Predicting body movements for person identification under different walking conditions

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dc.contributor.authorNguyen, Duc-Phongko
dc.contributor.authorPhan, Cong-Boko
dc.contributor.authorKoo, Seungbumko
dc.date.accessioned2019-01-22T08:32:38Z-
dc.date.available2019-01-22T08:32:38Z-
dc.date.created2018-12-15-
dc.date.created2018-12-15-
dc.date.issued2018-09-
dc.identifier.citationFORENSIC SCIENCE INTERNATIONAL, v.290, pp.303 - 309-
dc.identifier.issn0379-0738-
dc.identifier.urihttp://hdl.handle.net/10203/249039-
dc.description.abstractHuman motion during walking provides biometric information which can be utilized to quantify the similarity between two persons or identify a person. The purpose of this study was to develop a method for identifying a person using their walking motion when another walking motion under different conditions is given. This type of situation occurs frequently in forensic gait science. Twenty-eight subjects were asked to walk in a gait laboratory, and the positions of their joints were tracked using a three-dimensional motion capture system. The subjects repeated their walking motion both without a weight and with a tote bag weighing a total of 5% of their body weight in their right hand. The positions of 17 anatomical landmarks during two cycles of a gait trial were generated to form a gait vector. We developed two different linear transformation methods to determine the functional relationship between the normal gait vectors and the tote-bag gait vectors from the collected gait data, one using linear transformations and the other using partial least squares regression. These methods were validated by predicting the tote-bag gait vector given a normal gait vector of a person, accomplished by calculating the Euclidean distance between the predicted vector to the measured tote-bag gait vector of the same person. The mean values of the prediction scores for the two methods were 96.4 and 95.0, respectively. This study demonstrated the potential for identifying a person based on their walking motion, even under different walking conditions.-
dc.languageEnglish-
dc.publisherELSEVIER IRELAND LTD-
dc.titlePredicting body movements for person identification under different walking conditions-
dc.typeArticle-
dc.identifier.wosid000443355600044-
dc.identifier.scopusid2-s2.0-85051269827-
dc.type.rimsART-
dc.citation.volume290-
dc.citation.beginningpage303-
dc.citation.endingpage309-
dc.citation.publicationnameFORENSIC SCIENCE INTERNATIONAL-
dc.identifier.doi10.1016/j.forsciint.2018.07.022-
dc.contributor.localauthorKoo, Seungbum-
dc.contributor.nonIdAuthorNguyen, Duc-Phong-
dc.contributor.nonIdAuthorPhan, Cong-Bo-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorGait identification-
dc.subject.keywordAuthorWalking-
dc.subject.keywordAuthorHuman movement prediction-
dc.subject.keywordAuthorLinear transformation-
dc.subject.keywordAuthorPrincipal component analysis-
dc.subject.keywordAuthorPartial least squares regression-
dc.subject.keywordPlusHUMAN MOTION-
dc.subject.keywordPlusHUMAN GAIT-
dc.subject.keywordPlusRECOGNITION-
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ME-Journal Papers(저널논문)
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