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Gravitational and Dynamic Components of Muscle Torque Underlie Tonic and Phasic Muscle Activity during Goal-Directed Reaching

Overview of attention for article published in Frontiers in Human Neuroscience, September 2017
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
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Title
Gravitational and Dynamic Components of Muscle Torque Underlie Tonic and Phasic Muscle Activity during Goal-Directed Reaching
Published in
Frontiers in Human Neuroscience, September 2017
DOI 10.3389/fnhum.2017.00474
Pubmed ID
Authors

Erienne V. Olesh, Bradley S. Pollard, Valeriya Gritsenko

Abstract

Human reaching movements require complex muscle activations to produce the forces necessary to move the limb in a controlled manner. How gravity and the complex kinetic properties of the limb contribute to the generation of the muscle activation pattern by the central nervous system (CNS) is a long-standing and controversial question in neuroscience. To tackle this issue, muscle activity is often subdivided into static and phasic components. The former corresponds to posture maintenance and transitions between postures. The latter corresponds to active movement production and the compensation for the kinetic properties of the limb. In the present study, we improved the methodology for this subdivision of muscle activity into static and phasic components by relating them to joint torques. Ten healthy subjects pointed in virtual reality to visual targets arranged to create a standard center-out reaching task in three dimensions. Muscle activity and motion capture data were synchronously collected during the movements. The motion capture data were used to calculate postural and dynamic components of active muscle torques using a dynamic model of the arm with 5 degrees of freedom. Principal Component Analysis (PCA) was then applied to muscle activity and the torque components, separately, to reduce the dimensionality of the data. Muscle activity was also reconstructed from gravitational and dynamic torque components. Results show that the postural and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to define static and phasic components of muscle activity using muscle torques.

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X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 14%
Student > Ph. D. Student 5 14%
Professor > Associate Professor 4 11%
Student > Master 3 8%
Other 2 5%
Other 4 11%
Unknown 14 38%
Readers by discipline Count As %
Neuroscience 5 14%
Engineering 4 11%
Medicine and Dentistry 3 8%
Design 2 5%
Sports and Recreations 2 5%
Other 5 14%
Unknown 16 43%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 January 2018.
All research outputs
#7,393,440
of 23,460,553 outputs
Outputs from Frontiers in Human Neuroscience
#3,102
of 7,294 outputs
Outputs of similar age
#116,836
of 321,296 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#70
of 134 outputs
Altmetric has tracked 23,460,553 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,294 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 321,296 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.