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Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning
Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133596/ https://www.ncbi.nlm.nih.gov/pubmed/35646876 http://dx.doi.org/10.3389/fbioe.2022.877347 |
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author | Boukhennoufa, Issam Altai, Zainab Zhai, Xiaojun Utti, Victor McDonald-Maier, Klaus D Liew, Bernard X. W. |
author_facet | Boukhennoufa, Issam Altai, Zainab Zhai, Xiaojun Utti, Victor McDonald-Maier, Klaus D Liew, Bernard X. W. |
author_sort | Boukhennoufa, Issam |
collection | PubMed |
description | Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants n = 33). The outcome for prediction was the internal knee abduction impulse over the stance phase. Three-dimensional (3D) angular and linear displacement, velocity, and acceleration of the seven lower body segment’s center of mass (COM), relative to a fixed global coordinate system were derived and formed the predictor space (126 time-series predictors). The total number of observations in the dataset was 6,737. The datasets were split into training (75%, n = 5,052) and testing (25%, n = 1685) datasets. Five deep learning models were benchmarked against inverse dynamics in quantifying knee abduction impulse. A baseline 2D convolutional network model achieved a mean absolute percentage error (MAPE) of 10.80%. Transfer learning with InceptionTime was the best performing model, achieving the best MAPE of 8.28%. Encoding the time-series as images then using a 2D convolutional model performed worse than the baseline model with a MAPE of 16.17%. Time-series based deep learning models were superior to an image-based method when predicting knee abduction moment impulse during walking. Future studies looking to develop wearable technologies will benefit from knowing the optimal network architecture, and the benefit of transfer learning for predicting joint moments. |
format | Online Article Text |
id | pubmed-9133596 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91335962022-05-27 Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning Boukhennoufa, Issam Altai, Zainab Zhai, Xiaojun Utti, Victor McDonald-Maier, Klaus D Liew, Bernard X. W. Front Bioeng Biotechnol Bioengineering and Biotechnology Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants n = 33). The outcome for prediction was the internal knee abduction impulse over the stance phase. Three-dimensional (3D) angular and linear displacement, velocity, and acceleration of the seven lower body segment’s center of mass (COM), relative to a fixed global coordinate system were derived and formed the predictor space (126 time-series predictors). The total number of observations in the dataset was 6,737. The datasets were split into training (75%, n = 5,052) and testing (25%, n = 1685) datasets. Five deep learning models were benchmarked against inverse dynamics in quantifying knee abduction impulse. A baseline 2D convolutional network model achieved a mean absolute percentage error (MAPE) of 10.80%. Transfer learning with InceptionTime was the best performing model, achieving the best MAPE of 8.28%. Encoding the time-series as images then using a 2D convolutional model performed worse than the baseline model with a MAPE of 16.17%. Time-series based deep learning models were superior to an image-based method when predicting knee abduction moment impulse during walking. Future studies looking to develop wearable technologies will benefit from knowing the optimal network architecture, and the benefit of transfer learning for predicting joint moments. Frontiers Media S.A. 2022-05-12 /pmc/articles/PMC9133596/ /pubmed/35646876 http://dx.doi.org/10.3389/fbioe.2022.877347 Text en Copyright © 2022 Boukhennoufa, Altai, Zhai, Utti, McDonald-Maier and Liew. 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 Boukhennoufa, Issam Altai, Zainab Zhai, Xiaojun Utti, Victor McDonald-Maier, Klaus D Liew, Bernard X. W. Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning |
title | Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning |
title_full | Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning |
title_fullStr | Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning |
title_full_unstemmed | Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning |
title_short | Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning |
title_sort | predicting the internal knee abduction impulse during walking using deep learning |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133596/ https://www.ncbi.nlm.nih.gov/pubmed/35646876 http://dx.doi.org/10.3389/fbioe.2022.877347 |
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