A Bayesian approach for pervasive estimation of breaststroke velocity using a wearable IMU

Détails

ID Serval
serval:BIB_438748039AF3
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
A Bayesian approach for pervasive estimation of breaststroke velocity using a wearable IMU
Périodique
Pervasive and Mobile Computing
Auteur(s)
Dadashi F., Millet G.P., Aminian K.
ISSN
1574-1192
Statut éditorial
Publié
Date de publication
2014
Peer-reviewed
Oui
Pages
1-
Langue
anglais
Résumé
A ubiquitous assessment of swimming velocity (main metric of the performance) is essential for the coach to provide a tailored feedback to the trainee. We present a probabilistic framework for the data-driven estimation of the swimming velocity at every cycle using a low-cost wearable inertial measurement unit (IMU). The statistical validation of the method on 15 swimmers shows that an average relative error of 0.1 ± 9.6% and high correlation with the tethered reference system (rX,Y=0.91
) is achievable. Besides, a simple tool to analyze the influence of sacrum kinematics on the performance is provided.
Mots-clé
Bayesian learning, Breaststroke, Performance, Pervasive velocity estimation, Wearable IMU
Web of science
Création de la notice
25/02/2014 15:34
Dernière modification de la notice
20/08/2019 13:47
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