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A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees

There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prostheti...

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Autores principales: Griffiths, Benjamin, Diment, Laura, Granat, Malcolm H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625063/
https://www.ncbi.nlm.nih.gov/pubmed/34833534
http://dx.doi.org/10.3390/s21227458
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author Griffiths, Benjamin
Diment, Laura
Granat, Malcolm H.
author_facet Griffiths, Benjamin
Diment, Laura
Granat, Malcolm H.
author_sort Griffiths, Benjamin
collection PubMed
description There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users.
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spelling pubmed-86250632021-11-27 A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees Griffiths, Benjamin Diment, Laura Granat, Malcolm H. Sensors (Basel) Article There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users. MDPI 2021-11-10 /pmc/articles/PMC8625063/ /pubmed/34833534 http://dx.doi.org/10.3390/s21227458 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
Griffiths, Benjamin
Diment, Laura
Granat, Malcolm H.
A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees
title A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees
title_full A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees
title_fullStr A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees
title_full_unstemmed A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees
title_short A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees
title_sort machine learning classification model for monitoring the daily physical behaviour of lower-limb amputees
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625063/
https://www.ncbi.nlm.nih.gov/pubmed/34833534
http://dx.doi.org/10.3390/s21227458
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