网格法多变量调节性能评估。

Evaluation of the multivariate accommodation performance of the grid method.

机构信息

The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA.

出版信息

Appl Ergon. 2010 Dec;42(1):156-61. doi: 10.1016/j.apergo.2010.06.004. Epub 2010 Aug 5.

DOI:10.1016/j.apergo.2010.06.004

PMID:20688318

Abstract

The present study examined the multivariate accommodation performance (MAP) of the grid method, a distributed representative human models (RHM) generation method, in the context of men's pants sizing system design. Using the 1988 US Army male anthropometric data and ± 2.5 cm of fitting tolerance, the grid method selected two key dimensions (waist girth and crotch height) out of 12 anthropometric dimensions and identified 25 RHMs to accommodate 95% of the population. The average MAP of the RHMs decreased dramatically as the number of anthropometric dimensions considered increased (99% for single dimension and 14% for 12 dimensions). A standardized regression model was established which explains the effects of two factors (sum of anthropometric dimension ranges; adjusted R(2) between key dimensions and other anthropometric dimensions) on the MAP of RHMs. This regression model can be used to prioritize anthropometric dimensions for efficient MAP improvement of men's pants design.

摘要

本研究考察了网格法的多变量适应性能（MAP），这是一种分布式代表性人体模型（RHM）生成方法，应用于男性裤子尺码系统设计中。使用 1988 年美国陆军男性人体测量数据和±2.5cm 的适配公差，网格法从 12 个人体测量尺寸中选择了两个关键尺寸（腰围和裆高），并确定了 25 个 RHM，以适应 95%的人群。随着考虑的人体测量尺寸数量的增加，RHMs 的平均 MAP 急剧下降（单尺寸为 99%，12 尺寸为 14%）。建立了一个标准化回归模型，解释了两个因素（人体测量尺寸范围之和；关键尺寸与其他人体测量尺寸之间的调整 R²）对 RHMs 的 MAP 的影响。该回归模型可用于为男性裤子设计的高效 MAP 改进确定人体测量尺寸的优先级。