Abstract
The quality of arm movements typically improves in the sub-acute phase of stroke affecting the upper extremity. Here, we used whole arm kinematic analysis during reaching movements to distinguish whether these improvements are due to true recovery or to compensation. Fifty-three participants with post-acute stroke performed ∼80 reaching movement tests during 4 weeks of training with the ArmeoSpring exoskeleton. All participants showed improvements in end-effector performance, as measured by movement smoothness. Four ArmeoSpring angles, shoulder horizontal (SH) rotation, shoulder elevation (SE), elbow rotation, and forearm rotation, were recorded and analyzed. We first characterized healthy joint coordination patterns by performing a sparse principal component analysis on these four joint velocities recorded during reaching tests performed by young control participants. We found that two dominant joint correlations [SH with elbow rotation and SE with forearm rotation] explained over 95% of variance of joint velocity data. We identified two clusters of stroke participants by comparing the evolution of these two correlations in all tests. In the “Recoverer” cluster (N = 19), both joint correlations converged toward the respective correlations for control participants. Thus, Recoverers relearned how to generate smooth end-effector movements while developing joint movement patterns similar to those of control participants. In the “Compensator” cluster (N = 34), at least one of the two joint correlations diverged from the corresponding correlation of control participants. Compensators relearned how to generate smooth end-effector movements by discovering various new compensatory movement patterns dissimilar to those of control participants. New compensatory patterns included atypical decoupling of the SE and forearm joints, and atypical coupling of the SH rotation and elbow joints. There was no difference in clinical impairment level between the two groups either at the onset or at the end of training as assessed with the Upper Extremity Fugl-Meyer scale. However, at the start of training, the Recoverers showed significantly faster improvements in end-effector movement smoothness than the Compensators. Our analysis can be used to inform neurorehabilitation clinicians on how to provide movement feedback during practice and suggest avenues for refining exoskeleton robot therapy to reduce compensatory patterns.
Highlights
Individuals with stroke-induced loss of sensorimotor functionality in the upper extremity often experience some degree of improvements in the 6-month period after stroke
We found that while all participants with stroke recovered in the task space, only about a third relearned to move in joint space using patterns more similar to those of control participants during a month of kinematic training
These results are consistent with the proposal that post-stroke individuals become adept in new compensatory movements by performing repetitive movements through robotic training without receiving correction at the joint level (Crocher et al, 2012)
Summary
Individuals with stroke-induced loss of sensorimotor functionality in the upper extremity often experience some degree of improvements in the 6-month period after stroke Such improvements have been observed in motor impairment (Broeks et al, 1999; Duncan et al, 2000), in motor function (Wolf et al, 2001; Yozbatiran et al, 2008; Kitago et al, 2013; Winstein et al, 2016), and in measures of end-point kinematics, such as movement smoothness, speed, or range (Rohrer et al, 2002; Van Dokkum et al, 2014; Leinenga et al, 2016; Schweighofer et al, 2018). Because the ArmeoSpring is a redundant system with more degrees of freedom (DOF) than those of the endeffector in external space, it was unclear to what extent these fast and slow improvements in end-point kinematics were due to true recovery or to compensation
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