Many 3D digital models are designed and created as digital prototypes of vehicles, devices, or spaces intended for human use. However, to digitally validate or test these designs, an advanced digital human model is needed. This project explores the possibilities of extending the reach of digital prototyping beyond design into testing via an advanced digital human biomechanical model.
Human beings are experts at recognizing the identity of their fellow humans due to their ability to discriminate based on our varying shapes, colors, and sizes. Although most of the variation we use to recognize each other is found in external features of the body, such as height, weight, eye color, hair length, and skin tone, a large portion of human variation is contained in the skeleton. These variations originate from the person's age, geographic origin, sex, or simply the idiosyncrasies of an individual's genetics and environment.
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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