ISO/TR 5716:2025
(Main)Multivariate analysis using anthropometric data and a virtual fit tool
Multivariate analysis using anthropometric data and a virtual fit tool
This document specifies best practices for considering body size and shape (anthropometry) in design. Since most products, tasks, and environments interact with multiple user attributes at the same time, multivariate design techniques are necessary to obtain accurate accommodation estimates. Although the approach outlined can also incorporate user preference unrelated to anthropometry, the focus in this document is on “fit” and the spatial accommodation of users.
General Information
Standards Content (Sample)
Technical
Report
ISO/TR 5716
First edition
Multivariate analysis using
2025-11
anthropometric data and a virtual
fit tool
Reference number
© ISO 2025
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ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Univariate vs. multivariate analysis . 2
5 Virtual fit tests (VFTs) . 2
6 Multivariate design . 3
7 Other considerations . 4
7.1 General .4
7.2 Data availability .4
7.3 Cost of disaccommodation .4
7.4 Postural variability and preference .4
Annex A (informative) Details on univariate and multivariate analyses . 5
Bibliography . 8
iii
Foreword
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This document was prepared by Technical Committee ISO/TC 159, Ergonomics, Subcommittee SC 3,
Anthropometry and biomechanics.
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iv
Introduction
Guidelines for the design of artefacts, tasks, and environments with which people interact typically consider
the body size and shape of the target user population. Values for some relevant measures are specified
in tables such as ISO/TR 7250-2, with a focus on the extremes of the distribution (e.g. the 5th and 95th
percentiles). The process of identifying each measure and its associated limiting value one-at-a-time can
be termed the “univariate” approach to design. The underlying assumption is that if some percentage (e.g.
95 %) of the population is accommodated on each measure, that the overall accommodation will be 95 %.
This is readily demonstrated as false.
The increasing complexity of designs, accompanied by other demands such as globalization and secular
trends in stature and mass, necessitate more accurate — and flexible — approaches to design. This document
presents a modern approach to multivariate design in which all variables are considered simultaneously
through a process called virtual fit testing (VFT). The use of VFT facilitates the incorporation of optimization,
biomechanics modelling, and other rigorous design tools. It also provides the designer with flexibility in
the specification of designs since there is a number of different design variable combinations that produce
equivalent accommodation levels. The designer is free to choose from the possibilities based on, for example,
cost, manufacturability, or other demands.
NOTE 1 The practice outlined in this document is new for many users and can often provide accommodation
estimates that are lower (and more accurate) than those obtained using traditional univariate analysis.
NOTE 2 Conducting VFT necessitates a database consisting of the relevant measures for each of a large number
of individuals who represent the target user population. The summary statistics provided in ISO/TR 7250-2 are not
sufficient, although they can be used in conjunction with data synthesis techniques to provide the necessary detailed data.
v
Technical Report ISO/TR 5716:2025(en)
Multivariate analysis using anthropometric data and a virtual
fit tool
1 Scope
This document specifies best practices for considering body size and shape (anthropometry) in design. Since
most products, tasks, and environments interact with multiple user attributes at the same time, multivariate
design techniques are necessary to obtain accurate accommodation estimates. Although the approach
outlined can also incorporate user preference unrelated to anthropometry, the focus in this document is on
“fit” and the spatial accommodation of users.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
accommodation
when a user is able to interact with a design in the intended manner
3.2
disaccommodation
when a user is not able to interact with a design in the intended manner
3.3
design variables
aspects of a design that the designer is free to change
3.4
univariate design
considering each relevant anthropometric measure (e.g., leg length, seated eye height, etc.) separately and
independently of all other measures
3.5
multivariate design
considering all relevant anthropometric measures simultaneously
3.6
virtual fit test
VFT
virtual fitting trial
simulating the interaction of an individual user with a candidate design
4 Univariate vs. multivariate analysis
Good design practice intends for high levels of accommodation in the target user population. For example, a
designer can intend for 95 % of a population to be able to use the design safely and effectively. Data on the
body size and shape of the target user population provide information on the spatial requirements of users
and help the designer to make effective design decisions. One advantage to univariate analysis is the ease
with which the supporting data can be shared. For example, tables of percentiles, including ISO/TR 7250-2,
are readily published and disseminated through books and other traditional venues.
The univariate design approach to assessing fit involves identifying a relevant body dimension (e.g.,
th
seated hip breadth) and an associated percentile (e.g. 95 ) value above or below which individuals are
disaccommodated. For example, a recommended width between armrests of a chair can be identified
through an assessment of seated hip breadth. If the population of interest were female US military personnel
th
in the 2000s, the 95 -percentile seated hip breadth for females can be calculated from the ANSUR II dataset.
[3]
Armrests separated by this dimension would be expected to accommodate 95 % of the female population,
where accommodation is defined as having seated hip breadth less than the distance between the armrests
(Figure A.1).
However, users interact with more than one element of most products, work environments, and other
designed artefacts at a time. For example, in addition to being appropriately wide, the height and depth of a
chair will affect the user experience. The anthropometric measures associated with these design elements
can be highly correlated with each other (e.g. within measures of length) or there can be little correlation
(e.g. measures of length and measures of breadth).
Figure A.2 shows two scenarios in which univariate analyses are used to examine a multivariate design
problem involving just two measures from a population of 1 000 users. This population is much smaller
than what would typically be used for a VFT, but it is illustrative for this example. In each scenario, it is
the intention of the designer to accommodate 95 % of the population (950 individuals) on each measure.
In Figure A.2 a), the measures are highly correlated. The 50 individuals (5 %) disaccommodated on each
measure are shown. Since the measures are highly correlated, 29 of the individuals are disaccommodated
on both measures simultaneously. As a result, the overall accommodation is estimated to be 93,0 % (1 000
individuals – 50 disaccomodated on one measure – 50 disaccommodated on the other + 29 that were
disaccommodated on both). This is lower than the 95 % expected by the practitioner of univariate analysis.
Note that if the measures were perfectly correlated the 50 individuals disaccommodated on one measure
would be exactly the same as the 50 disaccommodated on the other and the expected 95 % (1 000-50-50+50)
would be achieved.
Figure A.2 b) shows the case when measures are not well correlated. In this case, only 3 individuals are
disaccommodated on both. The resulting accommodation is much lower than univariate analysis would
suggest — only 90,3 % (1 000-50-50+3). Since most multivariate problems involve 3+ measures, the actual
accommodation levels are often even lower than those shown in the simpl
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