A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan

In Taiwan, falls are one of the major causes of permanent disability and seeking medical care in older adults. One in seven people in the Taiwanese population exceeds the age of 65 years, and there are roughly 4 million community-dwelling older adults. One in six older adults have fallen or been dia...

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Autores principales:	Kai-Chih Lin, Rong-Jong Wai
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Artificial intelligence Internet of Things health promotion physical performance long-term care sub-healthy aging Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/823e521e55384554af18709e79b8c215
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spelling	oai:doaj.org-article:823e521e55384554af18709e79b8c2152021-11-09T00:02:37ZA Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan2169-353610.1109/ACCESS.2021.3123297https://doaj.org/article/823e521e55384554af18709e79b8c2152021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585709/https://doaj.org/toc/2169-3536In Taiwan, falls are one of the major causes of permanent disability and seeking medical care in older adults. One in seven people in the Taiwanese population exceeds the age of 65 years, and there are roughly 4 million community-dwelling older adults. One in six older adults have fallen or been diagnosed with Sarcopenia, which can lead to a loss of mobility. The major identified risk factors are impaired balance and gait. Implementing an early-stage prevention system is already an urgent requirement. The primary objective of this study is to propose an artificial intelligence (AI) Internet of Things (IoT) program and to develop an easy-access fall prevention system. This study took the criteria of the Asian Working Group for Sarcopenia (AWGS) and implemented it in field practice. Field experts reviewed data from the combination of gait parameters and gesture parameters and adaptively modified the training course bi-weekly. With 3 months of field practice and intervention, sub-healthy older adults’ average increase in gait speed was 29.83% for male participants and 34.06% for female participants. The results of this study demonstrate that rehabilitation of older adults can significantly improve mobility. This helps to understand the relationship of gait and gesture patterns to walking stability and strategies and adaptive interventions that could be taught in expertise programs to minimize the risk of fall.Kai-Chih LinRong-Jong WaiIEEEarticleArtificial intelligenceInternet of Thingshealth promotionphysical performancelong-term caresub-healthy agingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146404-146413 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence Internet of Things health promotion physical performance long-term care sub-healthy aging Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Artificial intelligence Internet of Things health promotion physical performance long-term care sub-healthy aging Electrical engineering. Electronics. Nuclear engineering TK1-9971 Kai-Chih Lin Rong-Jong Wai A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan
description	In Taiwan, falls are one of the major causes of permanent disability and seeking medical care in older adults. One in seven people in the Taiwanese population exceeds the age of 65 years, and there are roughly 4 million community-dwelling older adults. One in six older adults have fallen or been diagnosed with Sarcopenia, which can lead to a loss of mobility. The major identified risk factors are impaired balance and gait. Implementing an early-stage prevention system is already an urgent requirement. The primary objective of this study is to propose an artificial intelligence (AI) Internet of Things (IoT) program and to develop an easy-access fall prevention system. This study took the criteria of the Asian Working Group for Sarcopenia (AWGS) and implemented it in field practice. Field experts reviewed data from the combination of gait parameters and gesture parameters and adaptively modified the training course bi-weekly. With 3 months of field practice and intervention, sub-healthy older adults’ average increase in gait speed was 29.83% for male participants and 34.06% for female participants. The results of this study demonstrate that rehabilitation of older adults can significantly improve mobility. This helps to understand the relationship of gait and gesture patterns to walking stability and strategies and adaptive interventions that could be taught in expertise programs to minimize the risk of fall.
format	article
author	Kai-Chih Lin Rong-Jong Wai
author_facet	Kai-Chih Lin Rong-Jong Wai
author_sort	Kai-Chih Lin
title	A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan
title_short	A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan
title_full	A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan
title_fullStr	A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan
title_full_unstemmed	A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan
title_sort	feasible fall evaluation system via artificial intelligence gesture detection of gait and balance for sub-healthy community- dwelling older adults in taiwan
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/823e521e55384554af18709e79b8c215
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A Feasible Fall Evaluation System via Artificial Intelligence Gesture Detection of Gait and Balance for Sub-Healthy Community- Dwelling Older Adults in Taiwan

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