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Affine differential geometry analysis of human arm movements

Overview of attention for article published in Biological Cybernetics, April 2007
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  • Good Attention Score compared to outputs of the same age (66th percentile)

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1 X user
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101 Mendeley
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1 CiteULike
Title
Affine differential geometry analysis of human arm movements
Published in
Biological Cybernetics, April 2007
DOI 10.1007/s00422-007-0145-5
Pubmed ID
Authors

Tamar Flash, Amir A. Handzel

Abstract

Humans interact with their environment through sensory information and motor actions. These interactions may be understood via the underlying geometry of both perception and action. While the motor space is typically considered by default to be Euclidean, persistent behavioral observations point to a different underlying geometric structure. These observed regularities include the "two-thirds power law", which connects path curvature with velocity, and "local isochrony", which prescribes the relation between movement time and its extent. Starting with these empirical observations, we have developed a mathematical framework based on differential geometry, Lie group theory and Cartan's moving frame method for the analysis of human hand trajectories. We also use this method to identify possible motion primitives, i.e., elementary building blocks from which more complicated movements are constructed. We show that a natural geometric description of continuous repetitive hand trajectories is not Euclidean but equi-affine. Specifically, equi-affine velocity is piecewise constant along movement segments, and movement execution time for a given segment is proportional to its equi-affine arc-length. Using this mathematical framework, we then analyze experimentally recorded drawing movements. To examine movement segmentation and classification, the two fundamental equi-affine differential invariants-equi-affine arc-length and curvature are calculated for the recorded movements. We also discuss the possible role of conic sections, i.e., curves with constant equi-affine curvature, as motor primitives and focus in more detail on parabolas, the equi-affine geodesics. Finally, we explore possible schemes for the internal neural coding of motor commands by showing that the equi-affine framework is compatible with the common model of population coding of the hand velocity vector when combined with a simple assumption on its dynamics. We then discuss several alternative explanations for the role that the equi-affine metric may play in internal representations of motion perception and production.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 2%
France 1 <1%
Bulgaria 1 <1%
Portugal 1 <1%
Brazil 1 <1%
Belgium 1 <1%
Romania 1 <1%
United States 1 <1%
Unknown 92 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 23%
Student > Ph. D. Student 21 21%
Professor > Associate Professor 11 11%
Student > Master 11 11%
Professor 10 10%
Other 17 17%
Unknown 8 8%
Readers by discipline Count As %
Engineering 19 19%
Neuroscience 16 16%
Agricultural and Biological Sciences 13 13%
Computer Science 12 12%
Physics and Astronomy 9 9%
Other 18 18%
Unknown 14 14%
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 24 August 2022.
All research outputs
#6,536,791
of 23,164,913 outputs
Outputs from Biological Cybernetics
#164
of 681 outputs
Outputs of similar age
#24,317
of 76,903 outputs
Outputs of similar age from Biological Cybernetics
#1
of 3 outputs
Altmetric has tracked 23,164,913 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 681 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 74% 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 76,903 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 66% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them