摘要：This paper focused on the different characteristics of the shoulder cross-section curves closely related to the shape to subdivide the shoulder shapes. In this paper, 213 young college male students aged 18-26 were selected to measure the shoulder data with three-dimensional body scanner. With the help of imageware12.0 and matlabr2012b software, the cross-section curves which could be used to classify the shoulder shapes were extracted, and the method of subdividing the shoulder shapes with the curvature radius of the characteristic points of the cross-section curve and the ratio of sagittal to frontal diameter was established. K-means clustering method was used through dynamic clustering, the optimal classification number of shoulder shapes was determined to be 4 categories by variance analysis, and the shape differences of each shoulder shape were quantified; by comparing the curve shape of shoulder section, the curve change characteristics of 4 categories of shoulder section were further qualitatively described.
其他摘要：This paper focused on the different characteristics of the shoulder cross-section curves closely related to the shape to subdivide the shoulder shapes. In this paper, 213 young college male students aged 18-26 were selected to measure the shoulder data with three-dimensional body scanner. With the help of imageware12.0 and matlabr2012b software, the cross-section curves which could be used to classify the shoulder shapes were extracted, and the method of subdividing the shoulder shapes with the curvature radius of the characteristic points of the cross-section curve and the ratio of sagittal to frontal diameter was established. K-means clustering method was used through dynamic clustering, the optimal classification number of shoulder shapes was determined to be 4 categories by variance analysis, and the shape differences of each shoulder shape were quantified; by comparing the curve shape of shoulder section, the curve change characteristics of 4 categories of shoulder section were further qualitatively described.
