Prediction of 3D ground reaction forces during gait based on accelerometer data

Detalhes bibliográficos
Autor(a) principal: Leporace,Gustavo
Data de Publicação: 2018
Outros Autores: Batista,Luiz Alberto, Nadal,Jurandir
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Research on Biomedical Engineering (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300211
Resumo: Abstract Introduction The aim of this study was to predict 3D ground reaction force signals based on accelerometer data during gait, using a feed-forward neural network (MLP). Methods Seventeen healthy subjects were instructed to walk at a self-selected speed with a 3D accelerometer attached to the distal and anterior part of the shank. A force plate was embedded into the middle of the walkway. MLP neural networks with one hidden layer and three output layers were selected to simulate the anteroposterior (AP), vertical (Vert) and mediolateral (ML) ground reaction forces (GRF). The input layer was composed of fourteen inputs obtained from accelerometer signals, selected based on previous studies. Principal component analysis (PCA) was used to compare the simulated and collected curves. The Pearson correlation coefficient and the mean absolute deviation (MAD) between signals were calculated. Results PCA identified small, but significant differences between collected and simulated signals in the loading response phases of AP and ML GRF, while Vert did not show differences. The correlation between the simulated and collected signals was high (AP: 0.97; Vert: 0.98; ML: 0.80). MAD was 1.8%BW for AP, 4.5%BW for Vert and 1.4%BW for ML. Conclusion This study confirmed that multilayer perceptron neural network can predict the highly non-linear relationship of shank acceleration parameters and ground reaction forces, as well as other studies have done using plantar pressure devices. The greater advantages of this device are the low cost and the possibility of use outside the laboratory environment.
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spelling Prediction of 3D ground reaction forces during gait based on accelerometer dataAccelerationForceGaitFeedforward neural networkSimulationAbstract Introduction The aim of this study was to predict 3D ground reaction force signals based on accelerometer data during gait, using a feed-forward neural network (MLP). Methods Seventeen healthy subjects were instructed to walk at a self-selected speed with a 3D accelerometer attached to the distal and anterior part of the shank. A force plate was embedded into the middle of the walkway. MLP neural networks with one hidden layer and three output layers were selected to simulate the anteroposterior (AP), vertical (Vert) and mediolateral (ML) ground reaction forces (GRF). The input layer was composed of fourteen inputs obtained from accelerometer signals, selected based on previous studies. Principal component analysis (PCA) was used to compare the simulated and collected curves. The Pearson correlation coefficient and the mean absolute deviation (MAD) between signals were calculated. Results PCA identified small, but significant differences between collected and simulated signals in the loading response phases of AP and ML GRF, while Vert did not show differences. The correlation between the simulated and collected signals was high (AP: 0.97; Vert: 0.98; ML: 0.80). MAD was 1.8%BW for AP, 4.5%BW for Vert and 1.4%BW for ML. Conclusion This study confirmed that multilayer perceptron neural network can predict the highly non-linear relationship of shank acceleration parameters and ground reaction forces, as well as other studies have done using plantar pressure devices. The greater advantages of this device are the low cost and the possibility of use outside the laboratory environment.Sociedade Brasileira de Engenharia Biomédica2018-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300211Research on Biomedical Engineering v.34 n.3 2018reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.06817info:eu-repo/semantics/openAccessLeporace,GustavoBatista,Luiz AlbertoNadal,Jurandireng2018-10-30T00:00:00Zoai:scielo:S2446-47402018000300211Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2018-10-30T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false
dc.title.none.fl_str_mv Prediction of 3D ground reaction forces during gait based on accelerometer data
title Prediction of 3D ground reaction forces during gait based on accelerometer data
spellingShingle Prediction of 3D ground reaction forces during gait based on accelerometer data
Leporace,Gustavo
Acceleration
Force
Gait
Feedforward neural network
Simulation
title_short Prediction of 3D ground reaction forces during gait based on accelerometer data
title_full Prediction of 3D ground reaction forces during gait based on accelerometer data
title_fullStr Prediction of 3D ground reaction forces during gait based on accelerometer data
title_full_unstemmed Prediction of 3D ground reaction forces during gait based on accelerometer data
title_sort Prediction of 3D ground reaction forces during gait based on accelerometer data
author Leporace,Gustavo
author_facet Leporace,Gustavo
Batista,Luiz Alberto
Nadal,Jurandir
author_role author
author2 Batista,Luiz Alberto
Nadal,Jurandir
author2_role author
author
dc.contributor.author.fl_str_mv Leporace,Gustavo
Batista,Luiz Alberto
Nadal,Jurandir
dc.subject.por.fl_str_mv Acceleration
Force
Gait
Feedforward neural network
Simulation
topic Acceleration
Force
Gait
Feedforward neural network
Simulation
description Abstract Introduction The aim of this study was to predict 3D ground reaction force signals based on accelerometer data during gait, using a feed-forward neural network (MLP). Methods Seventeen healthy subjects were instructed to walk at a self-selected speed with a 3D accelerometer attached to the distal and anterior part of the shank. A force plate was embedded into the middle of the walkway. MLP neural networks with one hidden layer and three output layers were selected to simulate the anteroposterior (AP), vertical (Vert) and mediolateral (ML) ground reaction forces (GRF). The input layer was composed of fourteen inputs obtained from accelerometer signals, selected based on previous studies. Principal component analysis (PCA) was used to compare the simulated and collected curves. The Pearson correlation coefficient and the mean absolute deviation (MAD) between signals were calculated. Results PCA identified small, but significant differences between collected and simulated signals in the loading response phases of AP and ML GRF, while Vert did not show differences. The correlation between the simulated and collected signals was high (AP: 0.97; Vert: 0.98; ML: 0.80). MAD was 1.8%BW for AP, 4.5%BW for Vert and 1.4%BW for ML. Conclusion This study confirmed that multilayer perceptron neural network can predict the highly non-linear relationship of shank acceleration parameters and ground reaction forces, as well as other studies have done using plantar pressure devices. The greater advantages of this device are the low cost and the possibility of use outside the laboratory environment.
publishDate 2018
dc.date.none.fl_str_mv 2018-09-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300211
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000300211
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/2446-4740.06817
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
publisher.none.fl_str_mv Sociedade Brasileira de Engenharia Biomédica
dc.source.none.fl_str_mv Research on Biomedical Engineering v.34 n.3 2018
reponame:Research on Biomedical Engineering (Online)
instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)
instacron:SBEB
instname_str Sociedade Brasileira de Engenharia Biomédica (SBEB)
instacron_str SBEB
institution SBEB
reponame_str Research on Biomedical Engineering (Online)
collection Research on Biomedical Engineering (Online)
repository.name.fl_str_mv Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)
repository.mail.fl_str_mv ||rbe@rbejournal.org
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