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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

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Published on: March 21, 2021

A shape classification scheme for female torso.

Peng Li¹, K Blake Mitchell¹

¹US Army DEVCOM Soldier Center, 15 General Greene Ave, Natick, MA, 01760, USA.

Applied Ergonomics

|September 20, 2022

Summary

This summary is machine-generated.

This study introduces a female torso shape classification system using body measurements and 3D scans. It identifies key measurements for size and shape, aiding the apparel industry in better pattern design and fit.

Keywords:

ANSUR II Anthropometry Female torso shape Principal component analysis Shape classification Waist circumference

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Area of Science:

Anthropometry
Apparel Design
Biomechanical Engineering

Background:

Accurate female torso shape classification is crucial for the clothing industry and apparel researchers.
Existing methods may lack objectivity or comprehensive data.
Improving pattern design and fit requires a robust classification scheme.

Purpose of the Study:

To develop an objective classification scheme for female torso shapes.
To identify dominant anthropometric measurements defining torso shape and size.
To create a practical tool for apparel design and research.

Main Methods:

Utilized principal component analysis (PCA) on anthropometric measurements and 3D body scan data.
Analyzed torso-related measurements including Waist Circumference, Chest-Waist Drop, and Waist-Buttock Drop.
Generated mean shapes for each category from 3D scans.

Main Results:

Waist Circumference was identified as the most dominant variable for overall female torso size.
Chest-Waist Drop and Waist-Buttock Drop were found to jointly define local torso shape.
A classification scheme with nine shape categories based on these measurements was developed.

Conclusions:

The developed classification scheme provides an objective method for categorizing female torso shapes.
The findings enable more precise pattern design and improved apparel fit.
This research contributes to advancements in anthropometric data application within the apparel industry.