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Spatiotemporal characteristics of muscle patterns for ball catching

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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Title
Spatiotemporal characteristics of muscle patterns for ball catching
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00107
Pubmed ID
Authors

M. D'Andola, B. Cesqui, A. Portone, L. Fernandez, F. Lacquaniti, A. d'Avella

Abstract

What sources of information and what control strategies the central nervous system (CNS) uses to perform movements that require accurate sensorimotor coordination, such as catching a flying ball, is still debated. Here we analyzed the EMG waveforms recorded from 16 shoulder and elbow muscles in six subjects during catching of balls projected frontally from a distance of 6 m and arriving at two different heights and with three different flight times (550, 650, 750 ms). We found that a large fraction of the variation in the muscle patterns was captured by two time-varying muscle synergies, coordinated recruitment of groups of muscles with specific activation waveforms, modulated in amplitude and shifted in time according to the ball's arrival height and flight duration. One synergy was recruited with a short and fixed delay from launch time. Remarkably, a second synergy was recruited at a fixed time before impact, suggesting that it is timed according to an accurate time-to-contact estimation. These results suggest that the control of interceptive movements relies on a combination of reactive and predictive processes through the intermittent recruitment of time-varying muscle synergies. Knowledge of the dynamic effect of gravity and drag on the ball may be then implicitly incorporated in a direct mapping of visual information into a small number of synergy recruitment parameters.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 64 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 23%
Student > Master 12 18%
Researcher 10 15%
Student > Bachelor 5 8%
Professor 5 8%
Other 13 20%
Unknown 5 8%
Readers by discipline Count As %
Engineering 17 26%
Neuroscience 11 17%
Medicine and Dentistry 7 11%
Agricultural and Biological Sciences 6 9%
Computer Science 6 9%
Other 11 17%
Unknown 7 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 August 2013.
All research outputs
#20,196,821
of 22,715,151 outputs
Outputs from Frontiers in Computational Neuroscience
#1,157
of 1,336 outputs
Outputs of similar age
#248,768
of 280,748 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#105
of 131 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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