Robotic therapy for chronic stroke: general recovery of impairment or improved task-specific skill?

Abstract

There is a great need to develop new approaches for rehabilitation of the upper limb after stroke. Robotic therapy is a promising form of neurorehabilitation that can be delivered in higher doses than conventional therapy. Here we sought to determine whether the reported effects of robotic therapy, which have been based on clinical measures of impairment and function, are accompanied by improved motor control. Patients with chronic hemiparesis were trained for 3 wk, 3 days a week, with titrated assistive robotic therapy in two and three dimensions. Motor control improvements (i.e., skill) in both arms were assessed with a separate untrained visually guided reaching task. We devised a novel PCA-based analysis of arm trajectories that is sensitive to changes in the quality of entire movement trajectories without needing to prespecify particular kinematic features. Robotic therapy led to skill improvements in the contralesional arm. These changes were not accompanied by changes in clinical measures of impairment or function. There are two possible interpretations of these results. One is that robotic therapy only leads to small task-specific improvements in motor control via normal skill-learning mechanisms. The other is that kinematic assays are more sensitive than clinical measures to a small general improvement in motor control.

Keywords: kinematics; motor control; neurorehabilitation; robotic therapy; stroke.

Fig. 1.

ReoGo robotic device. Picture provided courtesy of Motorika Medical. Photograph by Eli Gross.

Fig. 2.

Average number of repetitions per session, divided into the 5 modes of interaction (A) and the different exercises performed (B). Separate prespecified protocols were followed by low- and medium-functioning patients. Each week, there were progressively more challenging movements, with an increase in the number of repetitions, a decrease in the amount of assistance from the robotic device, and incorporation of more complex movements. Init, initiated; S. Init, step initiated; F. Assist, follow assist; For., forward; Horz., horizontal; Abd. abduction.

Fig. 3.

Reaching trajectories from a representative subject, before and after training for the affected (top) and unaffected (bottom) arms.

Fig. 4.

Average squared Mahalanobis distances (AMD) (± confidence intervals) at each time point, for the affected and unaffected arms. Values from a reference population of healthy control subjects are presented for comparison. Even with the unaffected arm, patients do not reach the level of performance of the healthy control subjects.

Fig. 5.

Changes in average squared Mahalanobis distances (± confidence intervals) across time points for 3 methods: assuming independence across targets (Ind.), assuming uniform correlation across targets (Unif.), and assuming correlations within subgroups for single-joint (Sing.) and multijoint (Mult.) targets. Negative values represent an improvement in reaching kinematics. The only changes that reach statistical significance (*) occur between pretest 2 and posttest 1, for the affected/trained arm.