Literature DB >> 16207784

Haptic identification of surfaces as fields of force.

Vikram S Chib1, James L Patton, Kevin M Lynch, Ferdinando A Mussa-Ivaldi.   

Abstract

The ability to discriminate an object's shape and mechanical properties from touch is one of the most fundamental somatosensory functions. When exploring physical properties of an object, such as stiffness and curvature, humans probe the object's surface and obtain information from the many sensory receptors in their upper limbs. This sensory information is critical for the guidance of actions. We studied how humans acquire an internal representation of the shape and mechanical properties of surfaces and how this information affects the execution of trajectories over the surface. Experiments involved subjects executing trajectories while holding a planar manipulandum that renders planar virtual objects with variable shape and mechanical properties. Subjects were instructed to make reaching movements with the hand between points on the boundary of a curved virtual disk of varying stiffness and curvature. The results suggest two classifications of adaptive responses: force perturbations and object boundaries. In the first case, a rectilinear hand movement is enforced by opposing the interaction forces. In the second case, the trajectory conforms to the object boundary so as to reduce interaction forces. While this dichotomy is evident for very rigid and very soft objects, the likelihood of an object boundary classification depended, in a smooth and monotonic way, on the average force experienced during the initial movements. Furthermore, the observed response across a variety of stiffness values lead to a constant average interaction force after adaptation. This suggests that the nervous system may select from the two responses through a mechanism that attempts to establish a constant interaction force.

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Year:  2005        PMID: 16207784     DOI: 10.1152/jn.00610.2005

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  8 in total

1.  Motor adaptation as a process of reoptimization.

Authors:  Jun Izawa; Tushar Rane; Opher Donchin; Reza Shadmehr
Journal:  J Neurosci       Date:  2008-03-12       Impact factor: 6.167

2.  Experimental measure of arm stiffness during single reaching movements with a time-frequency analysis.

Authors:  Davide Piovesan; Alberto Pierobon; Paul DiZio; James R Lackner
Journal:  J Neurophysiol       Date:  2013-08-14       Impact factor: 2.714

3.  Vestibular benefits to task savings in motor adaptation.

Authors:  A M E Sarwary; L P J Selen; W P Medendorp
Journal:  J Neurophysiol       Date:  2013-06-19       Impact factor: 2.714

4.  Differences in context and feedback result in different trajectories and adaptation strategies in reaching.

Authors:  Fritzie Arce; Itai Novick; Maayan Shahar; Yuval Link; Claude Ghez; Eilon Vaadia
Journal:  PLoS One       Date:  2009-01-16       Impact factor: 3.240

5.  Estimating Human Wrist Stiffness during a Tooling Task.

Authors:  Gia-Hoang Phan; Clint Hansen; Paolo Tommasino; Aamani Budhota; Dhanya Menoth Mohan; Asif Hussain; Etienne Burdet; Domenico Campolo
Journal:  Sensors (Basel)       Date:  2020-06-08       Impact factor: 3.576

6.  Estimating the sources of motor errors for adaptation and generalization.

Authors:  Max Berniker; Konrad Kording
Journal:  Nat Neurosci       Date:  2008-11-16       Impact factor: 24.884

7.  Reshaping Movement Distributions With Limit-Push Robotic Training.

Authors:  Amit K Shah; Ian Sharp; Eyad Hajissa; James L Patton
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-05-21       Impact factor: 3.802

8.  Switching in Feedforward Control of Grip Force During Tool-Mediated Interaction With Elastic Force Fields.

Authors:  Olivier White; Amir Karniel; Charalambos Papaxanthis; Marie Barbiero; Ilana Nisky
Journal:  Front Neurorobot       Date:  2018-06-07       Impact factor: 2.650

  8 in total

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