Development of a simple algorithm to detect big air jumps and jumps during skiing.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 09 11 2023
accepted: 02 07 2024
medline: 18 7 2024
pubmed: 18 7 2024
entrez: 18 7 2024
Statut: epublish

Résumé

Jumping is an important task in skiing, snowboarding, ski jumping, figure skating, volleyball and many other sports. In these examples, jumping tasks are a performance criterion, and therefore detailed insight into them is important for athletes and coaches. Therefore, this paper aims to introduce a simple and easy-to-implement jump detection algorithm for skiing using acceleration data from inertial measurement units attached to ski boots. The algorithm uses the average of the absolute vertical accelerations of the two boots. We provide results for different parameter settings of the algorithm and two types of jumps: Big Air jumps and jumps during skiing. The latter are divided into small (time of flight < 500 ms) and medium (time of flight ≥ 500 ms) jumps. The algorithm detects 100% of Big Air, 94% of medium and 44% of small jumps. In addition, the settings with the highest detection rates also have the highest number of overdetected jumps. To resolve this conflict, a penalty-adjusted score that considers the number of overdetected jumps in the final performance analysis is proposed.

Identifiants

pubmed: 39024400
doi: 10.1371/journal.pone.0307255
pii: PONE-D-23-36744
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0307255

Informations de copyright

Copyright: © 2024 Kranzinger et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Stefan Kranzinger (S)

Salzburg Research Forschungsgesellschaft mbH, Human Motion Analytics, Salzburg, Austria.

Christina Kranzinger (C)

Salzburg Research Forschungsgesellschaft mbH, Human Motion Analytics, Salzburg, Austria.

Aaron Martinez Alvarez (A)

Red Bull Athlete Performance Center Los Angeles, Santa Monica, CA, United States of America.

Thomas Stöggl (T)

Red Bull Athlete Performance Center Salzburg, Salzburg, Austria.

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