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

Key components of mechanical work predict outcomes in robotic stroke therapy

Background

Clinical practice typically emphasizes active involvement during therapy. However, traditional approaches can offer only general guidance on the form of involvement that would be most helpful to recovery. Beyond assisting movement, robots allow comprehensive methods for measuring practice behaviors, including the energetic input of the learner. Using data from our previous study of robot-assisted therapy, we examined how separate components of mechanical work contribute to predicting training outcomes.

Methods

Stroke survivors (n = 11) completed six sessions in two-weeks of upper extremity motor exploration (self-directed movement practice) training with customized forces, while a control group (n = 11) trained without assistance. We employed multiple regression analysis to predict patient outcomes with computed mechanical work as independent variables, including separate features for elbow versus shoulder joints, positive (concentric) and negative (eccentric), flexion and extension.

Results

Our analysis showed that increases in total mechanical work during therapy were positively correlated with our final outcome metric, velocity range. Further analysis revealed that greater amounts of negative work at the shoulder and positive work at the elbow as the most important predictors of recovery (using cross-validated regression, R² = 52%). However, the work features were likely mutually correlated, suggesting a prediction model that first removed shared variance (using PCA, R² = 65–85%).

Conclusions

These results support robotic training for stroke survivors that increases energetic activity in eccentric shoulder and concentric elbow actions.

Trial registration

ClinicalTrials.gov, Identifier: NCT02570256. Registered 7 October 2015 – Retrospectively registered,

Error Augmentation in Immersive Virtual Reality for Bimanual Upper-Limb Rehabilitation in Individuals With and Without Hemiplegic Cerebral Palsy

With more readily available commercial immersive virtual reality (VR) technologies, the potential of new feedback strategies as tools to facilitate motor rehabilitation should be investigated. Augmented feedback or error augmentation (EA) can easily be shown in a virtual environment. Here, visual EA provided via immersive VR was tested for its effectiveness to improve bimanual symmetry in a reaching task. A single-session crossover design was used to test two training cases, with or without EA. With EA, the distance between hands in the forward direction was augmented. Participants were recruited from typically developing (TD) populations (n = 12, ages 13-21) and performed in an adapted environment with an initial asymmetry between limbs. Also, five participants with hemiplegic cerebral palsy (CP) (ages 14-21, MACS I-III) completed the study. Among TD participants, a significantly larger change in symmetry in the adapted environment was shown after EA than training without EA (F (1, 10) = 9.64, p = 0.01). Each participant in the CP group also improved more after EA training (8.8-103.7)%, such that they achieved lower symmetry error after training with EA. As participants in both groups adapted more symmetrically with EA, beneficial changes from this training method could be evaluated in future studies for longer-term functional changes.

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.

Regression techniques employing feature selection to predict clinical outcomes in stroke

It is not fully clear which measurable factors can reliably predict chronic stroke patients’ recovery of motor ability. In this analysis, we investigate the impact of patient demographic characteristics, movement features, and a three-week upper-extremity intervention on the post-treatment change in two widely used clinical outcomes—the Upper Extremity portion of the Fugl-Meyer and the Wolf Motor Function Test. Models based on LASSO, which in validation tests account for 65% and 86% of the variability in Fugl-Meyer and Wolf, respectively, were used to identify the set of salient demographic and movement features. We found that age, affected limb, and several measures describing the patient’s ability to efficiently direct motions with a single burst of speed were the most consequential in predicting clinical recovery. On the other hand, the upper-extremity intervention was not a significant predictor of recovery. Beyond a simple prognostic tool, these results suggest that focusing therapy on the more important features is likely to improve recovery. Such validation-intensive methods are a novel approach to determining the relative importance of patient-specific metrics and may help guide the design of customized therapy.