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...

Descrizione completa

Dettagli Bibliografici

Autori principali:	Carlo Dindorf, Jonas Dully, Jürgen Konradi, Claudia Wolf, Stephan Becker, Steven Simon, Janine Huthwelker, Frederike Werthmann, Johanna Kniepert, Philipp Drees, Ulrich Betz, Michael Fröhlich
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Frontiers Media S.A. 2024-02-01
Serie:	Frontiers in Bioengineering and Biotechnology
Soggetti:	machine learning deep learning spine variational autoencoder data augmentation statistical parametric mapping
Accesso online:	https://www.frontiersin.org/articles/10.3389/fbioe.2024.1350135/full
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!