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Analysis of Female Lower Body Shapes for the Development of Slacks Patterns : Exploring Body Clusters Using Machine Learning
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- 발행기관
- 국제문화기술진흥원 바로가기
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- 간행물
- International Journal of Advanced Culture Technology(IJACT) KCI 등재 바로가기
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- 통권
- Volume 12 Number 3 (2024.09)바로가기
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- 페이지
- pp.434-440
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A456520
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- SIZE KOREA updates body measurement data every five years, providing essential information for the fashion industry. This anthropometric data is widely used to diagnose consumer body shapes and develop optimal clothing sizes. Artificial intelligence, particularly machine learning, excels in predicting such body shape classifications. This study seeks to enhance the suitability of clothing design by applying the new analytical methodology of machine learning techniques to better capture and classify the unique body shapes of Korean women. In this study, machine learning techniques such as K-means clustering, Silhouette analysis, and Decision Tree analysis were used to classify the lower body shapes of Korean women in their twenties and identify standard body shapes useful for slacks design. The results showed that the lower body of the age group could be classified into three categories: 'small stature' (the majority), 'tall with an average lower body volume,' and 'medium height with a fuller lower body' (the smallest share). The three-cluster approach is validated through Silhouette analysis, which minimizes misclassification. Decision Tree analysis then further defines the criteria for these clusters, highlighting waist height and hip depth as the most significant factors, achieving a classification accuracy of 90.6%. While this study is not directly related to Robotic Process Automation, its detailed analysis of body shapes for slacks patterns can aid RPA in clothing production. Future research should continue integrating machine learning in human body and fashion design studies.
목차
Abstract
1. INTRODUCTION
2. LITERATURE REVIEW
2.1 Body Shape Typologies
2.2 Lessons Learned from Literature Review
3. METHODOLOGY
3.1 Data and Recent Changes in Korean Women’s Lower Body Measurements
3.2 Machine Learning Analysis
4. RESULTS AND DISCUSSION
4.1 Initial Stage of K-means Analysis
4.2 Silhouette Analysis
4.3 Secondary K-means Analysis
4.4 Decision Tree Analysis
5. CONCLUSION
REFERENCES
키워드
Lower Body Shapes K-means RPA Slacks Patterns Body Clusters저자
간행물 정보
발행기관
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- 발행기관명
- 국제문화기술진흥원 [The International Promotion Agency of Culture Technology]
- 설립연도
- 2009
- 분야
- 공학>공학일반
- 소개
- 본 진흥원은 문화기술(Culture Technology) 관련 산·학·연·관으로 구성된 비영리 단체이다. 문화기술(CT)은 정보통신기술(ICT), 문화적 사고 기반의 예술, 인문학, 디자인, 사회과학기술이 접목된 신융합기술(New Convergence Technology, NCT)로 정의한다. 인간의 삶의 질을 향상시키고, 진보된 방향으로 변화시키고, 문화기술 관련 분야의 학술 및 기술의 발전과 진흥에 공헌하기 위하여, 제3조의 필요한 사업을 행함을 그 목적으로 한다.
간행물
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- 간행물명
- International Journal of Advanced Culture Technology(IJACT)
- 간기
- 계간
- pISSN
- 2288-7202
- eISSN
- 2288-7318
- 수록기간
- 2013~2025
- 등재여부
- KCI 등재
- 십진분류
- KDC 600 DDC 700
이 권호 내 다른 논문 / International Journal of Advanced Culture Technology(IJACT) Volume 12 Number 3
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