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基于压力袜测量点数据的青年女性腿型分类

Classification of Young Women’s Leg Shape Based on Compressive Stockings Nominal Measurement Point Data
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摘要 针对压力袜使用过程中经常出现压力袜和人腿不适配问题,采用马丁测量法,对324名18~44岁青年女性的7个压力袜医疗标准中规定的人体腿部公称测量点的围度和高度数据进行采集,获取14项腿部变量数据。使用因子分析法对影响青年女性腿型的主成分因子进行提取,共提取出围度因子、腿部高度因子、踝部高度因子3个因子;并将这3个因子得分作为聚类变量进行K-means聚类,以轮廓系数作为评估聚类效果好坏的指标,最终将青年女性腿型分成5种:高中等型、偏矮瘦型、偏高瘦型、矮胖型、中等腿长偏胖型,各腿型分别占样本量的22.22%、19.14%、19.14%、16.05%、23.45%。该研究中以压力袜公称测量点数据为基础的腿型分类结果,为制定压力袜号型提供了参考和理论基础。 In view of the problem of compressive stockings and human leg mismatch,which often occur in the use of pressure stockings,the Martin measurement method was adopted to collect the circumference and height data of 7 nominal measurement points of 324 young women aged 18~44 years old in their legs,and 14 leg variables data were obtained.The principal component factors affecting the leg shape of young women were extracted by factor analysis,and three factors including circumference factor,leg height factor and ankle height factor were extracted.The scores of these three factors were used as clustering variables for K-means clustering,the silhouette coefficient was used as an index to evaluate the clustering results,and finally the leg shape of young women were divided into five types:long leg length and medium circumference type,slightly short and lean type,slightly high and lean type,short and fat type,medium leg length and slightly fat type,and each leg type accounted for 22.22%,19.14%,19.14%,16.05%,23.45%of the sample size,respectively.The leg-type classification results based on the nominal measurement point data of the compressive stockings provide a reference and theoretical basis for defining the compressive stockings size.
作者 王嫣然 孙玉钗 Wang Yanran;Sun Yuchai(College of Textile and Clothing Engineering,Soochow University,Suzhou,Jiangsu 215006,China)
出处 《针织工业》 北大核心 2024年第7期67-70,共4页 Knitting Industries
基金 江苏省先进纺织工程技术中心资助项目(XJFZ/2016/1) 苏州市科技局重点产业技术创新资助项目(SGC201722)。
关键词 腿型分类 压力袜 人体测量 青年女性 K-MEANS聚类 Leg Shape Classification Compressive Stockings Anthropometry Young Women K-means Clustering
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