English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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

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