Autoren:	Horst, Fabian Hoitz, Fabian Slijepcevic, Djordje Schons, Nicolas Beckmann, Hendrik Nigg, Benno M. Schöllhorn, Wolfgang I.
Titel:	Identification of subject-specific responses to footwear during running
Online-Publikationsdatum:	1-Dez-2023
Erscheinungsdatum:	2023
Sprache des Dokuments:	Englisch
Zusammenfassung/Abstract:	Placing a stronger focus on subject-specific responses to footwear may lead to a better functional understanding of footwear’s effect on running and its influence on comfort perception, performance, and pathogenesis of injuries. We investigated subject-specific responses to different footwear conditions within ground reaction force (GRF) data during running using a machine learning-based approach. We conducted our investigation in three steps, guided by the following hypotheses: (I) For each subject x footwear combination, unique GRF patterns can be identified. (II) For each subject, unique GRF characteristics can be identified across footwear conditions. (III) For each footwear condition, unique GRF characteristics can be identified across subjects. Thirty male subjects ran ten times at their preferred (self-selected) speed on a level and approximately 15 m long runway in four footwear conditions (barefoot and three standardised running shoes). We recorded three-dimensional GRFs for one right-foot stance phase per running trial and classified the GRFs using support vector machines. The highest median prediction accuracy of 96.2% was found for the subject x footwear classification (hypothesis I). Across footwear conditions, subjects could be discriminated with a median prediction accuracy of 80.0%. Across subjects, footwear conditions could be discriminated with a median prediction accuracy of 87.8%. Our results suggest that, during running, responses to footwear are unique to each subject and footwear design. As a result, considering subject-specific responses can contribute to a more differentiated functional understanding of footwear effects. Incorporating holistic analyses of biomechanical data is auspicious for the evaluation of (subject-specific) footwear effects, as unique interactions between subjects and footwear manifest in versatile ways. The applied machine learning methods have demonstrated their great potential to fathom subject-specific responses when evaluating and recommending footwear.
DDC-Sachgruppe:	796 Sport 796 Athletic and outdoor sports and games
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 02 Sozialwiss., Medien u. Sport
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9729
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Weitere Angaben zur Dokumentart:	Scientific article
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	Scientific reports 13
Seitenzahl oder Artikelnummer:	11284
Verlag:	Springer Nature
Verlagsort:	London
Erscheinungsdatum:	2023
ISSN:	2045-2322
DOI der Originalveröffentlichung:	10.1038/s41598-023-38090-0
Enthalten in den Sammlungen:	DFG-491381577-G