Clustering Runners' Response to Different Midsole Stack Heights: A Field Study.

cluster analysis principal component analysis running biomechanics running shoes wearable sensor

Journal

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

Informations de publication

Date de publication:
19 Jul 2024
Historique:
received: 21 06 2024
revised: 08 07 2024
accepted: 15 07 2024
medline: 27 7 2024
pubmed: 27 7 2024
entrez: 27 7 2024
Statut: epublish

Résumé

Advanced footwear technology featuring stack heights higher than 30 mm has been proven to improve running economy in elite and recreational runners. While it is understood that the physiological benefit is highly individual, the individual biomechanical response to different stack heights remains unclear. Thirty-one runners performed running trials with three different shoe conditions of 25 mm, 35 mm, and 45 mm stack height on an outdoor running course wearing a STRYD sensor. The STRYD running variables for each participant were normalized to the 25 mm shoe condition and used to cluster participants into three distinct groups. Each cluster showed unique running patterns, with leg spring stiffness and vertical oscillation contributing most to the variance. No significant differences were found between clusters in terms of body height, body weight, leg length, and running speed. This study indicates that runners change running patterns individually when running with footwear featuring different stack heights. Clustering these patterns can help understand subgroups of runners and potentially support running shoe recommendations.

Identifiants

pubmed: 39066091
pii: s24144694
doi: 10.3390/s24144694
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Jannik Koegel (J)

Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany.
adidas AG, 91074 Herzogenaurach, Germany.

Stacy Huerta (S)

Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany.

Markus Gambietz (M)

Autonomous Systems and Mechatronics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany.

Martin Ullrich (M)

adidas AG, 91074 Herzogenaurach, Germany.

Christian Heyde (C)

adidas AG, 91074 Herzogenaurach, Germany.

Eva Dorschky (E)

Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany.

Bjoern Eskofier (B)

Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany.

Classifications MeSH