HapticV1, Ncbi, Merge, bibRecord, 001A75

Force field adaptation can be learned using vision in the absence of proprioceptive error.

Identifieur interne : 001A75 ( Ncbi/Merge ); précédent : 001A74; suivant : 001A76

Force field adaptation can be learned using vision in the absence of proprioceptive error.

Auteurs : Alejandro Melendez-Calderon [Royaume-Uni] ; Lorenzo Masia ; Roger Gassert ; Giulio Sandini ; Etienne Burdet

Source :

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [ 1558-0210 ] ; 2011.

RBID : pubmed:21652280

English descriptors

KwdEn :
- Adaptation, Psychological (physiology), Adult, Algorithms, Arm (physiology), Biomechanical Phenomena, Feedback, Sensory (physiology), Female, Hand (physiology), Humans, Learning (physiology), Male, Models, Theoretical, Movement (physiology), Photic Stimulation, Proprioception (physiology), Psychomotor Performance (physiology), Robotics, Visual Perception (physiology), Young Adult.
MESH :
- physiology : Adaptation, Psychological, Arm, Feedback, Sensory, Hand, Learning, Movement, Proprioception, Psychomotor Performance, Visual Perception.
- Adult, Algorithms, Biomechanical Phenomena, Female, Humans, Male, Models, Theoretical, Photic Stimulation, Robotics, Young Adult.

Abstract

It has been shown that people can learn to perform a variety of motor tasks in novel dynamic environments without visual feedback, highlighting the importance of proprioceptive feedback in motor learning. However, our results show that it is possible to learn a viscous curl force field without proprioceptive error to drive adaptation, by providing visual information about the position error. Subjects performed reaching movements in a constraining channel created by a robotic interface. The force that subjects applied against the haptic channel was used to predict the unconstrained hand trajectory under a viscous curl force field. This trajectory was provided as visual feedback to the subjects during movement (virtual dynamics). Subjects were able to use this visual information (discrepant with proprioception) and gradually learned to compensate for the virtual dynamics. Unconstrained catch trials, performed without the haptic channel after learning the virtual dynamics, exhibited similar trajectories to those of subjects who learned to move in the force field in the unconstrained environment. Our results demonstrate that the internal model of the external dynamics that was formed through learning without proprioceptive error was accurate enough to allow compensation for the force field in the unconstrained environment. They suggest a method to overcome limitations in learning resulting from mechanical constraints of robotic trainers by providing suitable visual feedback, potentially enabling efficient physical training and rehabilitation using simple robotic devices with few degrees-of-freedom.

DOI: 10.1109/TNSRE.2011.2125990
PubMed: 21652280

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pubmed:21652280

Le document en format XML

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<author><name sortKey="Melendez Calderon, Alejandro" sort="Melendez Calderon, Alejandro" uniqKey="Melendez Calderon A" first="Alejandro" last="Melendez-Calderon">Alejandro Melendez-Calderon</name>
<affiliation wicri:level="1"><nlm:affiliation>Department of Bioengineering, Imperial College of Science Technology and Medicine, SW72AZ London, UK. amelende@imperial.ac.uk</nlm:affiliation>
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<author><name sortKey="Gassert, Roger" sort="Gassert, Roger" uniqKey="Gassert R" first="Roger" last="Gassert">Roger Gassert</name>
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<author><name sortKey="Sandini, Giulio" sort="Sandini, Giulio" uniqKey="Sandini G" first="Giulio" last="Sandini">Giulio Sandini</name>
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<author><name sortKey="Burdet, Etienne" sort="Burdet, Etienne" uniqKey="Burdet E" first="Etienne" last="Burdet">Etienne Burdet</name>
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<front><div type="abstract" xml:lang="en">It has been shown that people can learn to perform a variety of motor tasks in novel dynamic environments without visual feedback, highlighting the importance of proprioceptive feedback in motor learning. However, our results show that it is possible to learn a viscous curl force field without proprioceptive error to drive adaptation, by providing visual information about the position error. Subjects performed reaching movements in a constraining channel created by a robotic interface. The force that subjects applied against the haptic channel was used to predict the unconstrained hand trajectory under a viscous curl force field. This trajectory was provided as visual feedback to the subjects during movement (virtual dynamics). Subjects were able to use this visual information (discrepant with proprioception) and gradually learned to compensate for the virtual dynamics. Unconstrained catch trials, performed without the haptic channel after learning the virtual dynamics, exhibited similar trajectories to those of subjects who learned to move in the force field in the unconstrained environment. Our results demonstrate that the internal model of the external dynamics that was formed through learning without proprioceptive error was accurate enough to allow compensation for the force field in the unconstrained environment. They suggest a method to overcome limitations in learning resulting from mechanical constraints of robotic trainers by providing suitable visual feedback, potentially enabling efficient physical training and rehabilitation using simple robotic devices with few degrees-of-freedom.</div>
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Force field adaptation can be learned using vision in the absence of proprioceptive error.

Force field adaptation can be learned using vision in the absence of proprioceptive error.

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki