Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions
This work addresses gait analysis for medical or rehabilitation applications, but it is incremental as it builds on existing simulation and VAE methods.
The paper tackles the problem of predicting human gait from anatomy and vice versa by introducing Bidirectional GaitNet, a generative model that uses a musculoskeletal simulation with 304 units, achieving validation against real patient data.
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.