Subject-specific responses to footwear during running

NewsBot · Nov 23, 2022

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Identification of subject-specific responses to footwear during running
Fabian Horst et al
Preprint

Background

Placing a stronger focus on subject-specific responses to footwear may lead to a
better functional understanding of footwear effects on running and its influence on comfort
perception, performance, and pathogenesis of injuries. Here, we investigate subject-specific
responses to different footwear conditions within ground reaction force (GRF) data during
running using a machine learning-based approach. We conduct 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.

Methods

Thirty male subjects ran ten times at their preferred (self-selected) speed on a level and
approximately 15 m long runway in five footwear conditions (barefoot, subject’s own running
shoe, and three standardised running shoes). We recorded three-dimensional GRFs for one
right-foot stance phase per running trial and classified the vectorised GRFs using support vector
machines.

Results

The highest prediction accuracy was found for the subject x footwear classification
(hypothesis I). The median prediction accuracy was 95.7 %. This is approximately 137 times higher
than the zero-rule baseline (ZRB) of 0.7 %. Across footwear conditions, subjects could be
discriminated with a median prediction accuracy of 89.7 % (approximately 27 times higher than
the ZRB of 3.3 %). Across subjects, footwear conditions could be discriminated with a median
prediction accuracy of 76.3 % (approximately 4 times higher than the ZRB of 20.0 %).

Conclusion

Our results suggest that, during running, responses to footwear are unique to each
individual subject and footwear design. As a result, considering subject-specific responses
contribute to a more differentiated functional understanding of footwear effects. Incorporating
holistic biomechanical data is auspicious for the (subject-specific) evaluation of the footwear
effects, as unique interactions between subjects and footwear manifest in versatile ways.
Machine learning methods have demonstrated their great potential to fathom subject-specific
responses when evaluating and recommending footwear.
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NewsBot · Nov 23, 2022

Related Thread:
Subject Specific Optimisation of the Stiffness of Footwear Material

NewsBot · Aug 4, 2023

Identification of subject-specific responses to footwear during running
Fabian Horst, Fabian Hoitz, Djordje Slijepcevic, Nicolas Schons, Hendrik Beckmann, Benno M. Nigg & Wolfgang I. Schöllhorn
Scientific Reports volume 13, Article number: 11284 (2023)

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.
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