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

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...

Повний опис

Бібліографічні деталі
Автори: Carlo Dindorf, Jonas Dully, Jürgen Konradi, Claudia Wolf, Stephan Becker, Steven Simon, Janine Huthwelker, Frederike Werthmann, Johanna Kniepert, Philipp Drees, Ulrich Betz, Michael Fröhlich
Формат: Стаття
Мова:English
Опубліковано: Frontiers Media S.A. 2024-02-01
Серія:Frontiers in Bioengineering and Biotechnology
Предмети:
machine learning
deep learning
spine
variational autoencoder
data augmentation
statistical parametric mapping
Онлайн доступ:https://www.frontiersin.org/articles/10.3389/fbioe.2024.1350135/full

Інтернет

https://www.frontiersin.org/articles/10.3389/fbioe.2024.1350135/full

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