Breaking it down is better: haptic decomposition of complex movements aids in robot-assisted motor learning.
Identifieur interne : 000B83 ( PubMed/Checkpoint ); précédent : 000B82; suivant : 000B84Breaking it down is better: haptic decomposition of complex movements aids in robot-assisted motor learning.
Auteurs : Julius Klein [États-Unis] ; 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 [ 1558-0210 ] ; 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.
DOI: 10.1109/TNSRE.2012.2195202
PubMed: 22531825
Affiliations:
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pubmed:22531825Le document en format XML
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<front><div type="abstract" xml:lang="en">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.</div>
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<CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>J Neurophysiol. 2003 Apr;89(4):2279-88</RefSource>
<PMID Version="1">12612022</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Res Q Exerc Sport. 2003 Mar;74(1):104-9</RefSource>
<PMID Version="1">12659481</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Exp Psychol Learn Mem Cogn. 1983 Apr;9(2):334-45</RefSource>
<PMID Version="1">6222149</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Med Biol Eng Comput. 1987 Sep;25(5):527-32</RefSource>
<PMID Version="1">3446974</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Brain Cogn. 1988 Dec;8(3):326-47</RefSource>
<PMID Version="1">3214589</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Neurophysiol. 1989 Aug;62(2):582-94</RefSource>
<PMID Version="1">2769349</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Neurosci. 1994 May;14(5 Pt 2):3208-24</RefSource>
<PMID Version="1">8182467</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Res Q Exerc Sport. 1994 Dec;65(4):316-23</RefSource>
<PMID Version="1">7886280</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Neurophysiol. 1997 Feb;77(2):826-52</RefSource>
<PMID Version="1">9065853</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Exp Psychol. 1954 Nov;48(5):375-80</RefSource>
<PMID Version="1">13221731</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Med Biol Eng Comput. 2005 Jan;43(1):2-10</RefSource>
<PMID Version="1">15742713</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Res Q Exerc Sport. 2005 Mar;76(1):60-6</RefSource>
<PMID Version="1">15810771</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Am J Surg. 2005 Sep;190(3):359-63</RefSource>
<PMID Version="1">16105518</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Stroke. 2005 Sep;36(9):1960-6</RefSource>
<PMID Version="1">16109908</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Mot Behav. 2007 Jan;39(1):40-8</RefSource>
<PMID Version="1">17251170</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Neural Syst Rehabil Eng. 2007 Sep;15(3):336-46</RefSource>
<PMID Version="1">17894266</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>IEEE Trans Neural Syst Rehabil Eng. 2007 Sep;15(3):356-66</RefSource>
<PMID Version="1">17894268</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Conf Proc IEEE Eng Med Biol Soc. 2007;2007:4011-4</RefSource>
<PMID Version="1">18002879</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Brain. 2008 Feb;131(Pt 2):425-37</RefSource>
<PMID Version="1">18156154</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Neurorehabil Neural Repair. 2008 May-Jun;22(3):305-10</RefSource>
<PMID Version="1">18184932</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Mot Behav. 2008 Nov;40(6):545-56</RefSource>
<PMID Version="1">18980907</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Exerc Sport Sci Rev. 2009 Jan;37(1):43-51</RefSource>
<PMID Version="1">19098524</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Neurorehabil Neural Repair. 2009 Jan;23(1):5-13</RefSource>
<PMID Version="1">19109447</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Neuroeng Rehabil. 2009;6:20</RefSource>
<PMID Version="1">19531254</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Physiol Paris. 2009 Sep-Dec;103(3-5):276-85</RefSource>
<PMID Version="1">19665551</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Exp Brain Res. 2010 Mar;201(2):119-31</RefSource>
<PMID Version="1">19787345</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Rehabil Res Dev. 2011;48(4):355-66</RefSource>
<PMID Version="1">21674388</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Sports Sci. 2002 Jun;20(6):495-506</RefSource>
<PMID Version="1">12137179</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Behav Brain Sci. 2001 Oct;24(5):849-78; discussion 878-937</RefSource>
<PMID Version="1">12239891</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>Neurosci Lett. 2003 Mar 6;338(3):205-8</RefSource>
<PMID Version="1">12581832</PMID>
</CommentsCorrections>
<CommentsCorrections RefType="Cites"><RefSource>J Mot Behav. 2003 Sep;35(3):296-308</RefSource>
<PMID Version="1">12873844</PMID>
</CommentsCorrections>
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