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Kinematic decomposition and classification of octopus arm movements

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2013
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  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#3 of 1,475)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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1486 X users
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6 Wikipedia pages
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1 Redditor

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68 Mendeley
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Title
Kinematic decomposition and classification of octopus arm movements
Published in
Frontiers in Computational Neuroscience, January 2013
DOI 10.3389/fncom.2013.00060
Pubmed ID
Authors

Ido Zelman, Myriam Titon, Yoram Yekutieli, Shlomi Hanassy, Binyamin Hochner, Tamar Flash

Abstract

The octopus arm is a muscular hydrostat and due to its deformable and highly flexible structure it is capable of a rich repertoire of motor behaviors. Its motor control system uses planning principles and control strategies unique to muscular hydrostats. We previously reconstructed a data set of octopus arm movements from records of natural movements using a sequence of 3D curves describing the virtual backbone of arm configurations. Here we describe a novel representation of octopus arm movements in which a movement is characterized by a pair of surfaces that represent the curvature and torsion values of points along the arm as a function of time. This representation allowed us to explore whether the movements are built up of elementary kinematic units by decomposing each surface into a weighted combination of 2D Gaussian functions. The resulting Gaussian functions can be considered as motion primitives at the kinematic level of octopus arm movements. These can be used to examine underlying principles of movement generation. Here we used combination of such kinematic primitives to decompose different octopus arm movements and characterize several movement prototypes according to their composition. The representation and methodology can be applied to the movement of any organ which can be modeled by means of a continuous 3D curve.

X Demographics

X Demographics

The data shown below were collected from the profiles of 1,486 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Canada 1 1%
Unknown 65 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 21%
Researcher 10 15%
Student > Master 10 15%
Professor > Associate Professor 5 7%
Student > Bachelor 5 7%
Other 9 13%
Unknown 15 22%
Readers by discipline Count As %
Engineering 26 38%
Agricultural and Biological Sciences 12 18%
Computer Science 4 6%
Neuroscience 4 6%
Physics and Astronomy 2 3%
Other 5 7%
Unknown 15 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 301. 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 19 December 2023.
All research outputs
#117,556
of 25,801,916 outputs
Outputs from Frontiers in Computational Neuroscience
#3
of 1,475 outputs
Outputs of similar age
#648
of 291,259 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#1
of 138 outputs
Altmetric has tracked 25,801,916 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,475 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done particularly well, scoring higher than 99% 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 291,259 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.