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Wearable Sensor Systems for Fall Risk Assessment: A Review
Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable system...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329588/ https://www.ncbi.nlm.nih.gov/pubmed/35911615 http://dx.doi.org/10.3389/fdgth.2022.921506 |
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author | Subramaniam, Sophini Faisal, Abu Ilius Deen, M. Jamal |
author_facet | Subramaniam, Sophini Faisal, Abu Ilius Deen, M. Jamal |
author_sort | Subramaniam, Sophini |
collection | PubMed |
description | Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives. |
format | Online Article Text |
id | pubmed-9329588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93295882022-07-29 Wearable Sensor Systems for Fall Risk Assessment: A Review Subramaniam, Sophini Faisal, Abu Ilius Deen, M. Jamal Front Digit Health Digital Health Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives. Frontiers Media S.A. 2022-07-14 /pmc/articles/PMC9329588/ /pubmed/35911615 http://dx.doi.org/10.3389/fdgth.2022.921506 Text en Copyright © 2022 Subramaniam, Faisal and Deen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Subramaniam, Sophini Faisal, Abu Ilius Deen, M. Jamal Wearable Sensor Systems for Fall Risk Assessment: A Review |
title | Wearable Sensor Systems for Fall Risk Assessment: A Review |
title_full | Wearable Sensor Systems for Fall Risk Assessment: A Review |
title_fullStr | Wearable Sensor Systems for Fall Risk Assessment: A Review |
title_full_unstemmed | Wearable Sensor Systems for Fall Risk Assessment: A Review |
title_short | Wearable Sensor Systems for Fall Risk Assessment: A Review |
title_sort | wearable sensor systems for fall risk assessment: a review |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329588/ https://www.ncbi.nlm.nih.gov/pubmed/35911615 http://dx.doi.org/10.3389/fdgth.2022.921506 |
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