Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts
RGB-D sensors can collect postural data in an automatized way. However, the application of these devices in real work environments requires overcoming problems such as lack of accuracy or body parts' occlusion. This work presents the use of RGB-D sensors and genetic algorithms for the optimizat...
- Autores:
-
Diego-Mas, Jose Antonio
Poveda-Bautista, Rocío
Garzón Leal, Diana Carolina
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Universidad El Bosque
- Repositorio:
- Repositorio U. El Bosque
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unbosque.edu.co:20.500.12495/3509
- Acceso en línea:
- http://hdl.handle.net/20.500.12495/3509
https://doi.org/10.1016/j.apergo.2017.01.012
https://repositorio.unbosque.edu.co
- Palabra clave:
- Grupos profesionales
Ergonomía
Lugar de trabajo
RGB-D sensors
Workstation layout
Genetic algorithms
- Rights
- openAccess
- License
- Acceso abierto
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oai:repositorio.unbosque.edu.co:20.500.12495/3509 |
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Repositorio U. El Bosque |
repository_id_str |
|
dc.title.spa.fl_str_mv |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts |
dc.title.translated.spa.fl_str_mv |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts |
title |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts |
spellingShingle |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts Grupos profesionales Ergonomía Lugar de trabajo RGB-D sensors Workstation layout Genetic algorithms |
title_short |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts |
title_full |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts |
title_fullStr |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts |
title_full_unstemmed |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts |
title_sort |
Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts |
dc.creator.fl_str_mv |
Diego-Mas, Jose Antonio Poveda-Bautista, Rocío Garzón Leal, Diana Carolina |
dc.contributor.author.none.fl_str_mv |
Diego-Mas, Jose Antonio Poveda-Bautista, Rocío Garzón Leal, Diana Carolina |
dc.contributor.orcid.none.fl_str_mv |
Garzón Leal, Diana Carolina [0000-0002-9428-423X] |
dc.subject.decs.spa.fl_str_mv |
Grupos profesionales Ergonomía Lugar de trabajo |
topic |
Grupos profesionales Ergonomía Lugar de trabajo RGB-D sensors Workstation layout Genetic algorithms |
dc.subject.keywords.spa.fl_str_mv |
RGB-D sensors Workstation layout Genetic algorithms |
description |
RGB-D sensors can collect postural data in an automatized way. However, the application of these devices in real work environments requires overcoming problems such as lack of accuracy or body parts' occlusion. This work presents the use of RGB-D sensors and genetic algorithms for the optimization of workstation layouts. RGB-D sensors are used to capture workers' movements when they reach objects on workbenches. Collected data are then used to optimize workstation layout by means of genetic algorithms considering multiple ergonomic criteria. Results show that typical drawbacks of using RGB-D sensors for body tracking are not a problem for this application, and that the combination with intelligent algorithms can automatize the layout design process. The procedure described can be used to automatically suggest new layouts when workers or processes of production change, to adapt layouts to specific workers based on their ways to do the tasks, or to obtain layouts simultaneously optimized for several production processes. |
publishDate |
2017 |
dc.date.issued.none.fl_str_mv |
2017 |
dc.date.accessioned.none.fl_str_mv |
2020-07-15T22:02:06Z |
dc.date.available.none.fl_str_mv |
2020-07-15T22:02:06Z |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.local.none.fl_str_mv |
Artículo de revista |
dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
format |
http://purl.org/coar/resource_type/c_6501 |
dc.identifier.issn.none.fl_str_mv |
1872-9126 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12495/3509 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1016/j.apergo.2017.01.012 |
dc.identifier.instname.spa.fl_str_mv |
instname:Universidad El Bosque |
dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Universidad El Bosque |
dc.identifier.repourl.none.fl_str_mv |
https://repositorio.unbosque.edu.co |
identifier_str_mv |
1872-9126 instname:Universidad El Bosque reponame:Repositorio Institucional Universidad El Bosque |
url |
http://hdl.handle.net/20.500.12495/3509 https://doi.org/10.1016/j.apergo.2017.01.012 https://repositorio.unbosque.edu.co |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofseries.spa.fl_str_mv |
Applied ergonomics, 1872-9126, Vol. 65, 2017, p. 530-540 |
dc.relation.uri.none.fl_str_mv |
https://www.sciencedirect.com/science/article/abs/pii/S0003687017300200?via%3Dihub |
dc.rights.local.spa.fl_str_mv |
Acceso abierto |
dc.rights.accessrights.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess Acceso abierto |
dc.rights.creativecommons.none.fl_str_mv |
2017-11 |
rights_invalid_str_mv |
Acceso abierto http://purl.org/coar/access_right/c_abf2 2017-11 |
eu_rights_str_mv |
openAccess |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Elsevier |
dc.publisher.journal.spa.fl_str_mv |
Applied ergonomics |
institution |
Universidad El Bosque |
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Diego-Mas, Jose AntonioPoveda-Bautista, RocíoGarzón Leal, Diana CarolinaGarzón Leal, Diana Carolina [0000-0002-9428-423X]2020-07-15T22:02:06Z2020-07-15T22:02:06Z20171872-9126http://hdl.handle.net/20.500.12495/3509https://doi.org/10.1016/j.apergo.2017.01.012instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquehttps://repositorio.unbosque.edu.coapplication/pdfengElsevierApplied ergonomicsApplied ergonomics, 1872-9126, Vol. 65, 2017, p. 530-540https://www.sciencedirect.com/science/article/abs/pii/S0003687017300200?via%3DihubUsing RGB-D sensors and evolutionary algorithms for the optimization of workstation layoutsUsing RGB-D sensors and evolutionary algorithms for the optimization of workstation layoutsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Grupos profesionalesErgonomíaLugar de trabajoRGB-D sensorsWorkstation layoutGenetic algorithmsRGB-D sensors can collect postural data in an automatized way. However, the application of these devices in real work environments requires overcoming problems such as lack of accuracy or body parts' occlusion. This work presents the use of RGB-D sensors and genetic algorithms for the optimization of workstation layouts. RGB-D sensors are used to capture workers' movements when they reach objects on workbenches. Collected data are then used to optimize workstation layout by means of genetic algorithms considering multiple ergonomic criteria. Results show that typical drawbacks of using RGB-D sensors for body tracking are not a problem for this application, and that the combination with intelligent algorithms can automatize the layout design process. The procedure described can be used to automatically suggest new layouts when workers or processes of production change, to adapt layouts to specific workers based on their ways to do the tasks, or to obtain layouts simultaneously optimized for several production processes.Acceso abiertohttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessAcceso abierto2017-11ORIGINALJosé Antonio, Diego-Mas Rocío, Poveda-Bautista. Diana Garzón-Leal_2017.pdfJosé Antonio, Diego-Mas Rocío, Poveda-Bautista. Diana Garzón-Leal_2017.pdfapplication/pdf1826917http://18.204.144.38/bitstreams/89cfefd7-3864-4a33-8d50-f705792a6448/download1f22e0a754885db92c9a69b0ac25031cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://18.204.144.38/bitstreams/4f7a8a3b-d420-4c47-9ff5-adb660d4dd26/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILAntonio, Diego-Mas Rocío, Poveda-Bautista. Diana Garzón-Leal_2017.pdf.jpgAntonio, Diego-Mas Rocío, Poveda-Bautista. 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