By Tian Tan, Tom Van Wouwe, Keenon F. Werling, C. Karen Liu, Scott L. Delp, Jennifer L. Hicks, and Akshay S. Chaudhari
GaitDynamics is a generative foundation model for general-purpose gait dynamics prediction. We illustrate in three diverse tasks with different inputs, outputs, and clinical impacts: i) estimating external forces from kinematics, ii) predicting the influence of gait modifications on knee loading without human experiments, and iii) predicting comprehensive kinematics and kinetic changes that occur with increasing running speeds.
Our code is developed under the following environment. Versions different from ours may still work.
Python 3.9.16; Pytorch 1.13.1; Cuda 11.6; Cudnn 8.3.2; numpy 1.23.5;
GaitDynamics has a diffusion model and a force refinement model. Downstream task 1 uses both models, while downstream task 2 and 3 use only the diffusion model.
A Google Colab notebook is provided for the downstream tasks 1 – force estimation using flexible combinations of kinematic inputs. Upload an OpenSim model file (.osim) and kinematic data files (.mot) following the instructions in the notebook. Example files can be found in the example_usage folder.
This repository includes the code and models for a preprint and an abstract.