ERGOBOSS : Ergonomic Optimization of Body-Supporting Surfaces
Humans routinely sit or lean against supporting surfaces and it is important to shape these surfaces to be comfortable and ergonomic. We give a method to design the geometric shape of rigid supporting surfaces to maximize the ergonomics of physically based contact between the surface and a deformabl...
Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - PP(2021) vom: 14. Sept. |
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1. Verfasser: | |
Weitere Verfasser: | , , , |
Format: | Online-Aufsatz |
Sprache: | English |
Veröffentlicht: |
2021
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Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
Schlagworte: | Journal Article |
Zusammenfassung: | Humans routinely sit or lean against supporting surfaces and it is important to shape these surfaces to be comfortable and ergonomic. We give a method to design the geometric shape of rigid supporting surfaces to maximize the ergonomics of physically based contact between the surface and a deformable human. We model the soft deformable human using a layer of FEM deformable tissue surrounding a rigid core, with measured realistic elastic material properties, and large-deformation nonlinear analysis. We define a novel cost function to measure the ergonomics of contact between the human and the supporting surface. We give a stable and computationally efficient contact model that is differentiable with respect to the supporting surface shape. This makes it possible to optimize our ergonomic cost function using gradient-based optimizers. Our optimizer produces supporting surfaces superior to prior work on ergonomic shape design. Our examples include furniture, apparel and tools. We also validate our results by scanning a real human subject's foot and optimizing a shoe sole shape to maximize foot contact ergonomics. We 3D-print the optimized shoe sole, measure contact pressure using pressure sensors, and demonstrate that the real unoptimized and optimized pressure distributions qualitatively match those predicted by our simulation |
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Beschreibung: | Date Revised 20.02.2024 published: Print-Electronic Citation Status Publisher |
ISSN: | 1941-0506 |
DOI: | 10.1109/TVCG.2021.3112127 |