Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques.

decision tree human running random forest regression wearable devices

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
25 Nov 2020
Historique:
received: 12 10 2020
revised: 18 11 2020
accepted: 21 11 2020
entrez: 1 12 2020
pubmed: 2 12 2020
medline: 2 12 2020
Statut: epublish

Résumé

The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse information during ecological running. The purpose of the current study was to predict foot strike angle and classify foot strike pattern from Loadsol

Identifiants

pubmed: 33255671
pii: s20236737
doi: 10.3390/s20236737
pmc: PMC7728139
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Bundesministers fürWissenschaft, Forschung und Wirtschaft
ID : COMET: Digital Motion in Sports, Fitness, & Well-being (872574)
Organisme : Bundesministerium für Digitalisierung und Wirtschaftsstandort
ID : COMET: Digital Motion in Sports, Fitness, & Well-being (872574)

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Auteurs

Stephanie R Moore (SR)

Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein/Rif, Austria.

Christina Kranzinger (C)

Salzburg Research Forschungsgesellschaft m.b.H., Jakob-Haringer-Straße 5, 5020 Salzburg, Austria.

Julian Fritz (J)

Adidas AG, Adi-Dassler-Strasse 1, 91074 Herzogenaurach, Germany.

Thomas Stӧggl (T)

Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein/Rif, Austria.
Athlete Performance Center, Red Bull Sports, Brunnbachweg 71, 5303 Thalgau, Austria.

Josef Krӧll (J)

Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein/Rif, Austria.

Hermann Schwameder (H)

Department of Sport and Exercise Science, University of Salzburg, Schlossallee 49, 5400 Hallein/Rif, Austria.

Classifications MeSH