Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Boukhennoufa, Issam, Altai, Zainab, Zhai, Xiaojun, Utti, Victor, McDonald-Maier, Klaus D, Liew, Bernard X. W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784713604520476672
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
work_keys_str_mv AT boukhennoufaissam predictingtheinternalkneeabductionimpulseduringwalkingusingdeeplearning
AT altaizainab predictingtheinternalkneeabductionimpulseduringwalkingusingdeeplearning
AT zhaixiaojun predictingtheinternalkneeabductionimpulseduringwalkingusingdeeplearning
AT uttivictor predictingtheinternalkneeabductionimpulseduringwalkingusingdeeplearning
AT mcdonaldmaierklausd predictingtheinternalkneeabductionimpulseduringwalkingusingdeeplearning
AT liewbernardxw predictingtheinternalkneeabductionimpulseduringwalkingusingdeeplearning