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Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors
One of the most important forces generated during gait is the vertical ground reaction force (vGRF). This force can be measured using force plates, but these can limit the scope of gait analysis. This paper presents a method to estimate the vGRF using inertial measurement units (IMU) and machine lea...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570513/ https://www.ncbi.nlm.nih.gov/pubmed/37840666 http://dx.doi.org/10.3389/fbioe.2023.1199459 |
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author | Martínez-Pascual, David Catalán, José M. Blanco-Ivorra, Andrea Sanchís, Mónica Arán-Ais, Francisca García-Aracil, Nicolás |
author_facet | Martínez-Pascual, David Catalán, José M. Blanco-Ivorra, Andrea Sanchís, Mónica Arán-Ais, Francisca García-Aracil, Nicolás |
author_sort | Martínez-Pascual, David |
collection | PubMed |
description | One of the most important forces generated during gait is the vertical ground reaction force (vGRF). This force can be measured using force plates, but these can limit the scope of gait analysis. This paper presents a method to estimate the vGRF using inertial measurement units (IMU) and machine learning techniques. Four wearable IMUs were used to obtain flexion/extension angles of the hip, knee, and ankle joints, and an IMU placed over the C7 vertebra to measure vertical acceleration. We trained and compared the performance of two machine learning algorithms: feedforward neural networks (FNN) and random forest (RF). We investigated the importance of the inputs introduced into the models and analyzed in detail the contribution of lower limb kinematics and vertical acceleration to model performance. The results suggest that the inclusion of vertical acceleration increases the root mean square error in the FNN, while the RF appears to decrease it. We also analyzed the ability of the models to construct the force signal, with particular emphasis on the magnitude and timing of the vGRF peaks. Using the proposed method, we concluded that FNN and RF models can estimate the vGRF with high accuracy. |
format | Online Article Text |
id | pubmed-10570513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105705132023-10-14 Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors Martínez-Pascual, David Catalán, José M. Blanco-Ivorra, Andrea Sanchís, Mónica Arán-Ais, Francisca García-Aracil, Nicolás Front Bioeng Biotechnol Bioengineering and Biotechnology One of the most important forces generated during gait is the vertical ground reaction force (vGRF). This force can be measured using force plates, but these can limit the scope of gait analysis. This paper presents a method to estimate the vGRF using inertial measurement units (IMU) and machine learning techniques. Four wearable IMUs were used to obtain flexion/extension angles of the hip, knee, and ankle joints, and an IMU placed over the C7 vertebra to measure vertical acceleration. We trained and compared the performance of two machine learning algorithms: feedforward neural networks (FNN) and random forest (RF). We investigated the importance of the inputs introduced into the models and analyzed in detail the contribution of lower limb kinematics and vertical acceleration to model performance. The results suggest that the inclusion of vertical acceleration increases the root mean square error in the FNN, while the RF appears to decrease it. We also analyzed the ability of the models to construct the force signal, with particular emphasis on the magnitude and timing of the vGRF peaks. Using the proposed method, we concluded that FNN and RF models can estimate the vGRF with high accuracy. Frontiers Media S.A. 2023-09-29 /pmc/articles/PMC10570513/ /pubmed/37840666 http://dx.doi.org/10.3389/fbioe.2023.1199459 Text en Copyright © 2023 Martínez-Pascual, Catalán, Blanco-Ivorra, Sanchís, Arán-Ais and García-Aracil. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Martínez-Pascual, David Catalán, José M. Blanco-Ivorra, Andrea Sanchís, Mónica Arán-Ais, Francisca García-Aracil, Nicolás Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors |
title | Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors |
title_full | Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors |
title_fullStr | Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors |
title_full_unstemmed | Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors |
title_short | Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors |
title_sort | estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570513/ https://www.ncbi.nlm.nih.gov/pubmed/37840666 http://dx.doi.org/10.3389/fbioe.2023.1199459 |
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