Synthetic Data for Foot Strike Angle Estimation

Christoph Schranz, Stefan Kranzinger, Stephanie Moore

2024

Abstract

A runner’s foot strike angle (FSA) can be relied on to assess performance, comfort, and injury risk. However, the collection of FSA datasets is time-consuming and costly, which may result in small datasets in practice. Therefore, the creation of synthetic FSA datasets is of great interest to researchers to improve the performance of machine learning models while maintaining the same effort in data collection. We evaluate data augmentation (jittering, pattern mixing, SMOTE) and synthetic data generation (Generative Adversarial Networks, Variational Autoencoders) methods with four subsequent machine learning models to estimate the FSA on a dataset involving 30 runners across a range of FSAs. The results show promising results for the SVM and MLP, as well as for the jittering and pattern mixing augmentation methods. Our findings underscore the potential of data augmentation to improve FSA estimation accuracy.

Paper Citation

in Harvard Style

Schranz C., Kranzinger S. and Moore S. (2024). Synthetic Data for Foot Strike Angle Estimation. In Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS; ISBN 978-989-758-719-1, SciTePress, pages 113-118. DOI: 10.5220/0012890100003828

in Bibtex Style

@conference{icsports24,
author={Christoph Schranz and Stefan Kranzinger and Stephanie Moore},
title={Synthetic Data for Foot Strike Angle Estimation},
booktitle={Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS},
year={2024},
pages={113-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012890100003828},
isbn={978-989-758-719-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support - Volume 1: icSPORTS
TI - Synthetic Data for Foot Strike Angle Estimation
SN - 978-989-758-719-1
AU - Schranz C.
AU - Kranzinger S.
AU - Moore S.
PY - 2024
SP - 113
EP - 118
DO - 10.5220/0012890100003828
PB - SciTePress