QR-Code

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...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: 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
Sprache:Inglês
Veröffentlicht: Frontiers Media S.A. 2024-02-01
Schriftenreihe:Frontiers in Bioengineering and Biotechnology
Schlagworte:
machine learning
deep learning
spine
variational autoencoder
data augmentation
statistical parametric mapping
Online-Zugang:https://www.frontiersin.org/articles/10.3389/fbioe.2024.1350135/full
Tags: Tag hinzufügen
Keine Tags, Fügen Sie das erste Tag hinzu!

Ähnliche Einträge