Human activity recognition data analysis: history, evolutions, and new trends

The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from n...

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Autores:
Ariza Colpas, Paola Patricia
sVicario, Enrico
Oviedo Carrascal, Ana Isabel
aziz, shariq
Piñeres Melo, Marlon Alberto
Quintero linero, Alejandra paola
PATARA, FULVIO
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9460
Acceso en línea:
https://hdl.handle.net/11323/9460
https://doi.org/10.3390/s22093401
https://repositorio.cuc.edu.co/
Palabra clave:
Ambient assisted living—AAL
Human activity recognition—HAR
Activities of daily living—ADL
Activity recognition systems—ARS
Clustering
Unsupervised activity recognition
Supervised learning
Unsupervised learning
Ensemble learning
Deep learning
Reinforcement learning
Rights
openAccess
License
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
id RCUC2_befb999e69f3ddf39edea1702d412447
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9460
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Human activity recognition data analysis: history, evolutions, and new trends
title Human activity recognition data analysis: history, evolutions, and new trends
spellingShingle Human activity recognition data analysis: history, evolutions, and new trends
Ambient assisted living—AAL
Human activity recognition—HAR
Activities of daily living—ADL
Activity recognition systems—ARS
Clustering
Unsupervised activity recognition
Supervised learning
Unsupervised learning
Ensemble learning
Deep learning
Reinforcement learning
title_short Human activity recognition data analysis: history, evolutions, and new trends
title_full Human activity recognition data analysis: history, evolutions, and new trends
title_fullStr Human activity recognition data analysis: history, evolutions, and new trends
title_full_unstemmed Human activity recognition data analysis: history, evolutions, and new trends
title_sort Human activity recognition data analysis: history, evolutions, and new trends
dc.creator.fl_str_mv Ariza Colpas, Paola Patricia
sVicario, Enrico
Oviedo Carrascal, Ana Isabel
aziz, shariq
Piñeres Melo, Marlon Alberto
Quintero linero, Alejandra paola
PATARA, FULVIO
dc.contributor.author.spa.fl_str_mv Ariza Colpas, Paola Patricia
sVicario, Enrico
Oviedo Carrascal, Ana Isabel
aziz, shariq
Piñeres Melo, Marlon Alberto
Quintero linero, Alejandra paola
PATARA, FULVIO
dc.subject.proposal.eng.fl_str_mv Ambient assisted living—AAL
Human activity recognition—HAR
Activities of daily living—ADL
Activity recognition systems—ARS
Clustering
Unsupervised activity recognition
Supervised learning
Unsupervised learning
Ensemble learning
Deep learning
Reinforcement learning
topic Ambient assisted living—AAL
Human activity recognition—HAR
Activities of daily living—ADL
Activity recognition systems—ARS
Clustering
Unsupervised activity recognition
Supervised learning
Unsupervised learning
Ensemble learning
Deep learning
Reinforcement learning
description The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-08-17T20:12:04Z
dc.date.available.none.fl_str_mv 2022-08-17T20:12:04Z
dc.date.issued.none.fl_str_mv 2022-04-29
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.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
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
format http://purl.org/coar/resource_type/c_6501
dc.identifier.citation.spa.fl_str_mv Ariza-Colpas, P.P.; Vicario, E.; Oviedo-Carrascal, A.I.; Butt Aziz, S.; Piñeres-Melo, M.A.; Quintero-Linero, A.; Patara, F. Human Activity Recognition Data Analysis: History, Evolutions, and New Trends. Sensors 2022, 22, 3401. https://doi.org/10.3390/s22093401
dc.identifier.issn.spa.fl_str_mv 1424-3210
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9460
dc.identifier.url.spa.fl_str_mv https://doi.org/10.3390/s22093401
dc.identifier.doi.spa.fl_str_mv 10.3390/s22093401
dc.identifier.eissn.spa.fl_str_mv 1424-8220
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Ariza-Colpas, P.P.; Vicario, E.; Oviedo-Carrascal, A.I.; Butt Aziz, S.; Piñeres-Melo, M.A.; Quintero-Linero, A.; Patara, F. Human Activity Recognition Data Analysis: History, Evolutions, and New Trends. Sensors 2022, 22, 3401. https://doi.org/10.3390/s22093401
1424-3210
10.3390/s22093401
1424-8220
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9460
https://doi.org/10.3390/s22093401
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Sensors
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spelling Ariza Colpas, Paola PatriciasVicario, EnricoOviedo Carrascal, Ana Isabelaziz, shariq Piñeres Melo, Marlon AlbertoQuintero linero, Alejandra paolaPATARA, FULVIO2022-08-17T20:12:04Z2022-08-17T20:12:04Z2022-04-29Ariza-Colpas, P.P.; Vicario, E.; Oviedo-Carrascal, A.I.; Butt Aziz, S.; Piñeres-Melo, M.A.; Quintero-Linero, A.; Patara, F. Human Activity Recognition Data Analysis: History, Evolutions, and New Trends. Sensors 2022, 22, 3401. https://doi.org/10.3390/s220934011424-3210https://hdl.handle.net/11323/9460https://doi.org/10.3390/s2209340110.3390/s220934011424-8220Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.37 páginasapplication/pdfengMultidisciplinary Digital Publishing Institute (MDPI)Switzerland© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Human activity recognition data analysis: history, evolutions, and new trendsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.mdpi.com/1424-8220/22/9/3401Sensors1. Aracil, J.; Gordillo, F. Dinámica de Sistemas; Alianza Editorial: Madrid, Spain, 1997.2. Cramer, H.; Cansado, C. Métodos Matemáticos de Estadística; Aguilar: Madrid, Spain, 1968.3. Shapiro, S.C. Artificial intelligence. In Encyclopedia of Artificial Intelligence, 2nd ed.; Shapiro, S.C., Ed.; Wiley: New York, NY, USA, 1992; Volume 1.4. Rouse, M. Inteligencia Artificial, o AI. 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In Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, 19–21 February 2020; pp. 362–366.371922Ambient assisted living—AALHuman activity recognition—HARActivities of daily living—ADLActivity recognition systems—ARSClusteringUnsupervised activity recognitionSupervised learningUnsupervised learningEnsemble learningDeep learningReinforcement learningPublicationORIGINALHuman Activity Recognition Data Analysis.pdfHuman Activity Recognition Data Analysis.pdfapplication/pdf2030726https://repositorio.cuc.edu.co/bitstreams/507d01bf-5115-43b3-bab5-0b11561d2e2b/download6dd2f821d700c1ae339205e06a67c709MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/31f9a117-bb29-4f7f-b4fd-7c5eb20c290a/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTHuman Activity Recognition Data Analysis.pdf.txtHuman Activity Recognition Data Analysis.pdf.txttext/plain128060https://repositorio.cuc.edu.co/bitstreams/4b1a0d24-a22d-4033-865f-d9bb05a32714/downloadab9abfdb148e062520c2034f2ce7bbccMD53THUMBNAILHuman Activity Recognition Data Analysis.pdf.jpgHuman Activity Recognition Data Analysis.pdf.jpgimage/jpeg16287https://repositorio.cuc.edu.co/bitstreams/0bae1630-4af8-421f-a4ca-e9cf9b86f5e2/downloaddd00fe718de953538f5f115d5445f07bMD5411323/9460oai:repositorio.cuc.edu.co:11323/94602024-09-16 16:48:30.018https://creativecommons.org/licenses/by/4.0/© 2022 by the authors. Licensee MDPI, Basel, Switzerland.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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