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 health and performance. Conventional gait analysis requires laboratory-based experiments and physics-based simulations to quantify gait dynamics and analyze how dynamics change with treatment, training, injury, and disease. However, the high costs associated with experiments and simulations has confined the use of gait dynamics to small-scale research studies. While deep learning models offer low-cost prediction, and can be highly expressive in fitting large-scale data, existing models have primarily been trained on small datasets with homogenous demographics and focused on predicting a single output. To overcome these limitations, we developed GaitDynamics, a generative foundation model

SUBMITTER: Tan T 

PROVIDER: S-EPMC11957236 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature