For the purpose of custom-made garment design, the 3D-body curved surface shapes of 1,348 females in an extensive age group were investigated using the angle values of three curvatures (Kc, kc, and Hc) by multivariate analysis. The nine 3D shape types (three types in the 30s and 40s age groups, two types in the 50s age group, and one type of the 60s age group) were categorized by using each sum angle value of the elliptical (+Kc), the hyperbolic (-Kc), the convex (+Hc), and the concave (-Hc) curved shapes in ten areas. The different features of the 3D shape types mainly displayed higher or lower convex elliptical and concave hyperbolic curved shapes in the neck, shoulder, chest, sides of trunk, and arms areas. Age also factored into the 3D shape types, and was particularly notable in the differences between the higher convex elliptical curved shape of the 50s and 60s age groups and the lower convex elliptical curved shape of the 30s and 40s age groups in the abdomen, buttocks, and legs areas. Several 3D-body shape types were extracted in the concrete body forms and angle curvature values and were provided as useful information for numerically developing visual designs in custom-made garments.

: We hoped to extract support information to enable the selection of the well-suited garments to fit threedimensional body (hereafter 3D-body) shape images of young women. The 3D-body simulations for evaluating the shape images were created by means of non-tactile 3D-body measurement. Two hundred words were selected to describe various body shape images. Six key words indicating full-length body images (As : A1. Underweight, A2. Feminine, A3. Ideal, A4. Standard, A5. Masculine, and A6. Overweight figures) and 19 key words for partial body image (Bs : B1. Leg thickness to B19. Body shape) were extracted for the classification. The 3D-body shape images of 82 young females were evaluated on a scale of 1 to 5 using these key words. Six principal components of 3D-body shape images (As and Bs) were extracted, and 7 3D-body shape image groups (5 full-length and 2 partial) were classified using those 5 principal component scores.

The 3D curved surface shapes of tight and flared skirts were predicted precisely by the angle curvatures (concentrated Gaussian curvature Kc, concentrated geodesic curvature kc, and concentrated mean curvature Hc), model sizes, skirt designs, and fabrics. All of the 72 skirts, encompassing 3 female body models (mean body sizes of Japanese women in their 20s, 40s, and 70s), 6 kinds of fabrics, tight skirts, and 3 kinds of flared skirts, were investigated with attention to the differences of the curved surface shapes in detail. It has been found that it is possible to predict the number of nodes on hemlines for these 72 skirts with the three totalized feature factors, |Σ±Kc|+|Σ±kc|, |Σ±Hc|, and weight (g/ cm 2) × hemline length (cm), underlying the physical properties of fabric and model sizes, based on slightly high or high correlation coefficients (r = 0.73 to r = 0.93). Therefore, the number of nodes on hemlines of these 72 skirts can be calculated by these three totalized feature factors using a multiple regression analyzing technique, (multiple regression coefficient R = 0.96), based on higher prediction accuracy.

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