Reweighting Anthropometric Data using a Nearest Neighbor Approach

Open Access
Anil Kumar, Kannan
Graduate Program:
Engineering Design
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 29, 2017
Committee Members:
  • Matt Parkinson, Thesis Advisor
  • Anthropometry
  • Design for Human Variability
  • Data reweighting
  • Binning
  • Data weighting
A new method to reweight anthropometric data from a reference population to match that of a target population is proposed. When designing products and environments, detailed data on body size and shape are seldom available for the specific user population. Instead, the available data are outdated or represent a population that is demographically different on factors that are known to affect anthropometry. One way to mitigate this issue is to reweight available data such that they provide an accurate estimate of the target population of interest. This is done by assigning a statistical weight to each individual in the reference data, increasing or decreasing their influence on statistical models of the whole. This paper presents a new approach to reweighting these data. Instead of stratified sampling (the traditional approach), the proposed method uses a clustering algorithm to identify relationships between the target and reference populations using their height, mass, and body mass index. The newly weighted data were shown to provide more accurate estimates than traditional approaches. Data weighted with the new approach was used in different multivariate design test cases to demonstrate its use in real-world design applications. The improved accuracy that accompanies this method provides designers with an alternative to data synthesis techniques as they seek appropriate data to guide their design practice.