Currently, when designers build objects or tools with which humans interact, the digital prototypes they create cannot easily be tested against known human factors needs. By providing a digital human model to designers, the ergonomics of the digital prototypes can be tested and refined through simulation, thereby reducing the need to fabricate many iterations of physical prototypes late in the design process. Furthermore, if the digital human model is parametric and can represent a number of real human sizes, shapes, and motions, designs could be tested against entire target market segments.
Computers and 3D graphics applications are continuously increasing in power, memory, and rendering capabilities, making larger and more complex 3D scenes possible. Domains such as medical visualization, architecture and urban design, geospatial scanning, astrophysics, biochemistry, and abstract data analysis are beginning to consider massive datasets. Many of these datasets contain objects that exist at multiple scales, that is, the objects have meaningful observable properties at scales that are one or more orders of magnitude apart. This project investigates the properties and qualities of multiscale datasets in an effort to gain critical insights needed, in user experience and understanding, to make progress in increasingly complex contexts.
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