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Evaluation of the multivariate accommodation performance of the grid method.
Kihyo Jung1, Ochae Kwon, Heecheon You
1The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Applied Ergonomics
|August 7, 2010
Summary
The grid method efficiently identifies key body measurements for men's pants sizing. It uses representative human models (RHMs) to ensure 95% population coverage with minimal dimensions.
Area of Science:
- Anthropometry
- Apparel Design
- Human Factors Engineering
Background:
- Accurate sizing systems are crucial for men's apparel comfort and fit.
- Traditional methods often struggle to accommodate diverse body shapes effectively.
- Representative Human Models (RHMs) offer a data-driven approach to sizing system design.
Purpose of the Study:
- To evaluate the multivariate accommodation performance (MAP) of the grid method for generating RHMs.
- To determine the optimal anthropometric dimensions for men's pants sizing.
- To develop a model for predicting MAP based on anthropometric data.
Main Methods:
- Utilized the 1988 US Army male anthropometric dataset.
- Applied the grid method to generate distributed RHMs with a ± 2.5 cm fitting tolerance.
- Analyzed the impact of increasing anthropometric dimensions on MAP.
- Developed a standardized regression model to identify key factors influencing MAP.
Main Results:
- The grid method identified waist girth and crotch height as key dimensions for men's pants.
- 25 RHMs were generated to accommodate 95% of the male population.
- MAP decreased significantly with more dimensions considered (99% for one, 14% for twelve).
- A regression model explained MAP based on dimension ranges and inter-dimensional correlations.
Conclusions:
- The grid method effectively generates RHMs for men's pants sizing.
- Prioritizing key anthropometric dimensions is essential for maximizing MAP.
- The established regression model can guide efficient sizing system development.
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