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The Methods of Fall Detection: A Literature Review

Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems. This literature review explores the current state of research on FDS and its applications. The review s...

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Detalles Bibliográficos
Autores principales: Newaz, Nishat Tasnim, Hanada, Eisuke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255727/
https://www.ncbi.nlm.nih.gov/pubmed/37299939
http://dx.doi.org/10.3390/s23115212
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author Newaz, Nishat Tasnim
Hanada, Eisuke
author_facet Newaz, Nishat Tasnim
Hanada, Eisuke
author_sort Newaz, Nishat Tasnim
collection PubMed
description Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems. This literature review explores the current state of research on FDS and its applications. The review shows various types and strategies of fall detection methods. Each type of fall detection is discussed with its pros and cons. Datasets of fall detection systems are also discussed. Security and privacy issues related to fall detection systems are also considered in the discussion. The review also examines the challenges of fall detection methods. Sensors, algorithms, and validation methods related to fall detection are also talked over. This work found that fall detection research has gradually increased and become popular in the last four decades. The effectiveness and popularity of all strategies are also discussed. The literature review underscores the promising potential of FDS and highlights areas for further research and development.
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spelling pubmed-102557272023-06-10 The Methods of Fall Detection: A Literature Review Newaz, Nishat Tasnim Hanada, Eisuke Sensors (Basel) Review Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems. This literature review explores the current state of research on FDS and its applications. The review shows various types and strategies of fall detection methods. Each type of fall detection is discussed with its pros and cons. Datasets of fall detection systems are also discussed. Security and privacy issues related to fall detection systems are also considered in the discussion. The review also examines the challenges of fall detection methods. Sensors, algorithms, and validation methods related to fall detection are also talked over. This work found that fall detection research has gradually increased and become popular in the last four decades. The effectiveness and popularity of all strategies are also discussed. The literature review underscores the promising potential of FDS and highlights areas for further research and development. MDPI 2023-05-30 /pmc/articles/PMC10255727/ /pubmed/37299939 http://dx.doi.org/10.3390/s23115212 Text en © 2023 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
Newaz, Nishat Tasnim
Hanada, Eisuke
The Methods of Fall Detection: A Literature Review
title The Methods of Fall Detection: A Literature Review
title_full The Methods of Fall Detection: A Literature Review
title_fullStr The Methods of Fall Detection: A Literature Review
title_full_unstemmed The Methods of Fall Detection: A Literature Review
title_short The Methods of Fall Detection: A Literature Review
title_sort methods of fall detection: a literature review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255727/
https://www.ncbi.nlm.nih.gov/pubmed/37299939
http://dx.doi.org/10.3390/s23115212
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