Accurate estimation of peak vertical ground reaction force using the duty factor in level treadmill running.

Détails

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Etat: Public
Version: Final published version
Licence: Non spécifiée
ID Serval
serval:BIB_985C16D71DE1
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Accurate estimation of peak vertical ground reaction force using the duty factor in level treadmill running.
Périodique
Scandinavian journal of medicine & science in sports
Auteur⸱e⸱s
Patoz A., Lussiana T., Breine B., Gindre C., Malatesta D.
ISSN
1600-0838 (Electronic)
ISSN-L
0905-7188
Statut éditorial
Publié
Date de publication
02/2023
Peer-reviewed
Oui
Volume
33
Numéro
2
Pages
169-177
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
This study aimed to (1) construct a statistical model (SMM) based on the duty factor (DF) to estimate the peak vertical ground reaction force ( ) and (2) to compare the estimated to force plate gold standard (GSM). One hundred and fifteen runners ran at 9, 11, and 13 km/h. Force (1000 Hz) and kinematic (200 Hz) data were acquired with an instrumented treadmill and an optoelectronic system, respectively, to assess force-plate and kinematic based DFs. SMM linearly relates to the inverse of DF because DF was analytically associated with the inverse of the average vertical force during ground contact time and the latter was very highly correlated to . No systematic bias and a 4% root mean square error (RMSE) were reported between GSM and SMM using force-plate based DF values when considering all running speeds together. Using kinematic based DF values, SMM reported a systematic but small bias (0.05BW) and a 5% RMSE when considering all running speeds together. These findings support the use of SMM to estimate during level treadmill runs at endurance speeds if underlying DF values are accurately measured.
Mots-clé
Humans, Running, Biomechanical Phenomena, Exercise Test, Nutritional Status, Models, Statistical, Gait, biomechanics, curve fitting, gait analysis, linear regression, statistical model
Pubmed
Web of science
Open Access
Oui
Création de la notice
02/11/2022 9:19
Dernière modification de la notice
19/01/2023 7:53
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