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

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Park, Jungnam; Park, Moon Seok; Lee, Jehee; Won, Jungdam

Issue Date
Association for Computing Machinery, Inc
Proceedings - SIGGRAPH 2023 Conference Papers No.6
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.
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  • College of Medicine
  • Department of Medicine
Research Area Cerebral palsy, Medical image, Motion analysis, Pediatric orthopedic surgery, Statistics in orthopedic research


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