this article, we will summarize the scientific tools and models that we are investigating and present some of the results to date.

Scanning electron microscopes (SEMs) are used in neuroscience and materials science to image centimeters of sample area at nanometer scales. Since imaging rates are in large part SNR-limited, large collections can lead to weeks of around-the-clock imaging time. To increase data collection speed, we propose and demonstrate on an operational SEM a fast method to sparsely sample and reconstruct smooth images. To accurately localize the electron probe position at fast scan rates, we model the dynamics of the scan coils, and use the model to rapidly and accurately visit a randomly selected subset of pixel locations. Images are reconstructed from the undersampled data by compressed sensing inversion using image smoothness as a prior. We report image fidelity as a function of acquisition speed by comparing traditional raster to sparse imaging modes. Our approach is equally applicable to other domains of nanometer microscopy in which the time to position a probe is a limiting factor (e.g., atomic force microscopy), or in which excessive electron doses might otherwise alter the sample being observed (e.g., scanning transmission electron microscopy).