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Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors

In this work, a knee sleeve is presented for application in physical therapy applications relating to knee rehabilitation. The device is instrumented with sixteen piezoresistive sensors to measure knee angles during exercise, and can support at-home rehabilitation methods. The development of the dev...

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Autores principales: Wood, David S., Jensen, Kurt, Crane, Allison, Lee, Hyunwook, Dennis, Hayden, Gladwell, Joshua, Shurtz, Anne, Fullwood, David T., Seeley, Matthew K., Mitchell, Ulrike H., Christensen, William F., Bowden, Anton E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003122/
https://www.ncbi.nlm.nih.gov/pubmed/35408112
http://dx.doi.org/10.3390/s22072499
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author Wood, David S.
Jensen, Kurt
Crane, Allison
Lee, Hyunwook
Dennis, Hayden
Gladwell, Joshua
Shurtz, Anne
Fullwood, David T.
Seeley, Matthew K.
Mitchell, Ulrike H.
Christensen, William F.
Bowden, Anton E.
author_facet Wood, David S.
Jensen, Kurt
Crane, Allison
Lee, Hyunwook
Dennis, Hayden
Gladwell, Joshua
Shurtz, Anne
Fullwood, David T.
Seeley, Matthew K.
Mitchell, Ulrike H.
Christensen, William F.
Bowden, Anton E.
author_sort Wood, David S.
collection PubMed
description In this work, a knee sleeve is presented for application in physical therapy applications relating to knee rehabilitation. The device is instrumented with sixteen piezoresistive sensors to measure knee angles during exercise, and can support at-home rehabilitation methods. The development of the device is presented. Testing was performed on eighteen subjects, and knee angles were predicted using a machine learning regressor. Subject-specific and device-specific models are analyzed and presented. Subject-specific models average root mean square errors of 7.6 and 1.8 degrees for flexion/extension and internal/external rotation, respectively. Device-specific models average root mean square errors of 12.6 and 3.5 degrees for flexion/extension and internal/external rotation, respectively. The device presented in this work proved to be a repeatable, reusable, low-cost device that can adequately model the knee’s flexion/extension and internal/external rotation angles for rehabilitation purposes.
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spelling pubmed-90031222022-04-13 Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors Wood, David S. Jensen, Kurt Crane, Allison Lee, Hyunwook Dennis, Hayden Gladwell, Joshua Shurtz, Anne Fullwood, David T. Seeley, Matthew K. Mitchell, Ulrike H. Christensen, William F. Bowden, Anton E. Sensors (Basel) Article In this work, a knee sleeve is presented for application in physical therapy applications relating to knee rehabilitation. The device is instrumented with sixteen piezoresistive sensors to measure knee angles during exercise, and can support at-home rehabilitation methods. The development of the device is presented. Testing was performed on eighteen subjects, and knee angles were predicted using a machine learning regressor. Subject-specific and device-specific models are analyzed and presented. Subject-specific models average root mean square errors of 7.6 and 1.8 degrees for flexion/extension and internal/external rotation, respectively. Device-specific models average root mean square errors of 12.6 and 3.5 degrees for flexion/extension and internal/external rotation, respectively. The device presented in this work proved to be a repeatable, reusable, low-cost device that can adequately model the knee’s flexion/extension and internal/external rotation angles for rehabilitation purposes. MDPI 2022-03-24 /pmc/articles/PMC9003122/ /pubmed/35408112 http://dx.doi.org/10.3390/s22072499 Text en © 2022 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
Wood, David S.
Jensen, Kurt
Crane, Allison
Lee, Hyunwook
Dennis, Hayden
Gladwell, Joshua
Shurtz, Anne
Fullwood, David T.
Seeley, Matthew K.
Mitchell, Ulrike H.
Christensen, William F.
Bowden, Anton E.
Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors
title Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors
title_full Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors
title_fullStr Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors
title_full_unstemmed Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors
title_short Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors
title_sort accurate prediction of knee angles during open-chain rehabilitation exercises using a wearable array of nanocomposite stretch sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003122/
https://www.ncbi.nlm.nih.gov/pubmed/35408112
http://dx.doi.org/10.3390/s22072499
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