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Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics
Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians’ attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have bee...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156094/ https://www.ncbi.nlm.nih.gov/pubmed/34067644 http://dx.doi.org/10.3390/s21103481 |
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author | Roshdibenam, Venous Jogerst, Gerald J. Butler, Nicholas R. Baek, Stephen |
author_facet | Roshdibenam, Venous Jogerst, Gerald J. Butler, Nicholas R. Baek, Stephen |
author_sort | Roshdibenam, Venous |
collection | PubMed |
description | Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians’ attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician’s judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects’ locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults’ fall-risk status with relatively high sensitivity to geriatrician’s expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants’ gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations. |
format | Online Article Text |
id | pubmed-8156094 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81560942021-05-28 Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics Roshdibenam, Venous Jogerst, Gerald J. Butler, Nicholas R. Baek, Stephen Sensors (Basel) Article Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians’ attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician’s judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects’ locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults’ fall-risk status with relatively high sensitivity to geriatrician’s expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants’ gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations. MDPI 2021-05-17 /pmc/articles/PMC8156094/ /pubmed/34067644 http://dx.doi.org/10.3390/s21103481 Text en © 2021 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 | Article Roshdibenam, Venous Jogerst, Gerald J. Butler, Nicholas R. Baek, Stephen Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics |
title | Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics |
title_full | Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics |
title_fullStr | Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics |
title_full_unstemmed | Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics |
title_short | Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics |
title_sort | machine learning prediction of fall risk in older adults using timed up and go test kinematics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156094/ https://www.ncbi.nlm.nih.gov/pubmed/34067644 http://dx.doi.org/10.3390/s21103481 |
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