Occupant Packaging Research Articles

Development of a Hierarchical Estimation Method for Anthropometric Variables

Most regression models of anthropometric variables use stature and/or weight as regressors; however, these ‘flat’ regression models result in large errors for anthropometric variables having low correlations with the regressors. For better accuracy in estimating anthropometric variables, this study proposed a method to estimate anthropometric variables in a hierarchical manner based on the geometric and statistical relationships among the variables. By applying the proposed approach to 60 anthropometric variables selected for the design of an occupant package layout in a passenger car, hierarchical estimation structures were constructed and then based on the estimation structures hierarchical regression models were developed with the 1988 US Army anthropometric survey data. The hierarchical regression models were compared with the corresponding flat regression models in terms of adjusted R2 and SE, resulting in on average a 55% increase in adjusted R2 and a 31% decrease in SE when compared to the corresponding flat models.

인체변수의 계층적 추정기법 개발 및 적용

Most regression models of anthropometric variables use stature and/or weight as regressors; however, these `flat` regression models result in large errors for anthropometric variables having low correlations with the regressors. To develop more accurate regression models for anthropometric variables, this study proposed a method to estimate anthropometric variables in a hierarchical manner based on the relationships among the variables and a process to develop and improve corresponding regression models. By applying the proposed approach, a hierarchical estimation structure was constructed for 59 anthropometric variables selected for the occupant package design of a passenger car and corresponding regression models were developed with the 1988 US Army anthropometric survey data. The hierarchical regression models were compared with the corresponding flat regression models in terms of accuracy. As results, the standard errors of the hierarchical regression models decreased by 28% (4.3mm) on average compared with those of the flat models.