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Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units

Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while do...

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Autores principales: Hauth, Jeremiah, Jabri, Safa, Kamran, Fahad, Feleke, Eyoel W., Nigusie, Kaleab, Ojeda, Lauro V., Handelzalts, Shirley, Nyquist, Linda, Alexander, Neil B., Huan, Xun, Wiens, Jenna, Sienko, Kathleen H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309544/
https://www.ncbi.nlm.nih.gov/pubmed/34300399
http://dx.doi.org/10.3390/s21144661
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author Hauth, Jeremiah
Jabri, Safa
Kamran, Fahad
Feleke, Eyoel W.
Nigusie, Kaleab
Ojeda, Lauro V.
Handelzalts, Shirley
Nyquist, Linda
Alexander, Neil B.
Huan, Xun
Wiens, Jenna
Sienko, Kathleen H.
author_facet Hauth, Jeremiah
Jabri, Safa
Kamran, Fahad
Feleke, Eyoel W.
Nigusie, Kaleab
Ojeda, Lauro V.
Handelzalts, Shirley
Nyquist, Linda
Alexander, Neil B.
Huan, Xun
Wiens, Jenna
Sienko, Kathleen H.
author_sort Hauth, Jeremiah
collection PubMed
description Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time.
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spelling pubmed-83095442021-07-25 Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units Hauth, Jeremiah Jabri, Safa Kamran, Fahad Feleke, Eyoel W. Nigusie, Kaleab Ojeda, Lauro V. Handelzalts, Shirley Nyquist, Linda Alexander, Neil B. Huan, Xun Wiens, Jenna Sienko, Kathleen H. Sensors (Basel) Article Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time. MDPI 2021-07-07 /pmc/articles/PMC8309544/ /pubmed/34300399 http://dx.doi.org/10.3390/s21144661 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
Hauth, Jeremiah
Jabri, Safa
Kamran, Fahad
Feleke, Eyoel W.
Nigusie, Kaleab
Ojeda, Lauro V.
Handelzalts, Shirley
Nyquist, Linda
Alexander, Neil B.
Huan, Xun
Wiens, Jenna
Sienko, Kathleen H.
Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units
title Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units
title_full Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units
title_fullStr Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units
title_full_unstemmed Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units
title_short Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units
title_sort automated loss-of-balance event identification in older adults at risk of falls during real-world walking using wearable inertial measurement units
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309544/
https://www.ncbi.nlm.nih.gov/pubmed/34300399
http://dx.doi.org/10.3390/s21144661
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