Reweighting anthropometric data using a nearest neighbour approach

Kannan Anil Kumar, Matthew B. Parkinson

Research output: Contribution to journalArticlepeer-review

Abstract

When designing products and environments, detailed data on body size and shape are seldom available for the specific user population. 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 proposed method uses a clustering algorithm to identify relationships between the detailed and reference populations using their height, mass, and body mass index (BMI). The newly weighted data are shown to provide more accurate estimates than traditional approaches. 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.Practitioner Summary: Design practice is best guided by data on body size and shape that accurately represents the target user population. This research presents an alternative to data synthesis (e.g. regression or proportionality constants) for adapting data from one population for use in modelling another.

Original languageEnglish (US)
Pages (from-to)923-932
Number of pages10
JournalErgonomics
Volume61
Issue number7
DOIs
StatePublished - Jul 3 2018

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Physical Therapy, Sports Therapy and Rehabilitation

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