Titre
Estimation of horizontal running power using foot-worn inertial measurement units.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Auteur(s)
Apte, S.
Auteure/Auteur
Falbriard, M.
Auteure/Auteur
Meyer, F.
Auteure/Auteur
Millet, G.P.
Auteure/Auteur
Gremeaux, V.
Auteure/Auteur
Aminian, K.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
2296-4185
Statut éditorial
Publié
Date de publication
2023
Volume
11
Première page
1167816
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Résumé
Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.
PID Serval
serval:BIB_9F72BA2BF0E7
PMID
Open Access
Oui
Date de création
2023-07-13T11:53:49.517Z
Date de création dans IRIS
2025-05-20T22:42:57Z
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Nom
416. Apte FrontBBB23 horizontalRunningPower IMU.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
1.86 MB
Format
Adobe PDF
PID Serval
serval:BIB_9F72BA2BF0E7.P001
URN
urn:nbn:ch:serval-BIB_9F72BA2BF0E77
Somme de contrôle
(MD5):c497efd10b906540e5d32a2ccd69b773