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dc.contributor.authorFederolf, Peter
dc.contributor.authorTecante, Karelia
dc.contributor.authorNigg, Benno
dc.date.accessioned2012-11-15T09:17:40Z
dc.date.available2012-11-15T09:17:40Z
dc.date.issued2012-04
dc.identifierSeksjon for fysisk prestasjonsevne / Department of Physical Performances
dc.identifier.citationJournal of Biomechanics. 2012, 45(7), 1127-1132no_NO
dc.identifier.issn0021-9290
dc.identifier.urihttp://hdl.handle.net/11250/171006
dc.descriptionI Brage finner du siste tekst-versjon av artikkelen, og den kan inneholde ubetydelige forskjeller fra forlagets pdf-versjon. Forlagets pdf-versjon finner du på www.sciencedirect.com: http://dx.doi.org/10.1016/j.jbiomech.2012.02.008 / In Brage you'll find the final text version of the article, and it may contain insignificant differences from the journal's pdf version. The definitive version is available at www.sciencedirect.com: http://dx.doi.org/10.1016/j.jbiomech.2012.02.008no_NO
dc.description.abstractMovement variability has become an important field of research and has been studied to gain a better understanding of the neuro-muscular control of human movements. In addition to studies investigating “amplitude variability” there are a growing number of studies assessing the “temporal variability” in movements by applying non-linear analysis techniques. One limitation of the studies available to date is that they quantify variability features in specific, pre-selected biomechanical or physiological variables. In many cases it remains unclear if and to what degree these pre-selected variables quantify characteristics of the whole body movement. This technical note proposes to combine two analysis techniques that have already been applied for gait analysis in order to quantify variability features in walking with variables whose significance for the whole movements are known. Gait patterns were recorded using a full-body marker set on the subjects whose movements were captured with a standard motion tracing system. For each time frame the coordinates of all markers were interpreted as a high-dimensional “posture vector”. A principal component analysis (PCA) conducted on these posture vectors identified the main one-dimensional movement components of walking. Temporal variability of gait was then quantified by calculating the maximum Lyapunov Exponent (LyE) of these main movement components. The effectiveness of this approach was demonstrated by determining differences in temporal variability between walking in unstable shoes and walking in a normal athletic-type control shoe. Several additional conceptual and practical advantages of this combination of analysis methods were discussed.no_NO
dc.language.isoengno_NO
dc.publisherElsevierno_NO
dc.subjectLyapunov exponentno_NO
dc.subjectprincipal component analysisno_NO
dc.subjectdynamic stabilityno_NO
dc.subjectvariabilityno_NO
dc.subjectgaitno_NO
dc.subjectlocomotionno_NO
dc.subjectkinematicsno_NO
dc.titleA holistic approach to study the temporal variability in gaitno_NO
dc.typeJournal articleno_NO
dc.typePeer reviewedno_NO
dc.subject.nsiVDP::Technology: 500no_NO
dc.source.pagenumber1127-1132no_NO
dc.source.volume45no_NO
dc.source.journalJournal of Biomechanicsno_NO
dc.source.issue7no_NO


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