Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Caparelli,Thiago Bruno
Data de Publicação: 2017
Outros Autores: Naves,Eduardo Lázaro Martins
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=S2446-47402017000300229
Resumo: Abstract Introduction Historically, assessing the quality of human gait has been a difficult process. Advanced studies can be conducted using modern 3D systems. However, due to their high cost, usage of these 3D systems is still restricted to research environments. 2D systems offer simpler and more affordable solutions. Methods In this study, the gait of 40 volunteers walking on a treadmill was recorded in the sagittal plane, using a 2D motion capture system. The extracted joint angles data were used to create cyclograms. Sections of the cyclograms were used as inputs to artificial neural networks (ANNs), since they can represent the kinematic behavior of the lower body. This allowed for prediction of future states of the moving body. Results The results indicate that ANNs can predict the future states of the gait with high accuracy. Both single point and section predictions were successfully performed. Pearson’s correlation coefficient and matched-pairs t-test ensured that the results were statistically significant. Conclusion The combined use of ANNs and simple, accessible hardware is of great value in clinical practice. The use of cyclograms facilitates the analysis, as several gait characteristics can be easily recognized by their geometric shape. The predictive model presented in this paper facilitates generation of data that can be used in robotic locomotion therapy as a control signal or feedback element, aiding in the rehabilitation process of patients with motor dysfunction. The system proposes an interesting tool that can be explored to increase rehabilitation possibilities, providing better quality of life to patients.
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spelling Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networksGaitCyclogramArtificial Neural NetworkAbstract Introduction Historically, assessing the quality of human gait has been a difficult process. Advanced studies can be conducted using modern 3D systems. However, due to their high cost, usage of these 3D systems is still restricted to research environments. 2D systems offer simpler and more affordable solutions. Methods In this study, the gait of 40 volunteers walking on a treadmill was recorded in the sagittal plane, using a 2D motion capture system. The extracted joint angles data were used to create cyclograms. Sections of the cyclograms were used as inputs to artificial neural networks (ANNs), since they can represent the kinematic behavior of the lower body. This allowed for prediction of future states of the moving body. Results The results indicate that ANNs can predict the future states of the gait with high accuracy. Both single point and section predictions were successfully performed. Pearson’s correlation coefficient and matched-pairs t-test ensured that the results were statistically significant. Conclusion The combined use of ANNs and simple, accessible hardware is of great value in clinical practice. The use of cyclograms facilitates the analysis, as several gait characteristics can be easily recognized by their geometric shape. The predictive model presented in this paper facilitates generation of data that can be used in robotic locomotion therapy as a control signal or feedback element, aiding in the rehabilitation process of patients with motor dysfunction. The system proposes an interesting tool that can be explored to increase rehabilitation possibilities, providing better quality of life to patients.Sociedade Brasileira de Engenharia Biomédica2017-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402017000300229Research on Biomedical Engineering v.33 n.3 2017reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.01017info:eu-repo/semantics/openAccessCaparelli,Thiago BrunoNaves,Eduardo Lázaro Martinseng2018-08-02T00:00:00Zoai:scielo:S2446-47402017000300229Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2018-08-02T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false
dc.title.none.fl_str_mv Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks
title Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks
spellingShingle Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks
Caparelli,Thiago Bruno
Gait
Cyclogram
Artificial Neural Network
title_short Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks
title_full Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks
title_fullStr Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks
title_full_unstemmed Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks
title_sort Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks
author Caparelli,Thiago Bruno
author_facet Caparelli,Thiago Bruno
Naves,Eduardo Lázaro Martins
author_role author
author2 Naves,Eduardo Lázaro Martins
author2_role author
dc.contributor.author.fl_str_mv Caparelli,Thiago Bruno
Naves,Eduardo Lázaro Martins
dc.subject.por.fl_str_mv Gait
Cyclogram
Artificial Neural Network
topic Gait
Cyclogram
Artificial Neural Network
description Abstract Introduction Historically, assessing the quality of human gait has been a difficult process. Advanced studies can be conducted using modern 3D systems. However, due to their high cost, usage of these 3D systems is still restricted to research environments. 2D systems offer simpler and more affordable solutions. Methods In this study, the gait of 40 volunteers walking on a treadmill was recorded in the sagittal plane, using a 2D motion capture system. The extracted joint angles data were used to create cyclograms. Sections of the cyclograms were used as inputs to artificial neural networks (ANNs), since they can represent the kinematic behavior of the lower body. This allowed for prediction of future states of the moving body. Results The results indicate that ANNs can predict the future states of the gait with high accuracy. Both single point and section predictions were successfully performed. Pearson’s correlation coefficient and matched-pairs t-test ensured that the results were statistically significant. Conclusion The combined use of ANNs and simple, accessible hardware is of great value in clinical practice. The use of cyclograms facilitates the analysis, as several gait characteristics can be easily recognized by their geometric shape. The predictive model presented in this paper facilitates generation of data that can be used in robotic locomotion therapy as a control signal or feedback element, aiding in the rehabilitation process of patients with motor dysfunction. The system proposes an interesting tool that can be explored to increase rehabilitation possibilities, providing better quality of life to patients.
publishDate 2017
dc.date.none.fl_str_mv 2017-09-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.language.iso.fl_str_mv eng
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dc.relation.none.fl_str_mv 10.1590/2446-4740.01017
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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.33 n.3 2017
reponame:Research on Biomedical Engineering (Online)
instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)
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reponame_str Research on Biomedical Engineering (Online)
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