A time to exhaustion model during prolonged running based on wearable accelerometers
Defining relationships between running mechanisms and fatigue can be a major asset for optimising training. This article proposes a biomechanical model of time to exhaustion according to indicators derived from accelerometry data collected from the body. Ten volunteers were recruited for this study....
- Autores:
-
Provot, Thomas
Munera, Marcela
Chiementin, Xavier
Bolaers, Fabrice
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2019
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1499
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/1499
https://doi.org/10.1080/14763141.2018.1549682
- Palabra clave:
- Biomecánica
Modelo de fatiga deportiva
Stepwise regression
Biomechanical
Model sport fatigue
Regresión escalonada
Biomecánica
Modelo de fatiga deportiva
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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oai:repositorio.escuelaing.edu.co:001/1499 |
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Repositorio Institucional ECI |
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|
dc.title.spa.fl_str_mv |
A time to exhaustion model during prolonged running based on wearable accelerometers |
title |
A time to exhaustion model during prolonged running based on wearable accelerometers |
spellingShingle |
A time to exhaustion model during prolonged running based on wearable accelerometers Biomecánica Modelo de fatiga deportiva Stepwise regression Biomechanical Model sport fatigue Regresión escalonada Biomecánica Modelo de fatiga deportiva |
title_short |
A time to exhaustion model during prolonged running based on wearable accelerometers |
title_full |
A time to exhaustion model during prolonged running based on wearable accelerometers |
title_fullStr |
A time to exhaustion model during prolonged running based on wearable accelerometers |
title_full_unstemmed |
A time to exhaustion model during prolonged running based on wearable accelerometers |
title_sort |
A time to exhaustion model during prolonged running based on wearable accelerometers |
dc.creator.fl_str_mv |
Provot, Thomas Munera, Marcela Chiementin, Xavier Bolaers, Fabrice |
dc.contributor.author.none.fl_str_mv |
Provot, Thomas Munera, Marcela Chiementin, Xavier Bolaers, Fabrice |
dc.contributor.researchgroup.spa.fl_str_mv |
GiBiome |
dc.subject.armarc.none.fl_str_mv |
Biomecánica Modelo de fatiga deportiva |
topic |
Biomecánica Modelo de fatiga deportiva Stepwise regression Biomechanical Model sport fatigue Regresión escalonada Biomecánica Modelo de fatiga deportiva |
dc.subject.proposal.spa.fl_str_mv |
Stepwise regression Biomechanical Model sport fatigue Regresión escalonada Biomecánica Modelo de fatiga deportiva |
description |
Defining relationships between running mechanisms and fatigue can be a major asset for optimising training. This article proposes a biomechanical model of time to exhaustion according to indicators derived from accelerometry data collected from the body. Ten volunteers were recruited for this study. The participants were equipped with 3 accelerometers: on the right foot, at the tibia and at the L4-L5 lumbar spine. A running test was performed on a treadmill at 13.5 km/h until exhaustion. Thirty-one variables were deployed during the test. Multiple linear regressions were calculated to explain the time to exhaustion from the indicators calculated on the lumbar, tibia and foot individually and simultaneously. Time to exhaustion was predicted for simultaneous measurement points with r2=0.792 and 21 indicators; for the lumbar with r2=0.568 and 11 indicators; for the tibia with r2=558 and 11 indicators; and for the foot with r2=0.626 and 12 indicators. This study allows the accurate modelling of the time to exhaustion during a running-based test using indicators from accelerometer measurements. The individual models highlight that the location of the measurement point is important and that each location provides different information. Future studies should focus on homogeneous populations to improve predictions and errors. |
publishDate |
2019 |
dc.date.issued.none.fl_str_mv |
2019 |
dc.date.accessioned.none.fl_str_mv |
2021-05-27T00:06:09Z 2021-10-01T17:16:50Z |
dc.date.available.none.fl_str_mv |
2021-05-26 2021-10-01T17:16:50Z |
dc.type.spa.fl_str_mv |
Artículo de revista |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
format |
http://purl.org/coar/resource_type/c_2df8fbb1 |
status_str |
publishedVersion |
dc.identifier.issn.none.fl_str_mv |
1476-3141 |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.escuelaing.edu.co/handle/001/1499 |
dc.identifier.doi.none.fl_str_mv |
10.1080/14763141.2018.1549682 |
dc.identifier.url.none.fl_str_mv |
https://doi.org/10.1080/14763141.2018.1549682 |
identifier_str_mv |
1476-3141 10.1080/14763141.2018.1549682 |
url |
https://repositorio.escuelaing.edu.co/handle/001/1499 https://doi.org/10.1080/14763141.2018.1549682 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.citationedition.spa.fl_str_mv |
Sports Biomechanics : Volume 20, 2021 - Issue 3 |
dc.relation.citationendpage.spa.fl_str_mv |
14 |
dc.relation.citationstartpage.spa.fl_str_mv |
1 |
dc.relation.indexed.spa.fl_str_mv |
N/A |
dc.relation.ispartofjournal.spa.fl_str_mv |
Sports Biomechanics |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_14cb |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/closedAccess |
eu_rights_str_mv |
closedAccess |
rights_invalid_str_mv |
http://purl.org/coar/access_right/c_14cb |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Taylor and Francis Ltd. |
dc.publisher.place.spa.fl_str_mv |
Reino Unido |
dc.source.spa.fl_str_mv |
https://www.tandfonline.com/doi/abs/10.1080/14763141.2018.1549682?journalCode=rspb20 |
institution |
Escuela Colombiana de Ingeniería Julio Garavito |
bitstream.url.fl_str_mv |
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Provot, Thomasa84bc2cb9b879ed1ab78e3295dbcee15600Munera, Marcelaad6bfbf95a34697f0011b08cab1ec1cd600Chiementin, Xavier5381418aa944cc4518e7bd78724f965c600Bolaers, Fabricea9bea77498f5c6859824540a0d92f73d600GiBiome2021-05-27T00:06:09Z2021-10-01T17:16:50Z2021-05-262021-10-01T17:16:50Z20191476-3141https://repositorio.escuelaing.edu.co/handle/001/149910.1080/14763141.2018.1549682https://doi.org/10.1080/14763141.2018.1549682Defining relationships between running mechanisms and fatigue can be a major asset for optimising training. This article proposes a biomechanical model of time to exhaustion according to indicators derived from accelerometry data collected from the body. Ten volunteers were recruited for this study. The participants were equipped with 3 accelerometers: on the right foot, at the tibia and at the L4-L5 lumbar spine. A running test was performed on a treadmill at 13.5 km/h until exhaustion. Thirty-one variables were deployed during the test. Multiple linear regressions were calculated to explain the time to exhaustion from the indicators calculated on the lumbar, tibia and foot individually and simultaneously. Time to exhaustion was predicted for simultaneous measurement points with r2=0.792 and 21 indicators; for the lumbar with r2=0.568 and 11 indicators; for the tibia with r2=558 and 11 indicators; and for the foot with r2=0.626 and 12 indicators. This study allows the accurate modelling of the time to exhaustion during a running-based test using indicators from accelerometer measurements. The individual models highlight that the location of the measurement point is important and that each location provides different information. Future studies should focus on homogeneous populations to improve predictions and errors.Definir las relaciones entre los mecanismos de la carrera y la fatiga puede ser una baza importante para optimizar el entrenamiento. Este artículo propone un modelo biomecánico del tiempo hasta el agotamiento según los indicadores derivados de los datos de acelerometría recogidos del cuerpo. Se reclutaron diez voluntarios para este estudio. Los participantes estaban equipados con 3 acelerómetros: en el pie derecho, en la tibia y en la columna lumbar L4-L5. Se realizó una prueba de carrera en un tapiz rodante a 13,5 km/h hasta el agotamiento. Se desplegaron 31 variables durante la prueba. Se calcularon regresiones lineales múltiples para explicar el tiempo hasta el agotamiento a partir de los indicadores calculados en la zona lumbar, la tibia y el pie de forma individual y simultánea. El tiempo hasta el agotamiento se predijo para los puntos de medición simultáneos con r2=0,792 y 21 indicadores; para la zona lumbar con r2=0,568 y 11 indicadores; para la tibia con r2=558 y 11 indicadores; y para el pie con r2=0,626 y 12 indicadores. Este estudio permite modelar con precisión el tiempo hasta el agotamiento durante una prueba basada en la carrera utilizando indicadores procedentes de las mediciones del acelerómetro. Los modelos individuales ponen de manifiesto que la ubicación del punto de medición es importante y que cada ubicación proporciona información diferente. Los estudios futuros deberían centrarse en poblaciones homogéneas para mejorar las predicciones y los errores.application/pdfengTaylor and Francis Ltd.Reino Unidohttps://www.tandfonline.com/doi/abs/10.1080/14763141.2018.1549682?journalCode=rspb20A time to exhaustion model during prolonged running based on wearable accelerometersArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Sports Biomechanics : Volume 20, 2021 - Issue 3141N/ASports Biomechanicsinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbBiomecánicaModelo de fatiga deportivaStepwise regressionBiomechanicalModel sport fatigueRegresión escalonadaBiomecánicaModelo de fatiga deportivaORIGINALA time to exhaustion model during prolonged running based on wearable accelerometers.pdfA time to exhaustion model during prolonged running based 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