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pubmed-article:1921363pubmed:abstractTextA differentiation method, which combines the concepts of least squares and splines, has been developed to analyze human motion data. This data smoothing technique is not dependent on a choice of a cut-off frequency and yet it closely reflects the nature of the phenomenon. Two sets of published benchmark data were used to evaluate the new algorithm.lld:pubmed
pubmed-article:1921363pubmed:languageenglld:pubmed
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pubmed-article:1921363pubmed:statusMEDLINElld:pubmed
pubmed-article:1921363pubmed:monthAuglld:pubmed
pubmed-article:1921363pubmed:issn0148-0731lld:pubmed
pubmed-article:1921363pubmed:authorpubmed-author:YangK HKHlld:pubmed
pubmed-article:1921363pubmed:authorpubmed-author:SimonsWWlld:pubmed
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pubmed-article:1921363pubmed:volume113lld:pubmed
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pubmed-article:1921363pubmed:pagination348-51lld:pubmed
pubmed-article:1921363pubmed:dateRevised2004-11-17lld:pubmed
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pubmed-article:1921363pubmed:articleTitleDifferentiation of human motion data using combined spline and least squares concepts.lld:pubmed
pubmed-article:1921363pubmed:affiliationDepartment of Mathematics, West Virginia University, Morgantown 26506.lld:pubmed
pubmed-article:1921363pubmed:publicationTypeJournal Articlelld:pubmed
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