Overview / Introduction
Zone Differentiation (ZD or Zone Diff) is a term the Virtual Soldier Research group uses to indicate an analysis of the space surrounding the body. Unlike a reach envelope - which can only determine if a point in space is reachable – Zone Diff provides ergonomic and human factors issues related to the postures required to touch all the points in a discretized space surrounding an avatar.
Methods / Current Research
Technically, Zone Diff analysis results in a 3-dimensional dataset similar to MRI, CT, and PET data. Obtaining the Zone Diff data is straightforward and begins with determining the maximum distance that can be reached by a given avatar with a given anthropometry. This maximum reach distance is then used as the radius of a sphere midway between the hips. The dimensions of the Zone Diff space to be analyzed are defined by a cube whose faces are all tangent to that sphere.
Figure 1 shows a conceptual cubic volume defined by a maximum reach distance, centered between the avatar's hips.
The Zone Diff space is discretized at a user-defined level allowing a compromise between resolution and time to complete the analysis. This divides the space up into sub-volume elements and the centroid of each sub-volume element is used as a target for a predicted posture.
Figure 2 Shows a cubic volume subdivided into evenly spaced sub-volume elements
Posture prediction is performed on each target and the results are stored in a volumetric file format called a Direct Draw Surface (DDS) file, a 32-bit IEEE floating point 3D volume format, and referred to as a Zone Differentiation Volume or ZDV.
Like MRI, CT, and PET data, Zone Diff data have at least 2 properties; location and value. While the values associated with each location within the discretized space typically represent a specific human performance measure for each predicted posture, the values could be set to any number of metrics associated with the resulting postures. The values at each location within the evaluated space could represent something as basic as the angle of a specified joint but those values could also represent something more complex. For example, the Zone Diff values could represent the maximum amount of torque that can be generated by a specified joint for each predicted posture. In fact, the Zone Diff values could even be set to the time of day that each point was analyzed which – while a questionable use of the tool – demonstrates the flexibility of the system.
Leveraging techniques developed in computer graphics and volumetric image processing allow ZDV’s to be used for a variety of human factors analysis and design purposes.
Results
To aid in the analysis of an existing design, capabilities have been developed that allow the ZDV to be used as a real-time, volumetric texture map allowing any geometry within the Zone Diff space to take on the values within the ZDV. The ZD volume can be pseudo-colored using a predefined color scale where – for example - cool colors like greens and blues could represent regions within the ZDV that require relatively less effort or less discomfort to reach than areas represented by hot colors like yellows and reds. This capability provides a visual correlation between the human performance measures in the ZDV and the distance the various components of the existing design are from a given human model.
Figure 3 shows geometry for an overhead console pseudo-colored by the human performance measures in the ZD volume.
All of this can be performed in real-time meaning that the human model and/or the design geometry can be interactively translated and rotated while receiving real-time feedback for optimum position and orientation.
Of primary importance in the use of ZDV’ is an interactive transformation function that allows control over contrast, pseudo-color, and transparency of user-specified ranges of values within the ZDV. Use of the interactive transformation function tool allow design geometry to be exported in CAD compatible formats such that the vertices of Zone Diff-analyzed designs are colored by the human performance values calculated for the ZDV. This capability – in turn - allows the Zone Diff-analyzed geometry to be brought back into the original design environment in a way that retains the visual cues required for further design modification.
Leveraging ZDV’s as an aid in new product design includes using the interactive transformation function described above to make specified ranges of ZD volume data invisible, opaque, or some variable amount of translucence in-between. This allows the volume to reflect, for example, only the most desirable range of human performance measures.
Figure 4 left shows the entire ZD volume computed for discomfort for a given human model. Figure 4 right shows only a user-defined range of values that represent the least discomfort from the same ZD volume.
Use of marching cubes-like algorithms provides a means of creating a 3D mesh representation of the user-definable portions of any given ZDV. Once meshed, Boolean operations can be performed on multiple ZD volumes from selected anthropomorphic boundary cases to obtain the most desirable range of whatever human performance measures are sought across the targeted population. Finally, any of these volumes can be exported in a variety of CAD-compatible formats, complete with references to the midpoint between the avatar’s hips so that the geometry can be used as an aid in the development of a completely new design.
Figure 5 shows a Zone Diff Volume that has been exported as geometry compatible with CAD environments.
Contact Info
https://www.ccad.uiowa.edu/vsr/contact