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Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to d...

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Autores principales: Rehman, Rana Zia Ur, Zhou, Yuhan, Del Din, Silvia, Alcock, Lisa, Hansen, Clint, Guan, Yu, Hortobágyi, Tibor, Maetzler, Walter, Rochester, Lynn, Lamoth, Claudine J. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729621/
https://www.ncbi.nlm.nih.gov/pubmed/33297395
http://dx.doi.org/10.3390/s20236992
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author Rehman, Rana Zia Ur
Zhou, Yuhan
Del Din, Silvia
Alcock, Lisa
Hansen, Clint
Guan, Yu
Hortobágyi, Tibor
Maetzler, Walter
Rochester, Lynn
Lamoth, Claudine J. C.
author_facet Rehman, Rana Zia Ur
Zhou, Yuhan
Del Din, Silvia
Alcock, Lisa
Hansen, Clint
Guan, Yu
Hortobágyi, Tibor
Maetzler, Walter
Rochester, Lynn
Lamoth, Claudine J. C.
author_sort Rehman, Rana Zia Ur
collection PubMed
description Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.
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spelling pubmed-77296212020-12-12 Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders Rehman, Rana Zia Ur Zhou, Yuhan Del Din, Silvia Alcock, Lisa Hansen, Clint Guan, Yu Hortobágyi, Tibor Maetzler, Walter Rochester, Lynn Lamoth, Claudine J. C. Sensors (Basel) Article Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making. MDPI 2020-12-07 /pmc/articles/PMC7729621/ /pubmed/33297395 http://dx.doi.org/10.3390/s20236992 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rehman, Rana Zia Ur
Zhou, Yuhan
Del Din, Silvia
Alcock, Lisa
Hansen, Clint
Guan, Yu
Hortobágyi, Tibor
Maetzler, Walter
Rochester, Lynn
Lamoth, Claudine J. C.
Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
title Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
title_full Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
title_fullStr Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
title_full_unstemmed Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
title_short Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
title_sort gait analysis with wearables can accurately classify fallers from non-fallers: a step toward better management of neurological disorders
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729621/
https://www.ncbi.nlm.nih.gov/pubmed/33297395
http://dx.doi.org/10.3390/s20236992
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