Abstract
BackgroundCommon scales for clinical evaluation of post-stroke upper-limb motor recovery are often complemented with kinematic parameters extracted from movement trajectories. However, there is no a general consensus on which parameters to use. Moreover, the selected variables may be redundant and highly correlated or, conversely, may incompletely sample the kinematic information from the trajectories. Here we sought to identify a set of clinically useful variables for an exhaustive but yet economical kinematic characterization of upper limb movements performed by post-stroke hemiparetic subjects.MethodsFor this purpose, we pursued a top-down model-driven approach, seeking which kinematic parameters were pivotal for a computational model to generate trajectories of point-to-point planar movements similar to those made by post-stroke subjects at different levels of impairment.ResultsThe set of kinematic variables used in the model allowed for the generation of trajectories significantly similar to those of either sub-acute or chronic post-stroke patients at different time points during the therapy. Simulated trajectories also correctly reproduced many kinematic features of real movements, as assessed by an extensive set of kinematic metrics computed on both real and simulated curves. When inspected for redundancy, we found that variations in the variables used in the model were explained by three different underlying and unobserved factors related to movement efficiency, speed, and accuracy, possibly revealing different working mechanisms of recovery.ConclusionThis study identified a set of measures capable of extensively characterizing the kinematics of upper limb movements performed by post-stroke subjects and of tracking changes of different motor improvement aspects throughout the rehabilitation process.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-016-0187-9) contains supplementary material, which is available to authorized users.
Highlights
Common scales for clinical evaluation of post-stroke upper-limb motor recovery are often complemented with kinematic parameters extracted from movement trajectories
The low d% values between simulated and real angular trajectories (19 % for the shoulder and 5 % for the elbow, on average) showed that joint movements of post-stroke subjects were finely reproduced. Taken together these results demonstrated that the proposed computational model was able to simulate trajectories similar to those observed in post-stroke subjects at different impairment levels, both in the Cartesian and in the joint spaces
We investigated whether the kinematic features of real trajectories were well reproduced by simulated trajectories by comparing the values of the Evaluation parameters calculated on both trajectory types at T0 and T1 (Model-based parameters were used for the generation of the simulated trajectories as explained in section 2.3, and could not be used for the purpose of model evaluation)
Summary
Common scales for clinical evaluation of post-stroke upper-limb motor recovery are often complemented with kinematic parameters extracted from movement trajectories. We sought to identify a set of clinically useful variables for an exhaustive but yet economical kinematic characterization of upper limb movements performed by post-stroke hemiparetic subjects. Together with more traditional and widely accepted clinical scales in the last two decades investigators have characterized post-stroke motor recovery in terms of kinematic parameters extracted from hand and arm task-oriented movements [3, 5], which offer more objective measures of motor performance [6]. A few of them focused on finding a significant relationship between robotic measures collected longitudinally in post-stroke patients and clinical outcome measures, to increase acceptance of kinematic evaluation scales in practice [5, 6]. Too little effort has been made to identify the different aspects of movement improvement, how they can be described by kinematic robot-based measures [11], and whether they may dissociate with respect to recovery time course and to training response [11]
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