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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9103712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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