Preprint / Version 1

Identification of subject-specific responses to footwear during running


  • Fabian Horst Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Germany
  • Fabian Hoitz Biomedical Engineering, Schulich School of Engineering, University of Calgary, Alberta, Canada
  • Nicolas Schons Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Germany
  • Hendrik Beckmann Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Germany
  • Benno M. Nigg Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
  • Wolfgang I. Schöllhorn Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Germany



Support Vector Machines, Layer-wise Relevance Propagation, Ground Reaction Forces, Explainable Artificial Intelligence, XAI, Machine Learning



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.


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


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 %).


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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