A quantitative classification of body shape by 3-D measurement data

本研究的目的是展示年轻女性的肩型，建立肩型分类方法，并利用三维测量数据对肩型进行分类，因为肩部支撑着服装，而该部位的合身程度影响着服装的舒适性。结果明确了摄像机的最佳水平和角度，证实了三维数据的精度是好的，但也指出了对人体摇摆的注意。接下来，从2004年11月到12月，63名女学生志愿者被三维数字化拍摄。对三维图像和由三维坐标值计算的19个项目进行分析，以检查平均肩部形状和个体差异。然后我们应用了主要的Comp…对11个项目进行了较为深入的分析，提取了表征肩部形态的因素，并尝试利用聚类分析得到的成分分数对肩部形态进行分类，主要结果如下：3-D图片肩部形态多样，肩、颈部位尺寸个体差异较大。肩部曲率向前与肩部倾斜度、颈底深度与宽度的相关性很低，因此它们是独立的。作为主成分分析的结果，我们提取了三个主成分：第一个成分表示肩的大小和肩部向前弯曲的程度，第二个成分表示肩倾斜度、颈基斜度和前胸的圆度，第三个分量表示前胸宽和颈基宽。然后利用成分得分的聚类分析，将他们的肩部形状分为4组。只有两名成员属于第四组，所以我们试图解释其余三组的情况。第一组为平均肩型，第二组为方肩、小颈基斜度、丰满胸围，第三组肩宽、肩深较小，背宽较窄。较少

This study aimed to show the young women's shoulder shape, to establish the method of shoulder shape type classification, and to classify the shoulder shape by 3-D measurement data, because the shoulder part supports clothing and the fitting to this part influences the comfortableness of clothing.First, in order to confirm the optimum photography conditions and the precision of a non-contact 3-D digitizer used for this study, three experiments were conducted. As the results, the optimum level and angle of camera were clarified, and it was confirmed that the precision of 3-D data was good, however, it was indicated that the cautions to the sway of human body are required. Next, sixty-three female student volunteers were photographed by 3-D digitizer from November to December in 2004. The three-dimensional pictures and the 19 items computed from the 3-D coordinate values were analyzed to examine the average shoulder shape and the individual differences. Then we applied the principal comp … More onent analysis to the 11 items to extract factors which express the shoulder shapes, and tried to classify the shoulder shapes, using the component scores by cluster analysis.The main results are as follow ; their shoulder shapes by 3-D pictures are various and the sizes on shoulder and neck parts have large individual differences. The correlations between shoulder curvature forward and shoulder slope and between neck base depth and width are very low, therefore, they are independent. As the results of principal component analysis, we extracted three principal components ; the first component which express the shoulder size and the degree of shoulder curvature forward, the second component which express the shoulder slope, the neck-base slope, and the roundness of front chest, and the third component which express the front chest width and the neck-base width. Then using cluster analysis of component score, we classified their shoulder shapes to 4 groups. Only two members belong to the fourth group, so we tried to interpreter about the rest three groups. The first group has the average shoulder type, the second group has the square shoulder, the small neck-base slope, and the full bust, and the third group has the small shoulder width and depth and the narrow back width relatively. Less