Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML model...

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Autors principals:	Carlo Dindorf, Jonas Dully, Jürgen Konradi, Claudia Wolf, Stephan Becker, Steven Simon, Janine Huthwelker, Frederike Werthmann, Johanna Kniepert, Philipp Drees, Ulrich Betz, Michael Fröhlich
Format:	Artigo
Idioma:	Inglês
Publicat:	Frontiers Media S.A. 2024-02-01
Col·lecció:	Frontiers in Bioengineering and Biotechnology
Matèries:	machine learning deep learning spine variational autoencoder data augmentation statistical parametric mapping
Accés en línia:	https://www.frontiersin.org/articles/10.3389/fbioe.2024.1350135/full
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