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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.


Related in: MedlinePlus

Schematic overview of the computational model for post-stroke trajectories simulation. (1) Endpoint kinematics of one pathological subject making point to point forward and backward movements from the center of the workspace to one of eight different targets equally spaced around a circle of 14 cm of radius assisted by InMotion2. (2) Kinematic parameters are extracted from the real trajectories of the post-stroke subjects. The tangential speed profile of real trajectories (for each movement direction and subject, separately for each group of patients and time of therapy) is analyzed to extract probability distributions of nPK, <σ>, T, and MV. (3) Based on the inferred probability distributions, tangential speed profiles of simulated trajectories are generated by solving a constrained optimization problem (Eq. 2). (4) The transversal and longitudinal speed profiles of real trajectories are analyzed to extract probability distributions of ratio-ampL, ratio-ampN, ratio-nPK, MVN, CONTL, and CONTN. (5) From the simulated tangential velocities and the inferred distributions of kinematic parameters, transversal and longitudinal speed profiles of simulated trajectories are generated, by solving an unconstrained optimization problem (Eq. 4). (6) The trajectories in the Cartesian space defined by the (L, N) axes (see step 4) are obtained from the speed profiles by numerical integration. (7) The generated trajectories are then rotated by means of a geometrical transformation to reproduce the point-to-point movements performed by post-stroke subjects during a turn in the InMotion2 coordinate frame system
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Fig1: Schematic overview of the computational model for post-stroke trajectories simulation. (1) Endpoint kinematics of one pathological subject making point to point forward and backward movements from the center of the workspace to one of eight different targets equally spaced around a circle of 14 cm of radius assisted by InMotion2. (2) Kinematic parameters are extracted from the real trajectories of the post-stroke subjects. The tangential speed profile of real trajectories (for each movement direction and subject, separately for each group of patients and time of therapy) is analyzed to extract probability distributions of nPK, <σ>, T, and MV. (3) Based on the inferred probability distributions, tangential speed profiles of simulated trajectories are generated by solving a constrained optimization problem (Eq. 2). (4) The transversal and longitudinal speed profiles of real trajectories are analyzed to extract probability distributions of ratio-ampL, ratio-ampN, ratio-nPK, MVN, CONTL, and CONTN. (5) From the simulated tangential velocities and the inferred distributions of kinematic parameters, transversal and longitudinal speed profiles of simulated trajectories are generated, by solving an unconstrained optimization problem (Eq. 4). (6) The trajectories in the Cartesian space defined by the (L, N) axes (see step 4) are obtained from the speed profiles by numerical integration. (7) The generated trajectories are then rotated by means of a geometrical transformation to reproduce the point-to-point movements performed by post-stroke subjects during a turn in the InMotion2 coordinate frame system

Mentions: The patients and the healthy subjects were instructed to make point-to-point reaching movements forward and backward from the center of the workspace to one of eight different targets equally spaced around a circle of 14 cm of radius (Fig. 1 step 1) assisted by InMotion2 (Interactive Motion Technologies, Inc. Cambridge, Massachusetts) [18]. When the subjects sequentially carried out all the 16 movements, they completed one full turn.Fig. 1


Model-based variables for the kinematic assessment of upper-extremity impairments in post-stroke patients
Schematic overview of the computational model for post-stroke trajectories simulation. (1) Endpoint kinematics of one pathological subject making point to point forward and backward movements from the center of the workspace to one of eight different targets equally spaced around a circle of 14 cm of radius assisted by InMotion2. (2) Kinematic parameters are extracted from the real trajectories of the post-stroke subjects. The tangential speed profile of real trajectories (for each movement direction and subject, separately for each group of patients and time of therapy) is analyzed to extract probability distributions of nPK, <σ>, T, and MV. (3) Based on the inferred probability distributions, tangential speed profiles of simulated trajectories are generated by solving a constrained optimization problem (Eq. 2). (4) The transversal and longitudinal speed profiles of real trajectories are analyzed to extract probability distributions of ratio-ampL, ratio-ampN, ratio-nPK, MVN, CONTL, and CONTN. (5) From the simulated tangential velocities and the inferred distributions of kinematic parameters, transversal and longitudinal speed profiles of simulated trajectories are generated, by solving an unconstrained optimization problem (Eq. 4). (6) The trajectories in the Cartesian space defined by the (L, N) axes (see step 4) are obtained from the speed profiles by numerical integration. (7) The generated trajectories are then rotated by means of a geometrical transformation to reproduce the point-to-point movements performed by post-stroke subjects during a turn in the InMotion2 coordinate frame system
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Schematic overview of the computational model for post-stroke trajectories simulation. (1) Endpoint kinematics of one pathological subject making point to point forward and backward movements from the center of the workspace to one of eight different targets equally spaced around a circle of 14 cm of radius assisted by InMotion2. (2) Kinematic parameters are extracted from the real trajectories of the post-stroke subjects. The tangential speed profile of real trajectories (for each movement direction and subject, separately for each group of patients and time of therapy) is analyzed to extract probability distributions of nPK, <σ>, T, and MV. (3) Based on the inferred probability distributions, tangential speed profiles of simulated trajectories are generated by solving a constrained optimization problem (Eq. 2). (4) The transversal and longitudinal speed profiles of real trajectories are analyzed to extract probability distributions of ratio-ampL, ratio-ampN, ratio-nPK, MVN, CONTL, and CONTN. (5) From the simulated tangential velocities and the inferred distributions of kinematic parameters, transversal and longitudinal speed profiles of simulated trajectories are generated, by solving an unconstrained optimization problem (Eq. 4). (6) The trajectories in the Cartesian space defined by the (L, N) axes (see step 4) are obtained from the speed profiles by numerical integration. (7) The generated trajectories are then rotated by means of a geometrical transformation to reproduce the point-to-point movements performed by post-stroke subjects during a turn in the InMotion2 coordinate frame system
Mentions: The patients and the healthy subjects were instructed to make point-to-point reaching movements forward and backward from the center of the workspace to one of eight different targets equally spaced around a circle of 14 cm of radius (Fig. 1 step 1) assisted by InMotion2 (Interactive Motion Technologies, Inc. Cambridge, Massachusetts) [18]. When the subjects sequentially carried out all the 16 movements, they completed one full turn.Fig. 1

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.


Related in: MedlinePlus