Synthetic Data for Foot Strike Angle Estimation.
Schranz, Christoph; Kranzinger, Stefan and Moore, Stephanie (2024): Synthetic Data for Foot Strike Angle Estimation. In: Proceedings of the 12tInternational Conference on Sport Sciences Research and Technology Support. ISBN 978-989-758-719-1, pages 113-118.
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.
