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Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jungnam | - |
dc.contributor.author | Park, Moon Seok | - |
dc.contributor.author | Lee, Jehee | - |
dc.contributor.author | Won, Jungdam | - |
dc.date.accessioned | 2023-10-30T01:45:54Z | - |
dc.date.available | 2023-10-30T01:45:54Z | - |
dc.date.created | 2023-09-07 | - |
dc.date.created | 2023-09-07 | - |
dc.date.created | 2023-09-07 | - |
dc.date.created | 2023-09-07 | - |
dc.date.created | 2023-09-07 | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Proceedings - SIGGRAPH 2023 Conference Papers No.6 | - |
dc.identifier.uri | https://hdl.handle.net/10371/195861 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Bidirectional GaitNet: A Bidirectional Prediction Model of Human Gait and Anatomical Conditions | - |
dc.type | Article | - |
dc.identifier.doi | 10.1145/3588432.3591492 | - |
dc.citation.journaltitle | Proceedings - SIGGRAPH 2023 Conference Papers | - |
dc.identifier.wosid | 001117690500006 | - |
dc.identifier.scopusid | 2-s2.0-85168010864 | - |
dc.citation.number | 6 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Park, Moon Seok | - |
dc.contributor.affiliatedAuthor | Lee, Jehee | - |
dc.contributor.affiliatedAuthor | Won, Jungdam | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordAuthor | Clinical Gait Analysis | - |
dc.subject.keywordAuthor | GaitNet | - |
dc.subject.keywordAuthor | Musculoskeletal Simulation | - |
dc.subject.keywordAuthor | Predictive Gait Simulation | - |
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