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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 principales: Ariza-Colpas, Paola Patricia, Vicario, Enrico, Oviedo-Carrascal, Ana Isabel, Butt Aziz, Shariq, Piñeres-Melo, Marlon Alberto, Quintero-Linero, Alejandra, Patara, Fulvio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103712/
https://www.ncbi.nlm.nih.gov/pubmed/35591091
http://dx.doi.org/10.3390/s22093401
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author Ariza-Colpas, Paola Patricia
Vicario, Enrico
Oviedo-Carrascal, Ana Isabel
Butt Aziz, Shariq
Piñeres-Melo, Marlon Alberto
Quintero-Linero, Alejandra
Patara, Fulvio
author_facet Ariza-Colpas, Paola Patricia
Vicario, Enrico
Oviedo-Carrascal, Ana Isabel
Butt Aziz, Shariq
Piñeres-Melo, Marlon Alberto
Quintero-Linero, Alejandra
Patara, Fulvio
author_sort Ariza-Colpas, Paola Patricia
collection PubMed
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.
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spelling pubmed-91037122022-05-14 Human Activity Recognition Data Analysis: History, Evolutions, and New Trends Ariza-Colpas, Paola Patricia Vicario, Enrico Oviedo-Carrascal, Ana Isabel Butt Aziz, Shariq Piñeres-Melo, Marlon Alberto Quintero-Linero, Alejandra Patara, Fulvio Sensors (Basel) Review 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. MDPI 2022-04-29 /pmc/articles/PMC9103712/ /pubmed/35591091 http://dx.doi.org/10.3390/s22093401 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Ariza-Colpas, Paola Patricia
Vicario, Enrico
Oviedo-Carrascal, Ana Isabel
Butt Aziz, Shariq
Piñeres-Melo, Marlon Alberto
Quintero-Linero, Alejandra
Patara, Fulvio
Human Activity Recognition Data Analysis: History, Evolutions, and New Trends
title 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_short Human Activity Recognition Data Analysis: History, Evolutions, and New Trends
title_sort human activity recognition data analysis: history, evolutions, and new trends
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103712/
https://www.ncbi.nlm.nih.gov/pubmed/35591091
http://dx.doi.org/10.3390/s22093401
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