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dc.contributor.authorBurdet, E
dc.contributor.authorTee, KP
dc.contributor.authorMareels, I
dc.contributor.authorMilner, TE
dc.contributor.authorChew, CM
dc.contributor.authorFranklin, DW
dc.contributor.authorOsu, R
dc.contributor.authorKawato, M
dc.date.available2014-05-21T22:47:11Z
dc.date.available2005-09-09
dc.date.available2005-09-09
dc.date.available2005-09-09
dc.date.available2005-09-09
dc.date.issued2006-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000234274100004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationBurdet, E., Tee, K. P., Mareels, I., Milner, T. E., Chew, C. M., Franklin, D. W., Osu, R. & Kawato, M. (2006). Stability and motor adaptation in human arm movements. BIOLOGICAL CYBERNETICS, 94 (1), pp.20-32. https://doi.org/10.1007/s00422-005-0025-9.
dc.identifier.issn0340-1200
dc.identifier.urihttp://hdl.handle.net/11343/29255
dc.description.abstractIn control, stability captures the reproducibility of motions and the robustness to environmental and internal perturbations. This paper examines how stability can be evaluated in human movements, and possible mechanisms by which humans ensure stability. First, a measure of stability is introduced, which is simple to apply to human movements and corresponds to Lyapunov exponents. Its application to real data shows that it is able to distinguish effectively between stable and unstable dynamics. A computational model is then used to investigate stability in human arm movements, which takes into account motor output variability and computes the force to perform a task according to an inverse dynamics model. Simulation results suggest that even a large time delay does not affect movement stability as long as the reflex feedback is small relative to muscle elasticity. Simulations are also used to demonstrate that existing learning schemes, using a monotonic antisymmetric update law, cannot compensate for unstable dynamics. An impedance compensation algorithm is introduced to learn unstable dynamics, which produces similar adaptation responses to those found in experiments.
dc.languageEnglish
dc.publisherSPRINGER
dc.subjectArtificial Intelligence and Image Processing
dc.titleStability and motor adaptation in human arm movements
dc.typeJournal Article
dc.identifier.doi10.1007/s00422-005-0025-9
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentElectrical and Electronic Engineering
melbourne.source.titleBIOLOGICAL CYBERNETICS
melbourne.source.volume94
melbourne.source.issue1
melbourne.source.pages20-32
dc.research.codefor801
dc.description.pagestart20
melbourne.publicationid52663
melbourne.elementsid277684
melbourne.contributor.authorMareels, Iven
dc.identifier.eissn1432-0770
melbourne.accessrightsThis item is currently not available from this repository


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