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 language | English (US) |
|---|---|
| Pages (from-to) | 923-932 |
| Number of pages | 10 |
| Journal | Ergonomics |
| Volume | 61 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 3 2018 |
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All Science Journal Classification (ASJC) codes
- Human Factors and Ergonomics
- Physical Therapy, Sports Therapy and Rehabilitation
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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 journal › Article
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 -