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Anthropometry

Introduction to Anthropometry.

Anthropometry is the study of human sizing - the dimensions of the different parts of the body.

The simplest and most common form of anthropometry is Static Anthropometry, where people are measured in unmoving, defined postures.  The measurement points on the body are called Landmarks, and the measurement can be defined as a pure vertical or horizontal distance, a straight line, or along the surface, from one landmark to another.

The other kind of anthropometry is call Functional Anthropometry, and includes dynamic reaches and strength measurement.  This page is about static anthropometry.

This picture shows a subject marked up ready for measurement.

Some landmarks are actually bones under the surface of the skin, found by feeling and then marked.  For this reason 3-D scanning is often supplemented by some direct physical measurement.

anthrometric landmarks

This is an anthropometer being used to measure a straight-line distance between two landmarks.  An anthropometer is basically a large sliding calliper with a numerical readout.

anthropometer

Surface measurements are made with a simple flexible tape measure, or derived from scan data. tape body measurement
Sampling

Surveys can only measure a sample of the people they are interested in.  Samples sizes range from 10's to 1000's, depending on the scope and purpose.  In order to have a good match between the sample and the 'population', generally a mix of random and targeted selection is used, to make sure for example that a minority group has enough representation.  The larger the sample, the less likely it is to have an unexpected bias.

It's a characteristic of human variation that most people are near to the average, then there are proportionately fewer and fewer people towards the extremes.  In ergonomics it is normally the extremes that we are interested in, because that is where any given aspect of a design will start to "not fit".  The percentage of people who are smaller than a given size is called a "percentile", and typically designs are specified to fit from 1st/2nd/5th percentile to 95th/98th/99th.

Because the actual size of (say) the 2nd percentile is determined by the size of only 2 percent of the sample, sample size has a dramatic effect of the reliability of the resulting data - in a sample of 50 the smallest subject is the 2nd percentile, so if that individual happens to be particularly small, or the same size as the 2nd smallest, that will produce an error.  In practice statistics are used to smooth out the variation, but really then the error is just spread evenly across all the percentiles.

It is difficult to recruit volunteers who are extremely large or small, and in general government health surveys are much more successful at this than commercial clothing surveys.  For that reason clothing surveys characteristically show people to be taller and lighter than do government surveys.

What percentiles to use?

Once you've identified the right anthropometry data, and understood the measurement, you have to decide what percentiles to design for.  It's best to see this question in terms of excluding a percentage of users from the design, and then formally to consider:
  • What happens for excluded users - discomfort, inconvenience, danger etc?  The more severe the consequences, the fewer exclusions you can allow.
  • Do the excluded users expect this (e.g. a very tall person may be used to being cramped in an economy aircraft seat, but not in a luxury car)?
  • Can you warn the excluded users?
  • Are there degrees of exclusion that you should consider, beyond the basic target?  For example, set 95th percentile for one non-critical dimension and 99th for another that is more crucial.
  • How much would it cost to increase the design range?
Testing

It's never safe to go directly from anthropometric data to the finished design.  Any dataset has an error rate, and also the way the measurement was taken may not relate all that well to the way people will fit into and use your design in real life.

In addition there is an important issue that if more than one dimension has to fit, every extra dimension will exclude some more people.  Consequently a series of 2nd-to-98th percentile dimensions excludes a lot more that 4% of the users.

So you should always test with a Fitting Trial.