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Model-based variables for the kinematic assessment of upper-extremity impairments in post-stroke patients

View Article: PubMed Central - PubMed

ABSTRACT

Background: Common 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.

Methods: For 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.

Results: The 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.

Conclusion: This 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 material: The online version of this article (doi:10.1186/s12984-016-0187-9) contains supplementary material, which is available to authorized users.

No MeSH data available.


Real and simulated trajectories: Cartesian space. a Results for sub-acute patients. Real and simulated trajectories (first and second columns respectively) at T0 (first row) and T1 (second row) for a representative subject and for one repetition of the simulation. On the third columns the angular plots of the average normalized Euclidean distance among repetitions of the model (grey line, EI(S)) and among subjects (red line, EI(R)) for the 8 directions of movements. On the fourth columns the angular plots of the average normalized Euclidean distance between repetitions of the model and subjects for the 8 directions of movements (ERS). b Results for a representative chronic patient, same organization of sub-acute patient
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Fig2: Real and simulated trajectories: Cartesian space. a Results for sub-acute patients. Real and simulated trajectories (first and second columns respectively) at T0 (first row) and T1 (second row) for a representative subject and for one repetition of the simulation. On the third columns the angular plots of the average normalized Euclidean distance among repetitions of the model (grey line, EI(S)) and among subjects (red line, EI(R)) for the 8 directions of movements. On the fourth columns the angular plots of the average normalized Euclidean distance between repetitions of the model and subjects for the 8 directions of movements (ERS). b Results for a representative chronic patient, same organization of sub-acute patient

Mentions: Simulated trajectories were generated for sub-acute and chronic patients both at time T0 and T1 (Fig. 2). As explained in Methods (Computational model) our aim was not to precisely reconstruct real trajectories, but to generate simulated trajectories consistent with the real ones. Indeed, simulated trajectories showed significant geometrical similarity with the real trajectories. In fact, the range of variation of ERS was comparable to the intrinsic trajectory variability of real curves, EI(R), both at T0 and T1. The average ERS (across movement directions and subjects) was 14.52 ± 0.78 % at T0 and 13.02 ± 0.52 % at T1, for sub-acute patients, and 16.88 ± 1.43 % at T0 and 13.05 ± 1.25 % at T1, for chronic patients. Comparable ranges were confirmed by statistical tests. Indeed, only ERS for sub-acute patients at T1 was higher than EI(R) (p = 0.008).Fig. 2


Model-based variables for the kinematic assessment of upper-extremity impairments in post-stroke patients
Real and simulated trajectories: Cartesian space. a Results for sub-acute patients. Real and simulated trajectories (first and second columns respectively) at T0 (first row) and T1 (second row) for a representative subject and for one repetition of the simulation. On the third columns the angular plots of the average normalized Euclidean distance among repetitions of the model (grey line, EI(S)) and among subjects (red line, EI(R)) for the 8 directions of movements. On the fourth columns the angular plots of the average normalized Euclidean distance between repetitions of the model and subjects for the 8 directions of movements (ERS). b Results for a representative chronic patient, same organization of sub-acute patient
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5016877&req=5

Fig2: Real and simulated trajectories: Cartesian space. a Results for sub-acute patients. Real and simulated trajectories (first and second columns respectively) at T0 (first row) and T1 (second row) for a representative subject and for one repetition of the simulation. On the third columns the angular plots of the average normalized Euclidean distance among repetitions of the model (grey line, EI(S)) and among subjects (red line, EI(R)) for the 8 directions of movements. On the fourth columns the angular plots of the average normalized Euclidean distance between repetitions of the model and subjects for the 8 directions of movements (ERS). b Results for a representative chronic patient, same organization of sub-acute patient
Mentions: Simulated trajectories were generated for sub-acute and chronic patients both at time T0 and T1 (Fig. 2). As explained in Methods (Computational model) our aim was not to precisely reconstruct real trajectories, but to generate simulated trajectories consistent with the real ones. Indeed, simulated trajectories showed significant geometrical similarity with the real trajectories. In fact, the range of variation of ERS was comparable to the intrinsic trajectory variability of real curves, EI(R), both at T0 and T1. The average ERS (across movement directions and subjects) was 14.52 ± 0.78 % at T0 and 13.02 ± 0.52 % at T1, for sub-acute patients, and 16.88 ± 1.43 % at T0 and 13.05 ± 1.25 % at T1, for chronic patients. Comparable ranges were confirmed by statistical tests. Indeed, only ERS for sub-acute patients at T1 was higher than EI(R) (p = 0.008).Fig. 2

View Article: PubMed Central - PubMed

ABSTRACT

Background: Common 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.

Methods: For 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.

Results: The 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.

Conclusion: This 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 material: The online version of this article (doi:10.1186/s12984-016-0187-9) contains supplementary material, which is available to authorized users.

No MeSH data available.