Stroke Rehabilitation with Distorted Vision Perceived as Forces.

The concept of augmenting error in interactive reaching training has shown promise, but the possibility of doing this robot-free, with only visual feedback, has not been tested. Here we present very early results from a visual distortion environment that shifts the subject's cursor in the direction of instantaneous error as if it is being pushed by a robot. This clinical test asked chronic stroke survivors to visit the laboratory three times a week for three weeks as they practiced a bimanual virtual reality task for approximately 40 minutes. Results show that both treatment and control patients improved from the practice (Fugyl Meyer average increase of 4.2), and a slight advantage is seen at this point in the treatment group. These vision-only results may prove compelling because removing the robot reduces expenses, intimidation, complexity, confounding effects, and failure modes.

A fresh perspective on dissecting action into discrete submotions.

The hypothesis that reaching motions are constructed from discrete components has been explored since the earliest scientific investigations of human movement, although composition specifics have been contentious. We reinspect this process by analyzing the underlying motor intent (rather than actual motion) using our recently-developed intent determination technique. First, synthetic data analysis was used to determine our accuracy in detecting submotion events. Next, we evaluated this on healthy reaching movements and overcame the problem of indistinguishably blended submotions by exposing subjects to strong, abruptly changing forces, which lead to clear corrections identifiable using direction-based clustering. We were able to accurately recover submotion parameters and identify patterns in submotion count, peak kinetic energy, and peak-to-peak duration. These values were all exponentially distributed, which implies that selection of submotions may follow simple rules. This provides a novel opportunity to investigate human motor action using the tools of statistics.

Multivariate outcomes in a three week bimanual self-telerehabilitation with error augmentation post-stroke.

We present the outcomes of a study on stroke patients in a 3-week intervention of bimanual self-telerehabilitation. This training is similar to an upper-extremity treadmill in that patients can make use of their healthy arm to provide a cue for the more impaired arm. We further inspected a cohort that covertly received error augmentation training while they practiced. Finally, we focused here on the many quantitative measures available from the robotic device, testing if these measures collectively can predict outcome on the final day. We found in a cross-validation study that predictions are possible, yielding median r-squared values over 99%. Several particular measures were found to dominate their contribution to the prediction of recoverability. These results show that interactive self-rehabilitation may be a viable method for motor restoration, and the quantitative metrics available can be used to predict the eventual state of recovery.