Authors:
Christoph Schranz
1
;
Stefan Kranzinger
1
and
Stephanie Moore
2
Affiliations:
1
Human Motion Analytics, Salzburg Research Forschungsgesellschaft mbH, 5020 Salzburg, Austria
;
2
Department of Sport and Exercise Science, University of Salzburg, 5400 Hallein/Rif, Austria
Keyword(s):
Data Augmentation, Human Running, GAN, Autoencoder, Foot Strike Angle.
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.