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In order to improve the fit and comfort of helmets, we developed digital head models that represent the anthropometric and morphometric variability found in the U.S. Navy. We analyzed the size and shape variation using two related approaches. First, we used Procrustes superimposition, which minimizes the distances between all landmarks of all subjects. This allowed us to visualize the variation in landmark distribution of the face and to test for statistical differences. Second, we extracted curvatures along the surface of the head. This allowed us to characterize the variation in the shape of the head. To create a series of sized digital models, we used principal component analysis (PCA) to organize the variation in both the traditional measurements as well as the locations of the 3D landmarks. Using an adaptation of multivariate accommodation modeling we identified representative individuals who characterize 95% of the variation in size and shape.

The anthropometry of the truck driver population is largely unknown, the available databases are inappropriate, and anecdotal evidence suggests that the variation in population is significant. The combination of large anthropometric diversity with relative lack of data has made designing truck seats difficult in the past. To begin to remedy this situation, Sears Seating and Anthrotech combined resources to design a study to address this critical lack of anthropometric data on U.S. truck drivers in order to provide a scientific approach to new seat design. Data collected through the Anthropometric study is being used to develop a seat that will accommodate the majority of drivers with the adjustability to include the entire population.

Body size, or anthropometric, data can be applied to the design of truck cabs in a number of ways. The traditional approach involves specifying single dimensions with a specific percentile (e.g., 95th percentile Seated Eye Height). However, anthropometric dimensions vary independently, so specifying multiple dimensions can result in impossible design targets. We propose the use of multivariate accommodation models that have been successfully used in aircraft cockpit design. This approach allows the simultaneous inclusion of a larger number of dimensions while simplifying design by limiting the number of actual test cases. We use a sample analysis to show how this approach can be usefully applied to truck cab design.

Three-dimensional (3D) whole body scanners provide an opportunity for collecting large quantities of precise point data on human forms. However, product designers will likely always need some access to traditional point-to-point dimensional measurements. These measurements can also be obtained from the 3D whole body scanner if appropriate software is developed. This project describes a validation test for a collection of software tools designed to extract traditional dimensions from 3D whole body scans. To validate the software we scanned 123 male and female subjects on a Cyberware WB4 whole body scanner. Subjects were also measured for point-to-point dimensions using traditional instruments. We compared the mean absolute differences between traditional and software measurements to interobserver error data collected in a large traditional anthropometric survey (ANSUR), and to various criteria specific to garment applications.

This report describes the creation of anthropometric head forms. These head forms represent the population of U.S. children aged 2 to 18, and they can be used to develop bicycle helmets which fit the entire range of that population. A Cyberware digitizer was used to collect 3-D points on the heads of 1035 children. Six traditionally measured dimensions were also taken. The data were grouped into four size and shape categories. Statistical techniques were used to summarize the shapes of the heads in each of the categories. The resulting three-dimensional forms were physically rendered using an automated milling machine.