The technique as described here shows how relations between one-dimension body measurements can be determined and used in the design of three-dimensional products by using the software tool Elipse.
As was said before: the average person does not exist; therefore the use of one-dimensional data to determine product dimensions can easily lead to unwanted situations. The techniques of two-dimensional anthropometry allow you to get insight in the correlations between two different body dimensions. By that it can show the consequences for related body dimensions. However, this can only be done in a 2D window, to get insight in a 3D problem it is therefore needed to switch regularly between these windows
Data to use for this technique can be obtained from the same measurements as for 1D anthropometry. However, to determine correlations between different dimensions individual measurements of participants are needed.
- Possibility to obtain more useful information from a product design perspective with one-dimensional data
- Gives insight in correlation
- Gives insight in consequences for related body dimensions
- Limited to two dimensions at a time
- Requires original datasets with individual measurements
How to use two-dimensional data
Ellipse is a tool to support the interpretation of two-dimensional data. The program allows you to display two-dimensional sample data as points in a scatter plot and find out what percentage of the sample data are contained in rectangles, called Blocks. Ellipse simultaneously shows the percentage of probability of the bivariate normal distribution contained in the Block. Ellipse also shows an ellipse containing a certain probability percentage of the bivariate normal distribution.
- Op maat gemaakt Menselijke maten voor het gebruiken en beoordelen van gebruiks goederen by J.F.M. Molenbroek. TUD, 1994
Currently the TU Delft made two full datasets available that can be used for two-dimensional techniques. These will be available soon.
Available studies gives more information on the background of these datasets