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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in man...
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/PMC8879042/ https://www.ncbi.nlm.nih.gov/pubmed/35214377 http://dx.doi.org/10.3390/s22041476 |
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author | Zhang, Shibo Li, Yaxuan Zhang, Shen Shahabi, Farzad Xia, Stephen Deng, Yu Alshurafa, Nabil |
author_facet | Zhang, Shibo Li, Yaxuan Zhang, Shen Shahabi, Farzad Xia, Stephen Deng, Yu Alshurafa, Nabil |
author_sort | Zhang, Shibo |
collection | PubMed |
description | Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR. |
format | Online Article Text |
id | pubmed-8879042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88790422022-02-26 Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances Zhang, Shibo Li, Yaxuan Zhang, Shen Shahabi, Farzad Xia, Stephen Deng, Yu Alshurafa, Nabil Sensors (Basel) Review Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR. MDPI 2022-02-14 /pmc/articles/PMC8879042/ /pubmed/35214377 http://dx.doi.org/10.3390/s22041476 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 Zhang, Shibo Li, Yaxuan Zhang, Shen Shahabi, Farzad Xia, Stephen Deng, Yu Alshurafa, Nabil Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
title | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
title_full | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
title_fullStr | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
title_full_unstemmed | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
title_short | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
title_sort | deep learning in human activity recognition with wearable sensors: a review on advances |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8879042/ https://www.ncbi.nlm.nih.gov/pubmed/35214377 http://dx.doi.org/10.3390/s22041476 |
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