A computer vision based method for 3D posture estimation of symmetrical lifting
Work-related musculoskeletal disorders (WMSD) are commonly observed among the workers involved in material handling tasks such as lifting. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been...
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| Veröffentlicht in: | Journal of biomechanics 2018-03, Vol.69, p.40-46 |
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| creator | Mehrizi, Rahil Peng, Xi Xu, Xu Zhang, Shaoting Metaxas, Dimitris Li, Kang |
| description | Work-related musculoskeletal disorders (WMSD) are commonly observed among the workers involved in material handling tasks such as lifting. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been mainly conducted using surface marker-based methods, which is time consuming and tedious. During the past decade, computer vision based pose estimation techniques have gained an increasing interest and may be a viable alternative for surface marker-based human movement analysis. The aim of this study is to develop and validate a computer vision based marker-less motion capture method to assess 3D joint kinematics of lifting tasks. Twelve subjects performing three types of symmetrical lifting tasks were filmed from two views using optical cameras. The joints kinematics were calculated by the proposed computer vision based motion capture method as well as a surface marker-based motion capture method. The joint kinematics estimated from the computer vision based method were practically comparable to the joint kinematics obtained by the surface marker-based method. The mean and standard deviation of the difference between the joint angles estimated by the computer vision based method and these obtained by the surface marker-based method was 2.31 ± 4.00°. One potential application of the proposed computer vision based marker-less method is to noninvasively assess 3D joint kinematics of industrial tasks such as lifting. |
| doi_str_mv | 10.1016/j.jbiomech.2018.01.012 |
| format | Article |
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To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been mainly conducted using surface marker-based methods, which is time consuming and tedious. During the past decade, computer vision based pose estimation techniques have gained an increasing interest and may be a viable alternative for surface marker-based human movement analysis. The aim of this study is to develop and validate a computer vision based marker-less motion capture method to assess 3D joint kinematics of lifting tasks. Twelve subjects performing three types of symmetrical lifting tasks were filmed from two views using optical cameras. The joints kinematics were calculated by the proposed computer vision based motion capture method as well as a surface marker-based motion capture method. The joint kinematics estimated from the computer vision based method were practically comparable to the joint kinematics obtained by the surface marker-based method. The mean and standard deviation of the difference between the joint angles estimated by the computer vision based method and these obtained by the surface marker-based method was 2.31 ± 4.00°. One potential application of the proposed computer vision based marker-less method is to noninvasively assess 3D joint kinematics of industrial tasks such as lifting.</description><identifier>ISSN: 0021-9290</identifier><identifier>EISSN: 1873-2380</identifier><identifier>DOI: 10.1016/j.jbiomech.2018.01.012</identifier><identifier>PMID: 29398001</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Algorithms ; Biomechanical Phenomena ; Biomechanics ; Camcorders ; Cameras ; Computer vision ; Discriminative approach ; Female ; Histograms ; Hoisting ; Human mechanics ; Human motion ; Humans ; Joint kinematics assessment ; Joints - physiology ; Kinematics ; Lifting ; Male ; Marker-less motion capture ; Materials handling ; Methods ; Middle Aged ; Motion capture ; Movement ; Musculoskeletal diseases ; Pattern recognition ; Photography ; Posture ; Studies ; Surface markers ; Workers</subject><ispartof>Journal of biomechanics, 2018-03, Vol.69, p.40-46</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright © 2018 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Limited Mar 1, 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c459t-b52faf7bce4a148010afebe5c2a117136d70880ca50e66780ef98136bebbc9673</citedby><cites>FETCH-LOGICAL-c459t-b52faf7bce4a148010afebe5c2a117136d70880ca50e66780ef98136bebbc9673</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2001518526?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72341</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29398001$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mehrizi, Rahil</creatorcontrib><creatorcontrib>Peng, Xi</creatorcontrib><creatorcontrib>Xu, Xu</creatorcontrib><creatorcontrib>Zhang, Shaoting</creatorcontrib><creatorcontrib>Metaxas, Dimitris</creatorcontrib><creatorcontrib>Li, Kang</creatorcontrib><title>A computer vision based method for 3D posture estimation of symmetrical lifting</title><title>Journal of biomechanics</title><addtitle>J Biomech</addtitle><description>Work-related musculoskeletal disorders (WMSD) are commonly observed among the workers involved in material handling tasks such as lifting. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been mainly conducted using surface marker-based methods, which is time consuming and tedious. During the past decade, computer vision based pose estimation techniques have gained an increasing interest and may be a viable alternative for surface marker-based human movement analysis. The aim of this study is to develop and validate a computer vision based marker-less motion capture method to assess 3D joint kinematics of lifting tasks. Twelve subjects performing three types of symmetrical lifting tasks were filmed from two views using optical cameras. The joints kinematics were calculated by the proposed computer vision based motion capture method as well as a surface marker-based motion capture method. The joint kinematics estimated from the computer vision based method were practically comparable to the joint kinematics obtained by the surface marker-based method. The mean and standard deviation of the difference between the joint angles estimated by the computer vision based method and these obtained by the surface marker-based method was 2.31 ± 4.00°. One potential application of the proposed computer vision based marker-less method is to noninvasively assess 3D joint kinematics of industrial tasks such as lifting.</description><subject>Algorithms</subject><subject>Biomechanical Phenomena</subject><subject>Biomechanics</subject><subject>Camcorders</subject><subject>Cameras</subject><subject>Computer vision</subject><subject>Discriminative approach</subject><subject>Female</subject><subject>Histograms</subject><subject>Hoisting</subject><subject>Human mechanics</subject><subject>Human motion</subject><subject>Humans</subject><subject>Joint kinematics assessment</subject><subject>Joints - physiology</subject><subject>Kinematics</subject><subject>Lifting</subject><subject>Male</subject><subject>Marker-less motion capture</subject><subject>Materials handling</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Motion capture</subject><subject>Movement</subject><subject>Musculoskeletal diseases</subject><subject>Pattern recognition</subject><subject>Photography</subject><subject>Posture</subject><subject>Studies</subject><subject>Surface markers</subject><subject>Workers</subject><issn>0021-9290</issn><issn>1873-2380</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkM1rFTEQwIMo9rX6L5SAFy_7nMl-JTdLq1Yo9KLnkGQnNsvu5pnsFvrfm8drPXgRBgaG33z9GLtE2CNg92ncjzbEmdzDXgDKPWAJ8YrtUPZ1JWoJr9kOQGClhIIzdp7zCAB906u37EyoWkkA3LH7K-7ifNhWSvwx5BAXbk2mgc-0PsSB-5h4fcMPMa9bIk55DbNZj1j0PD_NBUvBmYlPwa9h-fWOvfFmyvT-OV-wn1-__Li-re7uv32_vrqrXNOqtbKt8Mb31lFjsJGAYDxZap0wiD3W3dCDlOBMC9R1vQTySpayJWud6vr6gn08zT2k-HsrZ-k5ZEfTZBaKW9aoVFN3tWqO6Id_0DFuaSnXaVEctChb0RWqO1EuxZwTeX1I5dX0pBH0Ubke9YtyfVSuAUuI0nj5PH6zMw1_214cF-DzCaDi4zFQ0tkFWhwNIZFb9RDD_3b8AZAFlR8</recordid><startdate>20180301</startdate><enddate>20180301</enddate><creator>Mehrizi, Rahil</creator><creator>Peng, Xi</creator><creator>Xu, Xu</creator><creator>Zhang, Shaoting</creator><creator>Metaxas, Dimitris</creator><creator>Li, Kang</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7TB</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20180301</creationdate><title>A computer vision based method for 3D posture estimation of symmetrical lifting</title><author>Mehrizi, Rahil ; Peng, Xi ; Xu, Xu ; Zhang, Shaoting ; Metaxas, Dimitris ; Li, Kang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c459t-b52faf7bce4a148010afebe5c2a117136d70880ca50e66780ef98136bebbc9673</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Biomechanical Phenomena</topic><topic>Biomechanics</topic><topic>Camcorders</topic><topic>Cameras</topic><topic>Computer vision</topic><topic>Discriminative approach</topic><topic>Female</topic><topic>Histograms</topic><topic>Hoisting</topic><topic>Human mechanics</topic><topic>Human motion</topic><topic>Humans</topic><topic>Joint kinematics assessment</topic><topic>Joints - physiology</topic><topic>Kinematics</topic><topic>Lifting</topic><topic>Male</topic><topic>Marker-less motion capture</topic><topic>Materials handling</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Motion capture</topic><topic>Movement</topic><topic>Musculoskeletal diseases</topic><topic>Pattern recognition</topic><topic>Photography</topic><topic>Posture</topic><topic>Studies</topic><topic>Surface markers</topic><topic>Workers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mehrizi, Rahil</creatorcontrib><creatorcontrib>Peng, Xi</creatorcontrib><creatorcontrib>Xu, Xu</creatorcontrib><creatorcontrib>Zhang, Shaoting</creatorcontrib><creatorcontrib>Metaxas, Dimitris</creatorcontrib><creatorcontrib>Li, Kang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of biomechanics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mehrizi, Rahil</au><au>Peng, Xi</au><au>Xu, Xu</au><au>Zhang, Shaoting</au><au>Metaxas, Dimitris</au><au>Li, Kang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A computer vision based method for 3D posture estimation of symmetrical lifting</atitle><jtitle>Journal of biomechanics</jtitle><addtitle>J Biomech</addtitle><date>2018-03-01</date><risdate>2018</risdate><volume>69</volume><spage>40</spage><epage>46</epage><pages>40-46</pages><issn>0021-9290</issn><eissn>1873-2380</eissn><abstract>Work-related musculoskeletal disorders (WMSD) are commonly observed among the workers involved in material handling tasks such as lifting. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been mainly conducted using surface marker-based methods, which is time consuming and tedious. During the past decade, computer vision based pose estimation techniques have gained an increasing interest and may be a viable alternative for surface marker-based human movement analysis. The aim of this study is to develop and validate a computer vision based marker-less motion capture method to assess 3D joint kinematics of lifting tasks. Twelve subjects performing three types of symmetrical lifting tasks were filmed from two views using optical cameras. The joints kinematics were calculated by the proposed computer vision based motion capture method as well as a surface marker-based motion capture method. The joint kinematics estimated from the computer vision based method were practically comparable to the joint kinematics obtained by the surface marker-based method. The mean and standard deviation of the difference between the joint angles estimated by the computer vision based method and these obtained by the surface marker-based method was 2.31 ± 4.00°. One potential application of the proposed computer vision based marker-less method is to noninvasively assess 3D joint kinematics of industrial tasks such as lifting.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>29398001</pmid><doi>10.1016/j.jbiomech.2018.01.012</doi><tpages>7</tpages></addata></record> |
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| ispartof | Journal of biomechanics, 2018-03, Vol.69, p.40-46 |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present); ProQuest Central UK/Ireland |
| subjects | Algorithms Biomechanical Phenomena Biomechanics Camcorders Cameras Computer vision Discriminative approach Female Histograms Hoisting Human mechanics Human motion Humans Joint kinematics assessment Joints - physiology Kinematics Lifting Male Marker-less motion capture Materials handling Methods Middle Aged Motion capture Movement Musculoskeletal diseases Pattern recognition Photography Posture Studies Surface markers Workers |
| title | A computer vision based method for 3D posture estimation of symmetrical lifting |
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