Breaking It Down Is Better: Haptic Decomposition of Complex Movements Aids in Robot-Assisted Motor Learning
Identifieur interne : 002561 ( Main/Merge ); précédent : 002560; suivant : 002562Breaking It Down Is Better: Haptic Decomposition of Complex Movements Aids in Robot-Assisted Motor Learning
Auteurs : Julius Klein ; Steven J. Spencer ; David J. ReinkensmeyerSource :
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [ 1534-4320 ] ; 2012.
English descriptors
- KwdEn :
- MESH :
- methods : Robotics.
- physiology : Arm, Joints, Learning, Memory, Motor Skills, Movement.
- rehabilitation : Stroke.
- Adult, Algorithms, Biomechanical Phenomena, Data Interpretation, Statistical, Female, Gravitation, Humans, Male.
Abstract
Training with haptic guidance has been proposed as a technique for learning complex movements in rehabilitation and sports, but it is unclear how to best deliver guidance-based training. Here, we hypothesized that breaking down a complex movement, similar to a tennis backhand, into simpler parts and then using haptic feedback from a robotic exoskeleton would help the motor system learn the movement. We also examined how the particular form of the decomposition affected learning. Three groups of unimpaired participants trained with the target arm movement broken down in three ways: 1) elbow flexion/extension and the unified shoulder motion independently (“anatomical” decomposition), 2) three component shoulder motions in Euler coordinates and elbow flexion/extension (“Euler” decomposition), or 3) the motion of the tip of the elbow and motion of the hand with respect to the elbow, independently (“visual” decomposition). A control group practiced the same number of movements, but experienced the target motion only, achieving eight times more direct practice with this motion. Despite less experience with the target motion, part training was better, but only when the arm trajectory was decomposed into anatomical components. Varying robotic movement training to include practice of simpler, anatomically-isolated motions may enhance its efficacy.
Url:
DOI: 10.1109/TNSRE.2012.2195202
PubMed: 22531825
PubMed Central: 4015469
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PMC:4015469Le document en format XML
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Spencer</name></author><author><name sortKey="Reinkensmeyer, David J" sort="Reinkensmeyer, David J" uniqKey="Reinkensmeyer D" first="David J." last="Reinkensmeyer">David J. Reinkensmeyer</name></author></analytic><series><title level="j">IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society</title><idno type="ISSN">1534-4320</idno><idno type="eISSN">1558-0210</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Algorithms</term><term>Arm (physiology)</term><term>Biomechanical Phenomena</term><term>Data Interpretation, Statistical</term><term>Female</term><term>Gravitation</term><term>Humans</term><term>Joints (physiology)</term><term>Learning (physiology)</term><term>Male</term><term>Memory (physiology)</term><term>Motor Skills (physiology)</term><term>Movement (physiology)</term><term>Robotics (methods)</term><term>Stroke (rehabilitation)</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Robotics</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Arm</term><term>Joints</term><term>Learning</term><term>Memory</term><term>Motor Skills</term><term>Movement</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Stroke</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Algorithms</term><term>Biomechanical Phenomena</term><term>Data Interpretation, Statistical</term><term>Female</term><term>Gravitation</term><term>Humans</term><term>Male</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">Training with haptic guidance has been proposed as a technique for learning complex movements in rehabilitation and sports, but it is unclear how to best deliver guidance-based training. Here, we hypothesized that breaking down a complex movement, similar to a tennis backhand, into simpler parts and then using haptic feedback from a robotic exoskeleton would help the motor system learn the movement. We also examined how the particular form of the decomposition affected learning. Three groups of unimpaired participants trained with the target arm movement broken down in three ways: 1) elbow flexion/extension and the unified shoulder motion independently (“anatomical” decomposition), 2) three component shoulder motions in Euler coordinates and elbow flexion/extension (“Euler” decomposition), or 3) the motion of the tip of the elbow and motion of the hand with respect to the elbow, independently (“visual” decomposition). A control group practiced the same number of movements, but experienced the target motion only, achieving eight times more direct practice with this motion. Despite less experience with the target motion, part training was better, but only when the arm trajectory was decomposed into anatomical components. Varying robotic movement training to include practice of simpler, anatomically-isolated motions may enhance its efficacy.</p></div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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