Reweighting anthropometric data using a nearest neighbour approach

Kannan Anil Kumar, Matthew B. Parkinson

Research output: Contribution to journalArticle

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

Fingerprint

Health Services Needs and Demand
Body Size
Clustering algorithms
Population
Statistical Models
Sampling
Practice Guidelines
Cluster Analysis
Body Mass Index
Weights and Measures
Research
proportionality
regression

All Science Journal Classification (ASJC) codes

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

Cite this

@article{53babd2b01f44d67925dc71efde8b888,
title = "Reweighting anthropometric data using a nearest neighbour approach",
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.",
author = "Kumar, {Kannan Anil} and Parkinson, {Matthew B.}",
year = "2018",
month = "7",
day = "3",
doi = "10.1080/00140139.2017.1421265",
language = "English (US)",
volume = "61",
pages = "923--932",
journal = "Ergonomics",
issn = "0014-0139",
publisher = "Taylor and Francis Ltd.",
number = "7",

}

Reweighting anthropometric data using a nearest neighbour approach. / Kumar, Kannan Anil; Parkinson, Matthew B.

In: Ergonomics, Vol. 61, No. 7, 03.07.2018, p. 923-932.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Reweighting anthropometric data using a nearest neighbour approach

AU - Kumar, Kannan Anil

AU - Parkinson, Matthew B.

PY - 2018/7/3

Y1 - 2018/7/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85042225919&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042225919&partnerID=8YFLogxK

U2 - 10.1080/00140139.2017.1421265

DO - 10.1080/00140139.2017.1421265

M3 - Article

C2 - 29461142

AN - SCOPUS:85042225919

VL - 61

SP - 923

EP - 932

JO - Ergonomics

JF - Ergonomics

SN - 0014-0139

IS - 7

ER -