The Anthropometrics of Disability - Designing Accessible Communities

and use databases effectively. The AIS system developed at TU Delft is an
example. Testing shows that students use it more effectively than data in
books.
• A reasoning model for designers should be developed to help them go
beyond code requirements. The codes and standards should only be guides.
The reasoning model could help designers provide an authoritative rationale
for departing from standards (e.g. equivalent facilitation).
• Policy incentives could improve the utilization of knowledge. For example,
government purchasing policies could encourage designers to use
anthropometric/ergonomic tools as a means to accommodate a given
percentage of the population.
• There needs to be more discussion on the most effective way to present and
collect anthropometric data in a way that will be useful to designers. This is a
major issue of concern among researchers. How can we increase demand for
research products One possibility is to make using anthropometric interesting
and entertaining.
Another set of recommendations focused on improving data analysis in research to
make use of results more effective:
• Data analysis strategies are needed before large-scale surveys are
implemented. Otherwise, the data may not be usable.
• In analysis of data, we should not rely on the normal distribution model. We
need to develop new strategies for analysis that take into consideration the
great variation in the population of people with disabilities and the fact that
there will be small numbers of people in each multidimensional sampling
category.
• Averages are meaningless in design for disabilities. Report findings in
percentiles and also individual performance. This allows users of the data to
determine who is being accommodated and, where the aggregated data is
inadequate or not detailed enough, the individual data can be used in
human modeling simulations of individuals performing specific tasks.
• Cumulative normal distributions could be more effective than percentiles for
code development because they identify clearly which part of the population
is included; however, such an approach implies an assumption of normality,
which might not be an accurate way to represent the range of body sizes
and abilities of those who have disabilities.
• Explore how static anthropometry could be used to predict baseline
functional measures. This would help us adapt static data, which is easier to
collect, for application to functional tasks.
• Develop 3 -D equivalents of percentiles in order to make it easy to generate
recommendations for 3 -D data.
4. IDENTIFYING RESEARCH PRIORITIES
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