From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data

Detalhes bibliográficos
Autor(a) principal: Palermo, Manuel
Data de Publicação: 2022
Outros Autores: Cerqueira, Sara M., André, João, Pereira, António, Santos, Cristina
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/80090
Resumo: This database is accompanied by a folder with all the scripts used to process and handle the data described. It is openly hosted in Zenodo: https://doi.org/10.5281/zenodo.5801927
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spelling From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor dataHuman Pose EstimationInertial DataDatasetEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaEngenharia e Tecnologia::Engenharia MédicaScience & TechnologyThis database is accompanied by a folder with all the scripts used to process and handle the data described. It is openly hosted in Zenodo: https://doi.org/10.5281/zenodo.5801927Additionally, an extended code repository is available on Github (https://github.com/ManuelPalermo/HumanInertialPose.git) with updated code to not only process the data described, but also calculate kinematics, visualize and evaluate the resulting motions and offers extended support for general inertial pose estimation pipelines. All scripts are based on the Python programming language and, thus, open source. The code contains a permissive MIT license for unrestricted usage.Wearable technology is expanding for motion monitoring. However, open challenges still limit its widespread use, especially in low-cost systems. Most solutions are either expensive commercial products or lower performance ad-hoc systems. Moreover, few datasets are available for the development of complete and general solutions. This work presents 2 datasets, with low-cost and high-end Magnetic, Angular Rate, and Gravity(MARG) sensor data. Provides data for the complete inertial pose pipeline analysis, starting from raw data, sensor-to-segment calibration, multi-sensor fusion, skeleton-kinematics, to complete Human pose. Contains data from 21 and 10 participants, respectively, performing 6 types of sequences, presenting high variability and complex dynamics with almost complete range-of-motion. Amounts to 3.5 M samples, synchronized with a ground-truth inertial motion capture system. Presents a method to evaluate data quality. This database may contribute to develop novel algorithms for each pipeline's processing steps, with applications in inertial pose estimation algorithms, human movement forecasting, and motion assessment in industrial or rehabilitation settings. All data and code to process and analyze the complete pipeline is freely available.This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n° 39479; Funding Reference: POCI-01-0247-FEDER-39479]. Sara Cerqueira was supported by the doctoral Grant SFRH/BD/151382/2021, financed by the Portuguese Foundation for Science and Technology (FCT), under MIT Portugal Program.Nature ResearchUniversidade do MinhoPalermo, ManuelCerqueira, Sara M.André, JoãoPereira, AntónioSantos, Cristina2022-09-302022-09-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/80090engPalermo, M., Cerqueira, S.M., André, J. et al. From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data. Sci Data 9, 591 (2022). https://doi.org/10.1038/s41597-022-01690-y2052-446310.1038/s41597-022-01690-y36180479https://www.nature.com/articles/s41597-022-01690-yinfo: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:RCAAP2023-12-30T01:25:32Zoai:repositorium.sdum.uminho.pt:1822/80090Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:22:10.838435Repositório 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 From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data
title From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data
spellingShingle From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data
Palermo, Manuel
Human Pose Estimation
Inertial Data
Dataset
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Engenharia e Tecnologia::Engenharia Médica
Science & Technology
title_short From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data
title_full From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data
title_fullStr From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data
title_full_unstemmed From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data
title_sort From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data
author Palermo, Manuel
author_facet Palermo, Manuel
Cerqueira, Sara M.
André, João
Pereira, António
Santos, Cristina
author_role author
author2 Cerqueira, Sara M.
André, João
Pereira, António
Santos, Cristina
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Palermo, Manuel
Cerqueira, Sara M.
André, João
Pereira, António
Santos, Cristina
dc.subject.por.fl_str_mv Human Pose Estimation
Inertial Data
Dataset
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Engenharia e Tecnologia::Engenharia Médica
Science & Technology
topic Human Pose Estimation
Inertial Data
Dataset
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Engenharia e Tecnologia::Engenharia Médica
Science & Technology
description This database is accompanied by a folder with all the scripts used to process and handle the data described. It is openly hosted in Zenodo: https://doi.org/10.5281/zenodo.5801927
publishDate 2022
dc.date.none.fl_str_mv 2022-09-30
2022-09-30T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/80090
url https://hdl.handle.net/1822/80090
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Palermo, M., Cerqueira, S.M., André, J. et al. From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data. Sci Data 9, 591 (2022). https://doi.org/10.1038/s41597-022-01690-y
2052-4463
10.1038/s41597-022-01690-y
36180479
https://www.nature.com/articles/s41597-022-01690-y
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 Research
publisher.none.fl_str_mv Nature Research
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
instname_str 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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