Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions

Abstract

We present a novel generative model, called Bidirectional GaitNet, that learns the relationship between human anatomy and its gait. The simulation model of human anatomy is a comprehensive, full-body, simulation-ready, musculoskeletal model with 304 Hill-type musculotendon units. The Bidirectional GaitNet consists of forward and backward models. The forward model predicts a gait pattern of a person with specific physical conditions, while the backward model estimates the physical conditions of a person when his/her gait pattern is provided. Our simulation-based approach first learns the forward model by distilling the simulation data generated by a state-of-the-art predictive gait simulator and then constructs a Variational Autoencoder (VAE) with the learned forward model as its decoder. Once it is learned its encoder serves as the backward model. We demonstrate our model on a variety of healthy/impaired gaits and validate it in comparison with physical examination data of real patients.

References in corpus (9)

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
• AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
• Learning Symmetric and Low-energy Locomotion
• QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars
• Neural probabilistic motor primitives for humanoid control
• Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation
• CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion
• Functionality-Driven Musculature Retargeting