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

ABSTRACT: 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 models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data. Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain

SUBMITTER: Dindorf C 

PROVIDER: S-EPMC10899878 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature