DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nguyen, Duc-Phong | ko |
dc.contributor.author | Phan, Cong-Bo | ko |
dc.contributor.author | Koo, Seungbum | ko |
dc.date.accessioned | 2019-01-22T08:32:38Z | - |
dc.date.available | 2019-01-22T08:32:38Z | - |
dc.date.created | 2018-12-15 | - |
dc.date.created | 2018-12-15 | - |
dc.date.created | 2018-12-15 | - |
dc.date.issued | 2018-09 | - |
dc.identifier.citation | FORENSIC SCIENCE INTERNATIONAL, v.290, pp.303 - 309 | - |
dc.identifier.issn | 0379-0738 | - |
dc.identifier.uri | http://hdl.handle.net/10203/249039 | - |
dc.description.abstract | Human 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.language | English | - |
dc.publisher | ELSEVIER IRELAND LTD | - |
dc.title | Predicting body movements for person identification under different walking conditions | - |
dc.type | Article | - |
dc.identifier.wosid | 000443355600044 | - |
dc.identifier.scopusid | 2-s2.0-85051269827 | - |
dc.type.rims | ART | - |
dc.citation.volume | 290 | - |
dc.citation.beginningpage | 303 | - |
dc.citation.endingpage | 309 | - |
dc.citation.publicationname | FORENSIC SCIENCE INTERNATIONAL | - |
dc.identifier.doi | 10.1016/j.forsciint.2018.07.022 | - |
dc.contributor.localauthor | Koo, Seungbum | - |
dc.contributor.nonIdAuthor | Nguyen, Duc-Phong | - |
dc.contributor.nonIdAuthor | Phan, Cong-Bo | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Gait identification | - |
dc.subject.keywordAuthor | Walking | - |
dc.subject.keywordAuthor | Human movement prediction | - |
dc.subject.keywordAuthor | Linear transformation | - |
dc.subject.keywordAuthor | Principal component analysis | - |
dc.subject.keywordAuthor | Partial least squares regression | - |
dc.subject.keywordPlus | HUMAN MOTION | - |
dc.subject.keywordPlus | HUMAN GAIT | - |
dc.subject.keywordPlus | RECOGNITION | - |
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