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A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning
Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469092/ https://www.ncbi.nlm.nih.gov/pubmed/34577243 http://dx.doi.org/10.3390/s21186037 |
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author | Bouchabou, Damien Nguyen, Sao Mai Lohr, Christophe LeDuc, Benoit Kanellos, Ioannis |
author_facet | Bouchabou, Damien Nguyen, Sao Mai Lohr, Christophe LeDuc, Benoit Kanellos, Ioannis |
author_sort | Bouchabou, Damien |
collection | PubMed |
description | Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. However, new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges, and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, as well as missing and needed contributions. However, we also propose directions, research opportunities, and solutions to accelerate advances in this field. |
format | Online Article Text |
id | pubmed-8469092 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84690922021-09-27 A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning Bouchabou, Damien Nguyen, Sao Mai Lohr, Christophe LeDuc, Benoit Kanellos, Ioannis Sensors (Basel) Review Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy, and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. However, new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges, and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, as well as missing and needed contributions. However, we also propose directions, research opportunities, and solutions to accelerate advances in this field. MDPI 2021-09-09 /pmc/articles/PMC8469092/ /pubmed/34577243 http://dx.doi.org/10.3390/s21186037 Text en © 2021 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 Bouchabou, Damien Nguyen, Sao Mai Lohr, Christophe LeDuc, Benoit Kanellos, Ioannis A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning |
title | A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning |
title_full | A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning |
title_fullStr | A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning |
title_full_unstemmed | A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning |
title_short | A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning |
title_sort | survey of human activity recognition in smart homes based on iot sensors algorithms: taxonomies, challenges, and opportunities with deep learning |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469092/ https://www.ncbi.nlm.nih.gov/pubmed/34577243 http://dx.doi.org/10.3390/s21186037 |
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