Gait analysis and identification
An efficient framework were proposed for identifying individuals from gait via feature-based method based on 3D motion capture data. Three different extraction methods were applied to achieve gait signatures. The average identification rate was over 93% with best result close to 100% in a 35 subject...
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| creator | Jie Hong Jinsheng Kang Price, M. E. |
| description | An efficient framework were proposed for identifying individuals from gait via feature-based method based on 3D motion capture data. Three different extraction methods were applied to achieve gait signatures. The average identification rate was over 93% with best result close to 100% in a 35 subject database. In additional, gait attractiveness was analyzed via Principle component analysis and linear regression method. A systematic relationship was found between the motions of individual markers and the attractiveness ratings. In a linear equation, ln(PCA1) and ln(PCA2) predicted ln(attract_value) with reasonable accuracy. |
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E.</creatorcontrib><description>An efficient framework were proposed for identifying individuals from gait via feature-based method based on 3D motion capture data. Three different extraction methods were applied to achieve gait signatures. The average identification rate was over 93% with best result close to 100% in a 35 subject database. In additional, gait attractiveness was analyzed via Principle component analysis and linear regression method. A systematic relationship was found between the motions of individual markers and the attractiveness ratings. In a linear equation, ln(PCA1) and ln(PCA2) predicted ln(attract_value) with reasonable accuracy.</description><identifier>ISBN: 9781467317221</identifier><identifier>ISBN: 1467317225</identifier><identifier>EISBN: 9781908549006</identifier><identifier>EISBN: 1908549009</identifier><language>eng</language><publisher>IEEE</publisher><subject>Equations ; Feature extraction ; Gait identify ; gait signature ; Humans ; Legged locomotion ; Linear regression ; Pattern recognition ; Principal component analysis ; Principle component analysis</subject><ispartof>18th International Conference on Automation and Computing (ICAC), 2012, p.1-6</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6330517$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6330517$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jie Hong</creatorcontrib><creatorcontrib>Jinsheng Kang</creatorcontrib><creatorcontrib>Price, M. E.</creatorcontrib><title>Gait analysis and identification</title><title>18th International Conference on Automation and Computing (ICAC)</title><addtitle>IConAC</addtitle><description>An efficient framework were proposed for identifying individuals from gait via feature-based method based on 3D motion capture data. Three different extraction methods were applied to achieve gait signatures. The average identification rate was over 93% with best result close to 100% in a 35 subject database. In additional, gait attractiveness was analyzed via Principle component analysis and linear regression method. A systematic relationship was found between the motions of individual markers and the attractiveness ratings. In a linear equation, ln(PCA1) and ln(PCA2) predicted ln(attract_value) with reasonable accuracy.</description><subject>Equations</subject><subject>Feature extraction</subject><subject>Gait identify</subject><subject>gait signature</subject><subject>Humans</subject><subject>Legged locomotion</subject><subject>Linear regression</subject><subject>Pattern recognition</subject><subject>Principal component analysis</subject><subject>Principle component analysis</subject><isbn>9781467317221</isbn><isbn>1467317225</isbn><isbn>9781908549006</isbn><isbn>1908549009</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpjZOC1NLcwtDSwMDWxNDAwY4bwTczMjQ3NjYwMORh4i4uzDIDAwtDczMiIk0HBPTGzRCExLzGnsjizGMhIUchMSc0ryUzLTE4syczP42FgTUvMKU7lhdLcDNJuriHOHrqZqamp8QVFmbmJRZXxZsbGBqaG5sb4ZQE_CCro</recordid><startdate>201209</startdate><enddate>201209</enddate><creator>Jie Hong</creator><creator>Jinsheng Kang</creator><creator>Price, M. E.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201209</creationdate><title>Gait analysis and identification</title><author>Jie Hong ; Jinsheng Kang ; Price, M. E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_63305173</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Equations</topic><topic>Feature extraction</topic><topic>Gait identify</topic><topic>gait signature</topic><topic>Humans</topic><topic>Legged locomotion</topic><topic>Linear regression</topic><topic>Pattern recognition</topic><topic>Principal component analysis</topic><topic>Principle component analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Jie Hong</creatorcontrib><creatorcontrib>Jinsheng Kang</creatorcontrib><creatorcontrib>Price, M. E.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jie Hong</au><au>Jinsheng Kang</au><au>Price, M. E.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Gait analysis and identification</atitle><btitle>18th International Conference on Automation and Computing (ICAC)</btitle><stitle>IConAC</stitle><date>2012-09</date><risdate>2012</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><isbn>9781467317221</isbn><isbn>1467317225</isbn><eisbn>9781908549006</eisbn><eisbn>1908549009</eisbn><abstract>An efficient framework were proposed for identifying individuals from gait via feature-based method based on 3D motion capture data. Three different extraction methods were applied to achieve gait signatures. The average identification rate was over 93% with best result close to 100% in a 35 subject database. In additional, gait attractiveness was analyzed via Principle component analysis and linear regression method. A systematic relationship was found between the motions of individual markers and the attractiveness ratings. In a linear equation, ln(PCA1) and ln(PCA2) predicted ln(attract_value) with reasonable accuracy.</abstract><pub>IEEE</pub></addata></record> |
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| identifier | ISBN: 9781467317221 |
| ispartof | 18th International Conference on Automation and Computing (ICAC), 2012, p.1-6 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Equations Feature extraction Gait identify gait signature Humans Legged locomotion Linear regression Pattern recognition Principal component analysis Principle component analysis |
| title | Gait analysis and identification |
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