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Estimating Stair Running Performance Using Inertial Sensors
Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713493/ https://www.ncbi.nlm.nih.gov/pubmed/29149063 http://dx.doi.org/10.3390/s17112647 |
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author | Ojeda, Lauro V. Zaferiou, Antonia M. Cain, Stephen M. Vitali, Rachel V. Davidson, Steven P. Stirling, Leia A. Perkins, Noel C. |
author_facet | Ojeda, Lauro V. Zaferiou, Antonia M. Cain, Stephen M. Vitali, Rachel V. Davidson, Steven P. Stirling, Leia A. Perkins, Noel C. |
author_sort | Ojeda, Lauro V. |
collection | PubMed |
description | Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time. |
format | Online Article Text |
id | pubmed-5713493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57134932017-12-07 Estimating Stair Running Performance Using Inertial Sensors Ojeda, Lauro V. Zaferiou, Antonia M. Cain, Stephen M. Vitali, Rachel V. Davidson, Steven P. Stirling, Leia A. Perkins, Noel C. Sensors (Basel) Article Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time. MDPI 2017-11-17 /pmc/articles/PMC5713493/ /pubmed/29149063 http://dx.doi.org/10.3390/s17112647 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ojeda, Lauro V. Zaferiou, Antonia M. Cain, Stephen M. Vitali, Rachel V. Davidson, Steven P. Stirling, Leia A. Perkins, Noel C. Estimating Stair Running Performance Using Inertial Sensors |
title | Estimating Stair Running Performance Using Inertial Sensors |
title_full | Estimating Stair Running Performance Using Inertial Sensors |
title_fullStr | Estimating Stair Running Performance Using Inertial Sensors |
title_full_unstemmed | Estimating Stair Running Performance Using Inertial Sensors |
title_short | Estimating Stair Running Performance Using Inertial Sensors |
title_sort | estimating stair running performance using inertial sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713493/ https://www.ncbi.nlm.nih.gov/pubmed/29149063 http://dx.doi.org/10.3390/s17112647 |
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