纺织学报 ›› 2023, Vol. 44 ›› Issue (07): 184-191.doi: 10.13475/j.fzxb.20220504501

• 服装工程 • 上一篇    下一篇

中国东部地区中老年女性体型特征与分类

刘咏梅1,2, 刘思忆1, 于晓坤1,2, 薛惠心1, 张向辉1,2()   

  1. 1.东华大学 服装与艺术设计学院, 上海 200051
    2.现代服装设计与技术教育部重点实验室, 上海 200051
  • 收稿日期:2022-05-16 修回日期:2023-02-28 出版日期:2023-07-15 发布日期:2023-08-10
  • 通讯作者: 张向辉(1978—),女,副教授,博士。主要研究方向为服装舒适性与服装结构设计。E-mail:zhangxianghui@dhu.edu.cn。
  • 作者简介:刘咏梅(1969—),女,副教授,博士。主要研究方向为服装先进制造与人体科学。

Body shape characteristics and classification of middle-aged and elderly women in eastern China

LIU Yongmei1,2, LIU Siyi1, YU Xiaokun1,2, XUE Huixin1, ZHANG Xianghui1,2()   

  1. 1. College of Fashion and Design, Donghua University, Shanghai 200051, China
    2. Key Laboratory of Clothing Design and Technology, Ministry of Education, Donghua University, Shanghai 200051, China
  • Received:2022-05-16 Revised:2023-02-28 Published:2023-07-15 Online:2023-08-10

RichHTML

17

PDF (PC)

112

摘要：

当今中国人口老龄化程度日益严峻,中老年人群规模逐渐扩大,与之相关的服装市场也进一步扩大,中老年人群对服装的舒适性和美观性提出了更高的要求。中老年女性相对于青年女性体型,在胸部、腰部、腹部和背部形态等方面存在显著差异,现行国家标准号型规格不能满足中老年人群要求。为此,选取207名中国东部地区50~65岁的中老年女性作为研究样本,采用马丁测量法采集了20项人体尺寸数据,参照国家标准并运用数理统计法得出中老年女性标准体,通过因子分析和相关性分析得到影响中老年女性体型的特征变量,最后通过快速聚类分析法对其体型进行分类。研究结果表明:国家标准号型对中老年女性体型的覆盖率较低;围度因子是影响样本体型差异的主要因素,前后腰节差是影响人体躯干形态的特征变量,身腰比、胸腰差、臀腰差是影响人体高瘦、丰满指数的特征变量;以前后腰节差为基准进行聚类,将中老年女性人体体型分为挺胸体、正常体、微驼背体和驼背体;以腰围、身腰比、胸腰差、臀腰差为基准进行聚类,将中老年女性人体体型分为小X型、H型、小A型和A型,得出各体型聚类指标的取值范围。

关键词: 服装设计, 中老年女性, 标准体, 体型特征, 体型分类, 聚类分析

Abstract:

Objective Nowadays, China's aging population is becoming increasingly severe, the size of middle-aged and elderly people is expanding, and so is the related clothing market. Middle-aged and elderly people put forward higher requirements for the comfort and aesthetetics of clothing, and the human body shape is closely related to the structure and shape design of clothing. Among them, the differences between middle-aged and elderly women and young women are significant in the shapes of chest, waist, abdomen and back. This research is proposed to study the body shape characteristics and classification of middle-aged and elderly women.

Method In order to further explore the body shape characteristics and distribution of middle-aged and elderly women, 207 middle-aged and elderly women aged 50-65 from eastern China were selected as research subjects, and 20 pieces of body size data were collected according Martin's measurement method. According to the national women's size standard, the collected data of body shape of middle-aged and elderly women were classified based on the difference of chest and waist and compared with the data of body shape proportion of the national standard to obtain the differences between the standard body shape of middle-aged and elderly women and the standard body shape of adult women. Further factoral analysis and correlation analysis were carried out on the collected data to acquire characteristic variables affecting the body shape of middle-aged and elderly women. Finally, based on the influence factor with the highest correlation, the body shape of middle-aged and elderly women was classified through rapid cluster analysis, and the value range of characteristic variables was calculated.

Results Compared with the national standard GB/T 1335.2—2008《Standard Sizing Systems for Garments-Women》, the proportion of Y and A types decreases while the proportion of B and C types increases, among which the proportion of B type is the highest (Tab. 2 and Tab. 3). The results of factor analysis showed that circumference factor was the main factor affecting the body shape difference of middle-aged and elderly women, the front and back waist section difference was the characteristic variable affecting the body shape of the torso, the waist ratio, the chest-waist difference, the hip-waist difference was the characteristic variable affecting the body height, slimness and fullness index (Tab. 4 and Tab. 5). Combined with factor analysis and extraction results of human characteristic variables, clustering was finally carried out from two perspectives, i.e. the difference between front and back waist section difference was taken as a clustering index, and waist, waist ratio, chest-waist difference and hip-waist difference were taken as clustering indexes. The results of K-means fast clustering showed that the human body shape was divided into humpback body, slight humpback body, normal body and chest pull-out body by using the difference of front and back waist as the benchmark. Based on waist circumference, waist ratio, chest-waist difference and hip-waist difference, human body shape was classified into X type, H type, small A type and A type. Finally, the value range of clustering indicators of each body type was obtained (Tab. 11 and Tab. 12).

Conclusion The body shape of middle-aged and elderly women shows an obvious trend of obesity, and the coverage rate for their body shape in the national standard is low, which calls for targeted clothing size standards to be established. Circumference factor is the main factor affecting the body size difference of middle-aged and old women. Through cluster analysis, the body shape of middle-aged and elderly women are divided into four categories from the aspects of trunk shape, and body height, thinness and fullness, and their body shape are subdivided more accurately, providing reference for the establishment of clothing type of middle-aged and elderly women. For future research, it is suggested to increase the sample size of the research subjects to better represent body shape of middle-aged and elderly women, so as to improve the accuracy of body type classification.

Key words: fashion design, middle-aged and elderly women, standard body, body shape characteristics, body shape classification, cluster analysis

中图分类号: 

  • TS941.2

表1

人体测量数据"

类别 年龄/岁 体重/
kg		 身高/
cm		 颈椎
点高/cm		 腰高/
cm		 肩宽/
cm		 前胸
宽/cm		 后背
宽/cm		 乳间
距/cm		 腹臀
厚/cm
最小值 50 36.7 144.7 122.3 85.5 35.7 30.0 28.0 11.4 19.8
最大值 65 78.7 170.5 145.0 104.1 47.0 42.0 42.0 21.5 31.8
统计均值 58.6 58.4 157.5 133.6 94.5 41.4 35.8 35.5 16.2 26.4
标准误差均值 0.3 0.6 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.2
标准差 3.6 8.3 4.9 4.3 3.6 2.3 2.4 2.7 2.0 2.5
方差 13.1 68.0 24.0 18.3 12.7 5.3 5.9 7.3 3.9 6.1
偏度 -0.37 0.17 -0.04 -0.16 -0.13 0.23 0.14 0.11 0.1 -0.03
峰度 -0.48 -0.19 -0.03 0.01 -0.16 -0.12 -0.37 -0.34 -0.27 -0.33
变异系数/% 6.14 14.21 3.11 3.22 3.81 5.56 6.70 7.61 12.35 9.47
类别 前腰
节长/cm		 颈侧点
到乳点/
cm		 后腰
节长/
cm		 背长/
cm		 颈根围/
cm		 胸围/
cm		 胸下围/
cm		 腰围/
cm		 臀围/
cm		 臂根
围/cm
最小值 37.7 22.9 37.6 34.0 35.8 72.0 65.5 63.6 80.3 32.5
最大值 51.0 32.0 51.2 45.4 46.3 109.0 100.0 96.5 104.3 47.8
统计均值 44.9 27.2 44.2 39.2 40.9 90.2 80.9 78.3 91.6 39.7
标准误差均值 0.2 0.1 0.2 0.2 0.1 0.5 0.5 0.5 0.3 0.2
标准差 2.6 1.8 2.6 2.3 2.0 6.9 6.7 6.8 4.9 2.9
方差 6.8 3.1 6.6 5.2 4.0 47.7 44.9 46.7 23.9 8.3
偏度 0.01 0.25 0.11 0.21 0.2 0.38 0.47 0.21 0.3 0.23
峰度 -0.31 0 -0.36 -0.12 -0.23 -0.26 -0.32 -0.33 -0.18 -0.28
变异系数/% 5.79 6.62 5.88 5.87 4.89 7.65 8.28 8.68 5.35 7.30

表2

中老年女性与国家标准体型分布"

体型
代号		 国家标准体型分布
比例/%		 中老年女性体型
分布比例/%
Y 14.8 2.9
A 44.1 26.1
B 33.7 47.8
C 6.5 21.7
总计 99.1 98.5

表3

国家标准体与中老年女性标准体型对比"

体型 群体 身高 颈椎
点高		 腰高 胸围 腰围 臀围 肩宽
A 标准 160.0 136.0 98.1 84.0 68.2 90.9 39.9
中老年 157.0 133.2 94.2 91.6 76.0 90.7 41.2
B 标准 160.0 136.3 98.0 88.0 76.6 94.8 40.3
中老年 157.8 133.8 95.2 90.1 78.8 91.8 41.6
C 标准 160.0 136.5 98.2 88.0 81.9 96.0 40.5
中老年 157.5 133.9 93.7 88.8 80.8 92.3 41.5

表4

因子分析总方差解释表"

成分
编号		 初始特征值 旋转载荷平方和
总计 方差
百分比/%		 累积
贡献率/%		 总计 方差百
分比/%		 累积
贡献率/%
1 8.901 44.506 44.506 7.756 38.782 38.782
2 3.143 15.716 60.222 3.039 15.195 53.977
3 1.764 8.819 69.041 2.834 14.168 68.145
4 1.098 5.488 74.529 1.277 6.384 74.529

表5

旋转后的因子分析成分矩阵"

指标
名称		 因子载荷系数 共同度
(公因子
方差)
成分1 成分2 成分3 成分4
年龄 0.021 -0.129 -0.159 0.827 0.726
体重 0.897 0.256 0.196 -0.013 0.909
身高 0.105 0.908 0.350 -0.055 0.960
颈椎点高 0.154 0.902 0.360 -0.016 0.967
腰高 0.145 0.958 -0.105 -0.072 0.955
肩宽 0.533 0.325 0.125 0.332 0.515
前胸宽 0.670 0.169 0.068 0.166 0.510
后背宽 0.665 0.176 0.201 0.208 0.556
腹臀厚 0.837 0.006 -0.011 -0.026 0.509
前腰节长 0.292 0.130 0.861 -0.090 0.702
颈侧点到乳点 0.656 0.124 0.159 0.434 0.852
后腰节长 0.187 0.123 0.871 -0.160 0.659
背长 0.049 0.177 0.889 0.095 0.834
颈根围 0.670 0.288 0.082 -0.113 0.833
胸围 0.930 0.014 0.110 0.081 0.552
胸下围 0.916 -0.012 0.109 0.082 0.885
腰围 0.896 -0.025 0.256 0.012 0.858
臀围 0.814 0.195 0.140 -0.068 0.868
臂根围 0.680 0.139 0.120 -0.177 0.725
乳间距 0.620 -0.126 -0.004 0.331 0.528

表6

因子中各变量的相关指数"

因子类别 变量名称 R ˉ j 2指数
围度因子 体重 0.506
肩宽 0.217
前胸宽 0.270
后背宽 0.295
腰臀厚 0.366
颈侧点到乳点 0.294
颈根围 0.265
胸围 0.498
胸下围 0.478
腰围 0.472
臀围 0.393
臂根围 0.253
乳间距 0.207
高度因子 身高 0.822
颈椎点高 0.831
腰高 0.725
躯干长度因子 前腰节长 0.578
后腰节长 0.576
背长 0.474
年龄因子 年龄 1

表7

人体整体形态派生变量及计算公式"

形态指标 派生变量 计算公式
体
质
指
标		 体质指数 BMI BMI=体重/身高2
高瘦指数 身胸比 身胸比=身高/胸围
身腰比 身腰比=身高/腰围
身臀比 身臀比=身高/臀围
丰满指数 胸腰差 胸腰差=胸围-腰围
臀腰差 臀腰差=臀围-腰围
胸凸量 胸凸量=上胸围-下胸围
躯干形态指标 前后腰节差 前后腰节差=
前腰节长-后腰节长

表8

体质指标各变量与BMI的相关性"

指标 BMI 个案数
皮尔逊相关性 Sig.(双尾)
身胸比 -0.849 0.000 207
身腰比 -0.863 0.000 207
身臀比 -0.839 0.000 207
胸腰差 -0.187 0.007 207
臀腰差 -0.494 0.000 207
胸凸量 0.032 0.643 207

图1

以2种标准进行聚类的SSE-K曲线"

表9

以前后腰节差为基准的体型分类"

体型
分类		 样本
频数		 样本
占比/%		 (平均值±
标准差)/cm		 最终聚类
中心/cm		 各类别
相关性
驼背体 47 24.64 -1.51±0.73 -1.35 F=473.26
p=0.00
微驼背体 60 28.99 0.17±0.39 -0.31
正常体 49 23.67 1.24±0.27 0.35
挺胸体 51 24.64 2.74±0.79 1.28

表10

4种体型的前后腰节差的取值范围"

体型分类 初始取值
范围/cm		 均值/
cm		 二次调整
取值范围/cm
驼背体 -2.24~-0.78 -1.51 -2.35~-0.67
微驼背体 -0.22~0.56 0.17 -0.67~0.70
正常体 0.97~1.51 1.24 0.70~1.99
挺胸体 1.95~3.53 2.74 1.99~3.49

表11

以腰围、身腰比、臀腰差和胸腰差为基准的体型分类"

体型
分类		 样本
频数		 样本
占比/%		 腰围/cm 身腰比 胸腰差/cm 臀腰差/cm
平均值±
标准差		 最终聚类
中心		 平均值±
标准差		 最终聚类
中心		 平均值±
标准差		 最终聚类
中心		 平均值±
标准差		 最终聚类
中心
X型 44 21.26 69.69±3.73 -1.16 2.26±0.12 1.26 15.19±2.78 0.97 18.75±2.64 1.15
H型 79 38.16 76.19±3.04 -0.33 2.07±0.08 -0.28 11.07±3.15 -0.08 14.07±2.54 0.24
小A型 67 32.37 83.87±3.59 0.65 1.88±0.07 -0.72 10.59±3.30 -0.20 10.00±3.06 -0.57
A型 17 8.21 94.29±5.48 1.98 1.68±0.10 -1.73 6.02±3.71 -1.35 3.25±4.10 -1.87
各特征类别
相关性		 F 254.76 248.96 38.90 150.88
p 0.00 0.00 0.00 0.00

表12

4种体型的腰围、身腰比、臀腰差和胸腰差的取值范围"

类别 腰围/cm 身腰比 胸腰差/cm 臀腰差/cm
初始取值
范围		 均值 二次调整
取值范围		 初始取值
范围		 均值 二次调整取
值范围		 初始取值
范围		 均值 二次调整取
值范围		 初始取值
范围		 均值 二次调整取
值范围/cm
X型 65.96~73.42 69.69 66.4~73.0 2.14~2.38 2.26 2.17~2.36 12.41~17.97 15.19 13.1~17.3 16.11~21.39 18.75 16.4~21.1
H型 73.15~79.23 76.19 73.0~80.0 1.99~2.15 2.07 1.98~2.17 7.92~14.22 11.07 10.8~13.1 11.53~16.61 14.07 12.0~16.4
小A型 80.28~87.46 83.87 80.0~89.0 1.81~1.95 1.88 1.78~1.98 7.29~13.89 10.59 8.3~10.8 6.94~13.06 10.00 6.6~12.0
A型 88.81~99.77 94.29 89.0~99.5 1.58~1.78 1.68 1.58~1.78 2.31~9.73 6.02 3.7~8.3 -0.85~7.35 3.25 -0.1~6.6
[1] 项鑫, 王乙. 中国人口老龄化现状、特点、原因及对策[J]. 中国老年学杂志, 2021, 41(18): 4149-4152.
XIANG Xin, WANG Yi. Current situation, characteristics, causes and countermeasures of population aging in China[J]. Chinese Journal of Gerontology, 2021, 41(18): 4149-4152.
[2] 陈明艳. 成年女性体型特征及其服装样板设计[J]. 纺织学报, 2005, 26(3): 121-124.
CHEN Mingyan. Body characteristics of the mature female and the design of template for suit-dress[J]. Journal of Textile Reasearch, 2005, 26(3): 121-124.
[3] 吴巧英, 袁观洛. 中老年女性与青年女性体型比较研究[J]. 东华大学学报(自然科学版), 2004, 20(1): 66-71.
WU Qiaoying, YUAN Guanluo. Comparison on characteristics of body form between middle-aged women and young women[J]. Journal of Donghua Univer-sity (Natural Science), 2004, 20(1): 66- 71.
[4] 陈晓玲, 彭小琴, 黄家剑. 湖南女大学生体型特征与分类研究[J]. 针织工业, 2021(8): 72-77.
CHEN Xiaoling, PENG Xiaoqin, HUANG Jiajian. Body shape characteristics and classification of female college students in Hunan province[J]. Knitting Industries, 2021(8): 72-77.
[5] 汪海仙, 尚笑梅. 人体体型分类方法研究综述[J]. 现代丝绸科学与技术, 2019, 34(3): 37-40.
WANG Haixian, SHANG Xiaomei. A review of research on human body classification methods[J]. Modern Silk Science & Technology, 2019, 34(3): 37-40.
[6] 杨玫. 关中地区中老年体型特征及服装结构设计研究[D]. 西安: 西安工程大学, 2016: 31.
YANG mei. The research on clothing structure design and body characteristics of the middle-aged in Guan Zhong region[D]. Xi'an: Xi'an Polytechnic University, 2016: 31.
[7] 于晓坤, 胡帆, 朱达辉, 等. 上海地区中老年女性体型研究[J]. 北京服装学院学报(自然科学版), 2016, 36(4): 9-17.
YU Xiaokun, HU Fan, ZHU Dahui, et al. Research on body shape of the middle and old aged women in Shanghai area[J]. Journal of Beijing Institute of Fashion Technology (Natural Science Edition), 2016, 36(4): 9-17.
[8] 邢英梅, 王竹君, 阚燕, 等. 基于因子分析和分层聚类的成年女性体型特征识别[J]. 河南工程学院学报(自然科学版), 2019, 31(2): 8-12.
XING Yingmei, WANG Zhujun, KAN Yan, et al. Feature identification of female's body type based on factor analysis and hierarchical clustering[J]. Journal of Henan University of Engineering(Natural Science Edition), 2019, 31(2): 8-12.
[9] 杨蕾, 马凯. 北京地区中年女性体型细分研究[J]. 北京服装学院学报(自然科学版), 2021, 41(2): 35-40.
YANG Lei, MA Kai. Body shape classification of middle-aged women in Beijing[J]. Journal of Beijing Institute of Fashion Technology(Natural Science Edition), 2021, 41(2): 35-40.
[10] 金娟凤, 孙洁, 倪世明, 等. 基于三维人体测量的青年女性臀部体型细分[J]. 纺织学报, 2013, 34(9): 108-112.
JIN Juanfeng, SUN Jie, NI Shiming, et al. Research on subdividing of young female's hip shapes based on 3-D body measurement[J]. Journal of Textile Research, 2013, 34(9): 108-112.
[11] 方方, 王子英. K-means聚类分析在人体体型分类中的应用[J]. 东华大学学报(自然科学版), 2014, 40(5): 593-598.
FANG Fang, WANG Ziying. Application of K-means clustering analysis in the body shape classification[J]. Journal of Donghua University (Natural Science), 2014, 40(5): 593-598.
[12] 韩凌波. K-均值算法中聚类个数优化问题研究[J]. 四川理工学院学报(自然科学版), 2012, 25(2): 77-80.
HAN Lingbo. Optimization study on class number of K-means algorithm[J]. Journal of Sichuan University of Science & Engineering (Natural Science Edition), 2012, 25(2): 77-80.
[1] 韩非, 郎晨宏, 邱夷平. 废旧纺织品循环经济的监督检验体系研究进展[J]. 纺织学报, 2023, 44(03): 231-238.
[2] 陈佳, 杨聪聪, 刘军平, 何儒汉, 梁金星. 手绘草图到服装图像的跨域生成[J]. 纺织学报, 2023, 44(01): 171-178.
[3] 钟泽君, 张贝贝, 徐凯忆, 王若雯, 顾冰菲. 基于特征参数的青年女性乳房形态分析[J]. 纺织学报, 2022, 43(10): 148-154.
[4] 汪芬芬, 王革辉, 黄添宜, 张向辉, 王永荣. 正侧面形态特征驱动的女性腿型分类[J]. 纺织学报, 2022, 43(09): 188-194.
[5] 刘咏梅, 薛惠心, 徐丹娘, 张向辉. 2020版东华女装原型的持续性结构修正[J]. 纺织学报, 2022, 43(07): 135-140.
[6] 张健, 徐凯忆, 赵崧灵, 顾冰菲. 基于二维照片的青年男性颈肩部形态分类与识别[J]. 纺织学报, 2022, 43(05): 143-149.
[7] 晋金迪, 姚彤, 王军, 孙见梅, 潘力. 大连地区老年女性体型分类研究[J]. 纺织学报, 2022, 43(05): 150-155.
[8] 王迪, 柯莹, 王宏付. Voronoi图形在参数化服装造型构建中的应用[J]. 纺织学报, 2021, 42(12): 131-137.
[9] 张印辉, 杨宏宽, 刘强, 何自芬. 基于模型压缩与感受野增强的下茧实时检测[J]. 纺织学报, 2021, 42(11): 29-38.
[10] 金鹏, 薛哲彬, 江润恬, 刘丹宇, 张弛. 具有安全防护功能的智能盲人服设计[J]. 纺织学报, 2021, 42(08): 135-143.
[11] 唐茜, 张冰冰, 郑笑雨. 婴幼儿可穿戴智能监测服装设计[J]. 纺织学报, 2021, 42(08): 156-160.
[12] 徐增波, 张玲, 张艳红, 陈桂清. 基于复杂网络提取和支持向量机模型分类的服装领型研究[J]. 纺织学报, 2021, 42(06): 146-152.
[13] 崔文, 李小辉. 女装衣身前浮余量与人体胸凸形态的关系[J]. 纺织学报, 2021, 42(04): 139-143.
[14] 王婷, 顾冰菲. 基于二维图像的青年女性颈肩部形态自动识别[J]. 纺织学报, 2020, 41(12): 111-117.
[15] 张恒. 基于翻领松量结构模型的翻折领结构设计方法[J]. 纺织学报, 2020, 41(11): 128-135.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号　 京公网安备11010502044800号
版权所有 © 2021 《纺织学报》编辑部
地址： 北京市朝阳区延静里中街3号主楼6层《纺织学报》杂志社　邮政编码：100025　
电话：010-65017711,65917740　传真：010-65016539　Email:fangzhixuebao@vip.126.com
本系统由北京玛格泰克科技发展有限公司设计开发