A major problem in designing highly specialized equipment such as oxygen masks, respirators, etc, is that the effectiveness of the equipment depends on its appropriateness for the size and shape of the body part for which it is designed. However, in general, among the individuals who are likely to be using this equipment, there is considerable heterogeneity in size and shape of the body parts. One solution is to use the available data to form homogeneous clusters of the population and then make separate designs for each cluster, commonly referred to as sizing. Current sizing practices are hindered by a problem termed 'observer-inherence'; in other words, the positioning and orientation of the reference axis system can affect the size and shape .groupings more than size and shape themselves do. The impact of observer-inherence ~s felt most on systems that require the most stringent fit, such as helmets with optical systems. For such systems, traditional measures and analysis practices can be almost useless.
In this paper, an analysis technique is introduced which should be observerinvariant in the three-dimensional case. The method is illustrated using points selected along a horizontal cross-section. The points are first transformed into values called curvatures which are subsequently transformed into a series of Fourier coefficients. These are then used for arriving at shape clusters or groupings. The shape differences (and similarities) within and between clusters are examined graphically and discussed. The technique developed here can be extended to form clusters using the curvatures of a surface instead of that of a cross-section (ie, can be extended to the three-dimensional case) and methods for doing so are discussed.