Publications
Detailed Information
Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions
Cited 1 time in
Web of Science
Cited 0 time in Scopus
- Authors
- Issue Date
- 2023
- Publisher
- Association for Computing Machinery, Inc
- Citation
- Proceedings - SIGGRAPH 2023 Conference Papers No.6
- 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.
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.