Automatic estimation of pose and falls in videos using computer vision model

Human pose estimation is defined as the process of locating joints of a person or a crowd given an image or video. Currently, this estimation is widely used for the evaluation of athletes, workers, and the monitoring of patients in clinical settings. However, human pose estimation is not an easy tas...

Full description

Autores:
Calvache, Daniela
Bernal, Hernán
Guarín, Juan F.
Aguía, Karen
Orjuela Cañón, Álvaro D.
Perdomo, Oscar J.
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
eng
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/3292
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/3292
https://repositorio.escuelaing.edu.co/
Palabra clave:
Tecnología médica
Medical technology
Monitoreo del paciente - Equipo y accesorios
Patient monitoring - Equipment and supplies
Trastornos de la postura
Posture disorders
Caídas (Accidentes)
Falls (Accidents)
Aprendizaje profundo
Estimación de la postura humana
Detección de caídas
Visión por computadora
Procesamiento de video digital
Deep learning
Human pose estimation
Fall detection
Computer vision
Digital video processing
Rights
closedAccess
License
http://purl.org/coar/access_right/c_14cb
id ESCUELAIG2_6a4dd39a2ed9dcc8dbb693c1994780a7
oai_identifier_str oai:repositorio.escuelaing.edu.co:001/3292
network_acronym_str ESCUELAIG2
network_name_str Repositorio Institucional ECI
repository_id_str
dc.title.eng.fl_str_mv Automatic estimation of pose and falls in videos using computer vision model
title Automatic estimation of pose and falls in videos using computer vision model
spellingShingle Automatic estimation of pose and falls in videos using computer vision model
Tecnología médica
Medical technology
Monitoreo del paciente - Equipo y accesorios
Patient monitoring - Equipment and supplies
Trastornos de la postura
Posture disorders
Caídas (Accidentes)
Falls (Accidents)
Aprendizaje profundo
Estimación de la postura humana
Detección de caídas
Visión por computadora
Procesamiento de video digital
Deep learning
Human pose estimation
Fall detection
Computer vision
Digital video processing
title_short Automatic estimation of pose and falls in videos using computer vision model
title_full Automatic estimation of pose and falls in videos using computer vision model
title_fullStr Automatic estimation of pose and falls in videos using computer vision model
title_full_unstemmed Automatic estimation of pose and falls in videos using computer vision model
title_sort Automatic estimation of pose and falls in videos using computer vision model
dc.creator.fl_str_mv Calvache, Daniela
Bernal, Hernán
Guarín, Juan F.
Aguía, Karen
Orjuela Cañón, Álvaro D.
Perdomo, Oscar J.
dc.contributor.author.none.fl_str_mv Calvache, Daniela
Bernal, Hernán
Guarín, Juan F.
Aguía, Karen
Orjuela Cañón, Álvaro D.
Perdomo, Oscar J.
dc.contributor.researchgroup.spa.fl_str_mv GiBiome
dc.subject.armarc.none.fl_str_mv Tecnología médica
Medical technology
Monitoreo del paciente - Equipo y accesorios
Patient monitoring - Equipment and supplies
Trastornos de la postura
Posture disorders
Caídas (Accidentes)
Falls (Accidents)
topic Tecnología médica
Medical technology
Monitoreo del paciente - Equipo y accesorios
Patient monitoring - Equipment and supplies
Trastornos de la postura
Posture disorders
Caídas (Accidentes)
Falls (Accidents)
Aprendizaje profundo
Estimación de la postura humana
Detección de caídas
Visión por computadora
Procesamiento de video digital
Deep learning
Human pose estimation
Fall detection
Computer vision
Digital video processing
dc.subject.proposal.spa.fl_str_mv Aprendizaje profundo
Estimación de la postura humana
Detección de caídas
Visión por computadora
Procesamiento de video digital
dc.subject.proposal.eng.fl_str_mv Deep learning
Human pose estimation
Fall detection
Computer vision
Digital video processing
description Human pose estimation is defined as the process of locating joints of a person or a crowd given an image or video. Currently, this estimation is widely used for the evaluation of athletes, workers, and the monitoring of patients in clinical settings. However, human pose estimation is not an easy task as it requires experts to manually assess the person’s position by using specialized equipment such as e-health devices (watches, bands, handles), markers, and high-cost cameras to monitor a limited scenario. The main goal of this article is to evaluate a marker-less low-cost computer vision system to get the automatic estimation of poses and fall detection on video by calculating the person’s joint angle with a high level of adaptability to any space. The proposed model is the first step in the construction of a tool that allows monitoring and generating alerts to prevent falls at home and clinical settings.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2024-10-01T17:39:32Z
dc.date.available.none.fl_str_mv 2024-10-01T17:39:32Z
dc.type.spa.fl_str_mv Artículo de revista
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.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.issn.spa.fl_str_mv 0277-786X
dc.identifier.uri.none.fl_str_mv https://repositorio.escuelaing.edu.co/handle/001/3292
dc.identifier.eissn.spa.fl_str_mv 0277-786X
dc.identifier.instname.spa.fl_str_mv Universidad Escuela Colombiana de Ingeniería
dc.identifier.reponame.spa.fl_str_mv Repositorio Digital
dc.identifier.repourl.spa.fl_str_mv https://repositorio.escuelaing.edu.co/
identifier_str_mv 0277-786X
Universidad Escuela Colombiana de Ingeniería
Repositorio Digital
url https://repositorio.escuelaing.edu.co/handle/001/3292
https://repositorio.escuelaing.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationedition.spa.fl_str_mv Nov. 2020
dc.relation.citationendpage.spa.fl_str_mv 9
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 2020
dc.relation.ispartofjournal.eng.fl_str_mv Proceedings Of Spie
dc.relation.issourceof.none.fl_str_mv The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru
dc.relation.references.spa.fl_str_mv Toshev, A. and Szegedy, C., “Deeppose: Human pose estimation via deep neural networks,” in [Proceedings of the IEEE conference on computer vision and pattern recognition], 1653–1660 (2014).
L´opez-Quintero, M. I., “Estimaci´on de la pose humana 2d en im´agenes est´ereo,” (2016).
Leitch, J., Stebbins, J., Paolini, G., and Zavatsky, A. B., “Identifying gait events without a force plate during running: A comparison of methods,” Gait & Posture 33(1), 130–132 (2011).
Davis, R. B., “Clinical gait analysis,” IEEE Engineering in Medicine and Biology Magazine 7(3), 35–40 (1988).
Li, X., Fan, Z., Liu, Y., Li, Y., and Dai, Q., “3d pose detection of closely interactive humans using multi-view cameras,” Sensors 19(12), 2831 (2019).
Wang, C., Wang, Y., and Yuille, A. L., “An approach to pose-based action recognition,” in [Proceedings of the IEEE conference on computer vision and pattern recognition], 915–922 (2013).
Hidalgo, G. et al., “Openpose: Real-time multi-person keypoint detection library for body, face, and hands estimation,” Retrieved April (2018).
Liu, J., Gu, Y., and Kamijo, S., “Customer pose estimation using orientational spatio-temporal network from surveillance camera,” Multimedia Systems 24, 1–19 (11 2017).
Buzzelli, M., Alb´e, A., and Ciocca, G., “A vision-based system for monitoring elderly people at home,” Applied Sciences 10(1), 374 (2020)
Sebestyen, G., Stoica, I., and Hangan, A., “Human activity recognition and monitoring for elderly people,” in [2016 IEEE 12th international conference on intelligent computer communication and processing (ICCP) ], 341–347, IEEE (2016).
Hall, A., Wilson, C. B., Stanmore, E., and Todd, C., “Implementing monitoring technologies in care homes for people with dementia: a qualitative exploration using normalization process theory,” International journal of nursing studies 72, 60–70 (2017).
Andriluka, M., Pishchulin, L., Gehler, P., and Schiele, B., “2d human pose estimation: New benchmark and state of the art analysis,” in [Proceedings of the IEEE Conference on computer Vision and Pattern Recognition], 3686–3693 (2014).
Cao, Z., Simon, T., Wei, S.-E., and Sheikh, Y., “Realtime multi-person 2d pose estimation using part affinity fields,” in [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition], 7291–7299 (2017).
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_14cb
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/closedAccess
eu_rights_str_mv closedAccess
rights_invalid_str_mv http://purl.org/coar/access_right/c_14cb
dc.format.extent.spa.fl_str_mv 9 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Society of Photo-Optical Instrumentation Engineers
dc.publisher.place.spa.fl_str_mv Lima, Perú
dc.source.spa.fl_str_mv https://www.spiedigitallibrary.org/conference-proceedings-of-spie#_=_
institution Escuela Colombiana de Ingeniería Julio Garavito
bitstream.url.fl_str_mv https://repositorio.escuelaing.edu.co/bitstream/001/3292/4/Automatic%20estimation%20of%20pose%20and%20falls%20in%20videos%20using%20computer%20vision%20model%20%281%29.pdf.txt
https://repositorio.escuelaing.edu.co/bitstream/001/3292/3/Portada%20Automatic%20estimation%20of%20pose%20and%20falls%20in%20videos%20using%20computer%20vision%20model%20%281%29.PNG
https://repositorio.escuelaing.edu.co/bitstream/001/3292/5/Automatic%20estimation%20of%20pose%20and%20falls%20in%20videos%20using%20computer%20vision%20model%20%281%29.pdf.jpg
https://repositorio.escuelaing.edu.co/bitstream/001/3292/2/license.txt
https://repositorio.escuelaing.edu.co/bitstream/001/3292/1/Automatic%20estimation%20of%20pose%20and%20falls%20in%20videos%20using%20computer%20vision%20model%20%281%29.pdf
bitstream.checksum.fl_str_mv 8abec9ce6db31fc604eb813ec2d2c86e
408116576f0e2ff095f41dd5b18c3d4e
08562fb39338cebd7a176a10d299f60b
5a7ca94c2e5326ee169f979d71d0f06e
daf0cac1bb84de2c8ff6846fbefb6312
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Escuela Colombiana de Ingeniería Julio Garavito
repository.mail.fl_str_mv repositorio.eci@escuelaing.edu.co
_version_ 1814355639433953280
spelling Calvache, Daniela9edcff2fb099d96bdc09d744bfd328b1Bernal, Hernán003fa3625f9c140a01dffcd37a8c41f6Guarín, Juan F.e6c3469ad7c0db6aa1ed46fb10af934eAguía, Karen80eb1fd4057bcd165fdec88bc7c644a4Orjuela Cañón, Álvaro D.5baa90644bd8f50c6ccf712fd1ea6607Perdomo, Oscar J.8ff3eff7c05313a1136da5be61e99d4dGiBiome2024-10-01T17:39:32Z2024-10-01T17:39:32Z20200277-786Xhttps://repositorio.escuelaing.edu.co/handle/001/32920277-786XUniversidad Escuela Colombiana de IngenieríaRepositorio Digitalhttps://repositorio.escuelaing.edu.co/Human pose estimation is defined as the process of locating joints of a person or a crowd given an image or video. Currently, this estimation is widely used for the evaluation of athletes, workers, and the monitoring of patients in clinical settings. However, human pose estimation is not an easy task as it requires experts to manually assess the person’s position by using specialized equipment such as e-health devices (watches, bands, handles), markers, and high-cost cameras to monitor a limited scenario. The main goal of this article is to evaluate a marker-less low-cost computer vision system to get the automatic estimation of poses and fall detection on video by calculating the person’s joint angle with a high level of adaptability to any space. The proposed model is the first step in the construction of a tool that allows monitoring and generating alerts to prevent falls at home and clinical settings.La estimación de la pose humana se define como el proceso de localizar las articulaciones de una persona o una multitud a partir de una imagen o un vídeo. Actualmente, esta estimación es muy utilizada para la evaluación de deportistas, trabajadores y el seguimiento de pacientes. en entornos clínicos. Sin embargo, la estimación de la pose humana no es una tarea fácil ya que requiere que los expertos realicen manualmente evaluar la posición de la persona mediante el uso de equipos especializados, como dispositivos de salud electrónica (relojes, pulseras, manijas), marcadores y cámaras de alto costo para monitorear un escenario limitado. El objetivo principal de este artículo es evaluar una Sistema de visión por computadora de bajo costo y sin marcadores para obtener la estimación automática de posturas y detección de caídas en video. calculando el ángulo articular de la persona con un alto nivel de adaptabilidad a cualquier espacio. El modelo propuesto es el primer paso en la construcción de una herramienta que permita monitorear y generar alertas para prevenir caídas en el hogar y entornos clínicos.9 páginasapplication/pdfengSociety of Photo-Optical Instrumentation EngineersLima, Perúhttps://www.spiedigitallibrary.org/conference-proceedings-of-spie#_=_Automatic estimation of pose and falls in videos using computer vision modelArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Nov. 2020912020Proceedings Of SpieThe 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, PeruToshev, A. and Szegedy, C., “Deeppose: Human pose estimation via deep neural networks,” in [Proceedings of the IEEE conference on computer vision and pattern recognition], 1653–1660 (2014).L´opez-Quintero, M. I., “Estimaci´on de la pose humana 2d en im´agenes est´ereo,” (2016).Leitch, J., Stebbins, J., Paolini, G., and Zavatsky, A. B., “Identifying gait events without a force plate during running: A comparison of methods,” Gait & Posture 33(1), 130–132 (2011).Davis, R. B., “Clinical gait analysis,” IEEE Engineering in Medicine and Biology Magazine 7(3), 35–40 (1988).Li, X., Fan, Z., Liu, Y., Li, Y., and Dai, Q., “3d pose detection of closely interactive humans using multi-view cameras,” Sensors 19(12), 2831 (2019).Wang, C., Wang, Y., and Yuille, A. L., “An approach to pose-based action recognition,” in [Proceedings of the IEEE conference on computer vision and pattern recognition], 915–922 (2013).Hidalgo, G. et al., “Openpose: Real-time multi-person keypoint detection library for body, face, and hands estimation,” Retrieved April (2018).Liu, J., Gu, Y., and Kamijo, S., “Customer pose estimation using orientational spatio-temporal network from surveillance camera,” Multimedia Systems 24, 1–19 (11 2017).Buzzelli, M., Alb´e, A., and Ciocca, G., “A vision-based system for monitoring elderly people at home,” Applied Sciences 10(1), 374 (2020)Sebestyen, G., Stoica, I., and Hangan, A., “Human activity recognition and monitoring for elderly people,” in [2016 IEEE 12th international conference on intelligent computer communication and processing (ICCP) ], 341–347, IEEE (2016).Hall, A., Wilson, C. B., Stanmore, E., and Todd, C., “Implementing monitoring technologies in care homes for people with dementia: a qualitative exploration using normalization process theory,” International journal of nursing studies 72, 60–70 (2017).Andriluka, M., Pishchulin, L., Gehler, P., and Schiele, B., “2d human pose estimation: New benchmark and state of the art analysis,” in [Proceedings of the IEEE Conference on computer Vision and Pattern Recognition], 3686–3693 (2014).Cao, Z., Simon, T., Wei, S.-E., and Sheikh, Y., “Realtime multi-person 2d pose estimation using part affinity fields,” in [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition], 7291–7299 (2017).info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbTecnología médicaMedical technologyMonitoreo del paciente - Equipo y accesoriosPatient monitoring - Equipment and suppliesTrastornos de la posturaPosture disordersCaídas (Accidentes)Falls (Accidents)Aprendizaje profundoEstimación de la postura humanaDetección de caídasVisión por computadoraProcesamiento de video digitalDeep learningHuman pose estimationFall detectionComputer visionDigital video processingTEXTAutomatic estimation of pose and falls in videos using computer vision model (1).pdf.txtAutomatic estimation of pose and falls in videos using computer vision model (1).pdf.txtExtracted texttext/plain20799https://repositorio.escuelaing.edu.co/bitstream/001/3292/4/Automatic%20estimation%20of%20pose%20and%20falls%20in%20videos%20using%20computer%20vision%20model%20%281%29.pdf.txt8abec9ce6db31fc604eb813ec2d2c86eMD54metadata only accessTHUMBNAILPortada Automatic estimation of pose and falls in videos using computer vision model (1).PNGPortada Automatic estimation of pose and falls in videos using computer vision model (1).PNGimage/png136373https://repositorio.escuelaing.edu.co/bitstream/001/3292/3/Portada%20Automatic%20estimation%20of%20pose%20and%20falls%20in%20videos%20using%20computer%20vision%20model%20%281%29.PNG408116576f0e2ff095f41dd5b18c3d4eMD53open accessAutomatic estimation of pose and falls in videos using computer vision model (1).pdf.jpgAutomatic estimation of pose and falls in videos using computer vision model (1).pdf.jpgGenerated Thumbnailimage/jpeg11953https://repositorio.escuelaing.edu.co/bitstream/001/3292/5/Automatic%20estimation%20of%20pose%20and%20falls%20in%20videos%20using%20computer%20vision%20model%20%281%29.pdf.jpg08562fb39338cebd7a176a10d299f60bMD55metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3292/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALAutomatic estimation of pose and falls in videos using computer vision model (1).pdfAutomatic estimation of pose and falls in videos using computer vision model (1).pdfapplication/pdf5160205https://repositorio.escuelaing.edu.co/bitstream/001/3292/1/Automatic%20estimation%20of%20pose%20and%20falls%20in%20videos%20using%20computer%20vision%20model%20%281%29.pdfdaf0cac1bb84de2c8ff6846fbefb6312MD51metadata only access001/3292oai:repositorio.escuelaing.edu.co:001/32922024-10-02 03:01:48.832metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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

Publicaciones similares