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GaitDynamics: A Generative Foundation Model for Analyzing Human Walking and Running.

Tian Tan¹, Tom Van Wouwe², Keenon F Werling³

¹Department of Radiology, Stanford University.

Research Square

|April 1, 2025

Summary

This summary is machine-generated.

A new deep learning model, GaitDynamics, accurately predicts human gait dynamics from diverse data. This foundation model offers low-cost, rapid analysis for injury prevention, disease treatment, and performance coaching.

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Area of Science:

Biomechanics
Machine Learning
Human Motion Analysis

Background:

Human gait analysis is crucial for health and performance but is costly and limited by traditional lab experiments and simulations.
Existing deep learning models for gait analysis are often trained on limited datasets and predict only single outputs.

Purpose of the Study:

To develop GaitDynamics, a generative foundation model for human gait analysis trained on a large, diverse dataset.
To demonstrate the model's versatility in various gait-related prediction tasks.

Main Methods:

Trained a generative foundation model, GaitDynamics, on a large dataset encompassing diverse demographics and gait patterns.
Validated the model's performance on tasks including estimating ground reaction forces, predicting effects of gait modifications, and analyzing changes with running speed.

Main Results:

GaitDynamics accurately estimates ground reaction forces from kinematics, even with missing data and in unrepresented populations.
The model predicts the impact of gait modifications on knee loading and kinematic/force changes with running speed.
Predictions are accurate, rapid (seconds), and robust, utilizing flexible inputs.

Conclusions:

GaitDynamics offers a low-cost, versatile, and accurate solution for human gait analysis.
The model has significant potential for applications in injury prevention, disease management, and performance optimization.
All associated data, code, and trained models are publicly available to facilitate research and application.