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Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running
The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisabil...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351929/ https://www.ncbi.nlm.nih.gov/pubmed/34370747 http://dx.doi.org/10.1371/journal.pone.0248608 |
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author | Rivadulla, Adrian Chen, Xi Weir, Gillian Cazzola, Dario Trewartha, Grant Hamill, Joseph Preatoni, Ezio |
author_facet | Rivadulla, Adrian Chen, Xi Weir, Gillian Cazzola, Dario Trewartha, Grant Hamill, Joseph Preatoni, Ezio |
author_sort | Rivadulla, Adrian |
collection | PubMed |
description | The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and takes the distal tibia anteroposterior velocity, ankle dorsiflexion/plantar flexion angle and the anteroposterior and vertical velocities of the foot centre of mass as input features to predict the contact phase within a given gait cycle. The chosen model architecture underwent 5-fold cross-validation and the final model was tested in a subset of participants from each dataset (30%). Non-parametric Bland-Altman analyses (bias and [95% limits of agreement]) and root mean squared error (RMSE) were used to compare FootNet against the force data step event detection method. The association between detection errors and running speed, foot-strike angle and incline were also investigated. FootNet outperformed previously published algorithms (foot-strike bias = 0 [–10, 7] ms, RMSE = 5 ms; toe-off bias = 0 [–10, 10] ms, RMSE = 6 ms; and contact time bias = 0 [–15, 15] ms, RMSE = 8 ms) and proved robust to different running speeds, foot-strike angles and inclines. We have made FootNet’s source code publicly available for step event detection in treadmill running when force data are not available. |
format | Online Article Text |
id | pubmed-8351929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83519292021-08-10 Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running Rivadulla, Adrian Chen, Xi Weir, Gillian Cazzola, Dario Trewartha, Grant Hamill, Joseph Preatoni, Ezio PLoS One Research Article The accurate detection of foot-strike and toe-off is often critical in the assessment of running biomechanics. The gold standard method for step event detection requires force data which are not always available. Although kinematics-based algorithms can also be used, their accuracy and generalisability are limited, often requiring corrections for speed or foot-strike pattern. The purpose of this study was to develop FootNet, a novel kinematics and deep learning-based algorithm for the detection of step events in treadmill running. Five treadmill running datasets were gathered and processed to obtain segment and joint kinematics, and to identify the contact phase within each gait cycle using force data. The proposed algorithm is based on a long short-term memory recurrent neural network and takes the distal tibia anteroposterior velocity, ankle dorsiflexion/plantar flexion angle and the anteroposterior and vertical velocities of the foot centre of mass as input features to predict the contact phase within a given gait cycle. The chosen model architecture underwent 5-fold cross-validation and the final model was tested in a subset of participants from each dataset (30%). Non-parametric Bland-Altman analyses (bias and [95% limits of agreement]) and root mean squared error (RMSE) were used to compare FootNet against the force data step event detection method. The association between detection errors and running speed, foot-strike angle and incline were also investigated. FootNet outperformed previously published algorithms (foot-strike bias = 0 [–10, 7] ms, RMSE = 5 ms; toe-off bias = 0 [–10, 10] ms, RMSE = 6 ms; and contact time bias = 0 [–15, 15] ms, RMSE = 8 ms) and proved robust to different running speeds, foot-strike angles and inclines. We have made FootNet’s source code publicly available for step event detection in treadmill running when force data are not available. Public Library of Science 2021-08-09 /pmc/articles/PMC8351929/ /pubmed/34370747 http://dx.doi.org/10.1371/journal.pone.0248608 Text en © 2021 Rivadulla et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rivadulla, Adrian Chen, Xi Weir, Gillian Cazzola, Dario Trewartha, Grant Hamill, Joseph Preatoni, Ezio Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running |
title | Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running |
title_full | Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running |
title_fullStr | Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running |
title_full_unstemmed | Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running |
title_short | Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running |
title_sort | development and validation of footnet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351929/ https://www.ncbi.nlm.nih.gov/pubmed/34370747 http://dx.doi.org/10.1371/journal.pone.0248608 |
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