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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9003122 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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