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
Summary: | 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. |
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