Identification of subject-specific responses to footwear during running

Abstract

AbstractPlacing 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.

Autoren

Fabian Horst
Fabian Hoitz
Djordje Slijepcevic
Nicolas Schons
Hendrik Beckmann
Benno M Nigg
Wolfgang I Schöllhorn

DOI

10.1038/s41598-023-38090-0

eISSN

2045-2322

Ausgabe der Veröffentlichung

1

Zeitschrift

Scientific Reports

Sprache

en

Artikelnummer

11284

Online publication date

2023

Status

Published online

Herausgeber

Springer Science and Business Media LLC

Herausgeber URL

http://dx.doi.org/10.1038/s41598-023-38090-0

Datum der Datenerfassung

2023

Titel

Identification of subject-specific responses to footwear during running

Ausgabe der Zeitschrift

13

Datenquelle: Crossref

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.

Addresses

Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Mainz, Germany. horst@uni-mainz.de.

Autoren

Fabian Horst
Fabian Hoitz
Djordje Slijepcevic
Nicolas Schons
Hendrik Beckmann
Benno M Nigg
Wolfgang I Schöllhorn

DOI

10.1038/s41598-023-38090-0

eISSN

2045-2322

Externe Identifier

PubMed Identifier: 37438380
PubMed Central ID: PMC10338529

Funding acknowledgements

Johannes Gutenberg-Universität Mainz:
Gesellschaft für Forschungsförderung Niederösterreich: IntelliGait3D (#FTI17-014)

Open access

true

ISSN

2045-2322

Ausgabe der Veröffentlichung

1

Zeitschrift

Scientific reports

Schlüsselwörter

Foot
Humans
Records
Running
Male
Gonadotropin-Releasing Hormone
Machine Learning

Sprache

eng

Medium

Electronic

Online publication date

2023

Open access status

Open Access

Paginierung

11284

Datum der Veröffentlichung

2023

Status

Published

Publisher licence

CC BY

Datum der Datenerfassung

2023

Titel

Identification of subject-specific responses to footwear during running.

Sub types

Research Support, Non-U.S. Gov't
research-article
Journal Article

Ausgabe der Zeitschrift

13

Files

https://www.nature.com/articles/s41598-023-38090-0.pdf https://europepmc.org/articles/PMC10338529?pdf=render

Datenquelle: Europe PubMed Central

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.

Date of acceptance

2023

Autoren

Fabian Horst
Fabian Hoitz
Djordje Slijepcevic
Nicolas Schons
Hendrik Beckmann
Benno M Nigg
Wolfgang I Schöllhorn

Autoren-URL

https://www.ncbi.nlm.nih.gov/pubmed/37438380

DOI

10.1038/s41598-023-38090-0

eISSN

2045-2322

Externe Identifier

PubMed Central ID: PMC10338529

Ausgabe der Veröffentlichung

1

Zeitschrift

Sci Rep

Schlüsselwörter

Humans
Male
Foot
Gonadotropin-Releasing Hormone
Machine Learning
Records
Running

Sprache

eng

Country

England

Paginierung

11284

PII

10.1038/s41598-023-38090-0

Datum der Veröffentlichung

2023

Status

Published online

Datum, an dem der Datensatz öffentlich gemacht wurde

2023

Titel

Identification of subject-specific responses to footwear during running.

Sub types

Journal Article
Research Support, Non-U.S. Gov't

Ausgabe der Zeitschrift

13

Datenquelle: PubMed

Author's licence

CC-BY

Autoren

Fabian Horst
Fabian Hoitz
Djordje Slijepcevic
Nicolas Schons
Hendrik Beckmann
Benno M Nigg
Wolfgang I Schöllhorn

Hosting institution

Universitätsbibliothek Mainz

Sammlungen

DFG-491381577-G

Resource version

Published version

DOI

10.1038/s41598-023-38090-0

File(s) embargoed

false

Open access

true

ISSN

2045-2322

Zeitschrift

Scientific reports

Schlüsselwörter

796 Sport
796 Athletic and outdoor sports and games

Sprache

eng

Open access status

Open Access

Paginierung

11284

Datum der Veröffentlichung

2023

Public URL

https://openscience.ub.uni-mainz.de/handle/20.500.12030/9747

Herausgeber

Springer Nature

Datum der Datenerfassung

2023

Datum, an dem der Datensatz öffentlich gemacht wurde

2023

Zugang

Public

Titel

Identification of subject-specific responses to footwear during running

Ausgabe der Zeitschrift

13

Files

identification_of_subjectspec-20231201094604035.pdf

Datenquelle: OPENSCIENCE.UB

Identification of subject-specific responses to footwear during running

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