A multi-camera and multimodal dataset for posture and gait analysis
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Conjunto de dados |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/84265 |
Resumo: | Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user’s progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device. |
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A multi-camera and multimodal dataset for posture and gait analysisEngenharia e Tecnologia::Engenharia MédicaScience & TechnologySaúde de qualidadeMonitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user’s progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device.This work has been supported by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant 2020.05708.BD and under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020.Nature Publishing GroupUniversidade do MinhoPalermo, Manuel CastroLopes, João Pedro MendesAndré, João Carlos Vieira PeixotoMatias, Ana C.Cerqueira, João JoséSantos, Cristina2022-10-062022-10-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/datasetapplication/pdfhttps://hdl.handle.net/1822/84265engPalermo, M., Lopes, J.M., André, J. et al. A multi-camera and multimodal dataset for posture and gait analysis. Sci Data 9, 603 (2022). https://doi.org/10.1038/s41597-022-01722-72052-446310.1038/s41597-022-01722-736202855https://www.nature.com/articles/s41597-022-01722-7#citeasinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:07:23Zoai:repositorium.sdum.uminho.pt:1822/84265Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:58:21.047115Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A multi-camera and multimodal dataset for posture and gait analysis |
title |
A multi-camera and multimodal dataset for posture and gait analysis |
spellingShingle |
A multi-camera and multimodal dataset for posture and gait analysis Palermo, Manuel Castro Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
title_short |
A multi-camera and multimodal dataset for posture and gait analysis |
title_full |
A multi-camera and multimodal dataset for posture and gait analysis |
title_fullStr |
A multi-camera and multimodal dataset for posture and gait analysis |
title_full_unstemmed |
A multi-camera and multimodal dataset for posture and gait analysis |
title_sort |
A multi-camera and multimodal dataset for posture and gait analysis |
author |
Palermo, Manuel Castro |
author_facet |
Palermo, Manuel Castro Lopes, João Pedro Mendes André, João Carlos Vieira Peixoto Matias, Ana C. Cerqueira, João José Santos, Cristina |
author_role |
author |
author2 |
Lopes, João Pedro Mendes André, João Carlos Vieira Peixoto Matias, Ana C. Cerqueira, João José Santos, Cristina |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Palermo, Manuel Castro Lopes, João Pedro Mendes André, João Carlos Vieira Peixoto Matias, Ana C. Cerqueira, João José Santos, Cristina |
dc.subject.por.fl_str_mv |
Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
topic |
Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
description |
Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user’s progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-06 2022-10-06T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/dataset |
format |
dataset |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/84265 |
url |
https://hdl.handle.net/1822/84265 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Palermo, M., Lopes, J.M., André, J. et al. A multi-camera and multimodal dataset for posture and gait analysis. Sci Data 9, 603 (2022). https://doi.org/10.1038/s41597-022-01722-7 2052-4463 10.1038/s41597-022-01722-7 36202855 https://www.nature.com/articles/s41597-022-01722-7#citeas |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Nature Publishing Group |
publisher.none.fl_str_mv |
Nature Publishing Group |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132373533589504 |