Details
- Title: GaitDynamics: A Generative AI Model for Gait Analysis
- Speakers: Tian Tan, Ph.D., Wu Tsai Human Performance Alliance, Stanford University
- Time: September 23, 2026 at 9:00 AM Pacific Time
Abstract
The dynamics of human gait, including kinematics, joint moments, and external forces, offer key insights into human health and performance. Conventional gait analysis requires human experiments and physics-based simulations to quantify these dynamics. However, the high costs associated with human experiments and simulations has confined the scale of research studies. Data-driven models offer low-cost alternatives to performing experience by learning from large-scale datasets. Using the open-source AddBiomechanics dataset, Dr. Tan developed a generative model for efficient gait analysis across diverse downstream tasks. In the first part of the webinar, Dr. Tan will cover the two primary components of the study, including:
- Generative Architecture: GaitDynamics uses a diffusion-based architecture that iteratively generates outputs with flexible combinations of inputs.
- Downstream Applications: The model’s versatility across tasks with diverse inputs and outputs, such as using kinematics to estimate ground reaction forces and using synthetic trunk sway angles to predict their influence on knee adduction moments.
In the second part of the webinar, Dr. Tan will lead a hands-on tutorial using Hugging Face demos of the GaitDynamics model. He will demonstrate how to investigate gait-parameter changes across different running cadences and how to predict ground reaction forces from flexible combinations of input kinematics.
Tan, T., Van Wouwe, T., Werling, K. F., Liu, C. K., Delp, S. L., Hicks, J. L., & Chaudhari, A. S. (2026). GaitDynamics: A generative foundation model for analyzing human walking and running. Nature Biomedical Engineering, 1-13.
Our Speaker
Tian Tan, PhD
Researcher Engineer
Tian Tan is a Research Engineer at Stanford University. As a member of the Wu Tsai Human Performance Alliance and Neuromuscular Biomechanics Lab, he seeks to advance biomechanics and movement health through data science. Prior to joining Stanford, he obtained his PhD degree in Mechanical Engineering from Shanghai Jiao Tong University.
