Sehee Min
Jehee Lee
ABSTRACT
Gait is a key barometer to analyze human body conditions. We propose a personalized gait analysis framework which can diagnose a possible muscularskeletal disorders with a single gait cycle. Our framework built over a gait manifold which reveals the principle kinematic characteristics in the temporal pose sequence. Body parameters such as muscle, skeleton, and joint limits for an arbitrary gait cycle can be approximated by measuring similarity in the small latent space. We present a physical gait simulator to enrich the gait space paired with the body conditions.
Supplemental Material
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- Seunghwan Lee, Moonseok Park, Kyoungmin Lee, and Jehee Lee. 2019. Scalable Muscle-Actuated Human Simulation and Control. ACM Trans. Graph. 38, 4, Article 73 (2019).Google Scholar
Digital Library
- L. McInnes, J. Healy, and J. Melville. 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. (2018). arxiv:1802.03426 [stat.ML]Google Scholar
- Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf. 2017. Wasserstein auto-encoders. arXiv preprint arXiv:1711.01558(2017).Google Scholar
- Jungdam Won and Jehee Lee. 2019. Learning Body Shape Variation in Physics-Based Characters. ACM Trans. Graph. 38, 6, Article 207 (2019).Google ScholarDigital Library
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