GaitDynamics: a generative foundation model for analyzing human walking and running.

ABSTRACT: Understanding the dynamics of human gait, including both motions and forces, is vital to promote human mobility. While deep learning models may have advantages over costly laboratory-based experiments and physics-based simulations, existing models have been trained on small datasets with homogeneous demographics and focus on predicting a single output. We developed GaitDynamics, a generative foundation model trained on a large dataset of diverse gait patterns, which allows for flexible inputs, outputs and clinical applications. We illustrate the use of GaitDynamics for: (1) estimating ground reaction forces from kinematics with high accuracy even with missing kinematic data, (2) predicting the effects of gait modifications on knee loading without resource-intensive experiments and (3) pred

SUBMITTER: Tan T 

PROVIDER: S-EPMC12928157 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature