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A Survey on Recent Advances in Wearable Fall Detection Systems
With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the Eur...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201510/ https://www.ncbi.nlm.nih.gov/pubmed/32420327 http://dx.doi.org/10.1155/2020/2167160 |
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author | Ramachandran, Anita Karuppiah, Anupama |
author_facet | Ramachandran, Anita Karuppiah, Anupama |
author_sort | Ramachandran, Anita |
collection | PubMed |
description | With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey. |
format | Online Article Text |
id | pubmed-7201510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-72015102020-05-15 A Survey on Recent Advances in Wearable Fall Detection Systems Ramachandran, Anita Karuppiah, Anupama Biomed Res Int Review Article With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey. Hindawi 2020-01-13 /pmc/articles/PMC7201510/ /pubmed/32420327 http://dx.doi.org/10.1155/2020/2167160 Text en Copyright © 2020 Anita Ramachandran and Anupama Karuppiah. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Ramachandran, Anita Karuppiah, Anupama A Survey on Recent Advances in Wearable Fall Detection Systems |
title | A Survey on Recent Advances in Wearable Fall Detection Systems |
title_full | A Survey on Recent Advances in Wearable Fall Detection Systems |
title_fullStr | A Survey on Recent Advances in Wearable Fall Detection Systems |
title_full_unstemmed | A Survey on Recent Advances in Wearable Fall Detection Systems |
title_short | A Survey on Recent Advances in Wearable Fall Detection Systems |
title_sort | survey on recent advances in wearable fall detection systems |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7201510/ https://www.ncbi.nlm.nih.gov/pubmed/32420327 http://dx.doi.org/10.1155/2020/2167160 |
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