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This review highlights the importance of data augmentation in addressing limited data availability and improving model generalization in biomechanics. Tailoring augmentation to data characteristics can enhance the performance and relevance of predictive models. However, understanding how different augmentation techniques impact data quality and downstream performance remains essential for developing better methods.
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Halmich et al. (2025) studied this question.
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