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Analysis of Accuracy in Pointing with Redundant Hand-held Tools: A Geometric Approach to the Uncontrolled Manifold Method

Overview of attention for article published in PLoS Computational Biology, April 2013
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Title
Analysis of Accuracy in Pointing with Redundant Hand-held Tools: A Geometric Approach to the Uncontrolled Manifold Method
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1002978
Pubmed ID
Authors

Domenico Campolo, Ferdinan Widjaja, Hong Xu, Wei Tech Ang, Etienne Burdet

Abstract

This work introduces a coordinate-independent method to analyse movement variability of tasks performed with hand-held tools, such as a pen or a surgical scalpel. We extend the classical uncontrolled manifold (UCM) approach by exploiting the geometry of rigid body motions, used to describe tool configurations. In particular, we analyse variability during a static pointing task with a hand-held tool, where subjects are asked to keep the tool tip in steady contact with another object. In this case the tool is redundant with respect to the task, as subjects control position/orientation of the tool, i.e. 6 degrees-of-freedom (dof), to maintain the tool tip position (3dof) steady. To test the new method, subjects performed a pointing task with and without arm support. The additional dof introduced in the unsupported condition, injecting more variability into the system, represented a resource to minimise variability in the task space via coordinated motion. The results show that all of the seven subjects channeled more variability along directions not directly affecting the task (UCM), consistent with previous literature but now shown in a coordinate-independent way. Variability in the unsupported condition was only slightly larger at the endpoint but much larger in the UCM.

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Geographical breakdown

Country Count As %
United Kingdom 2 3%
Netherlands 1 2%
Belgium 1 2%
Spain 1 2%
Japan 1 2%
Unknown 60 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 27%
Researcher 10 15%
Student > Master 9 14%
Professor 4 6%
Other 4 6%
Other 9 14%
Unknown 12 18%
Readers by discipline Count As %
Engineering 17 26%
Psychology 9 14%
Medicine and Dentistry 8 12%
Sports and Recreations 6 9%
Neuroscience 4 6%
Other 6 9%
Unknown 16 24%