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Population Models

Watch “Fundamentals of DfHV: Experiments” for a summary on setting up experiments for task-oriented models.

A traditional spatial analysis design tool that is an alternative to the boundary manikin approach is the task-oriented percentile model. Such a model makes use of tests involving a sample population performing a task related to the dimension under consideration. Required adjustability is then defined by the selections or capabilities of the desired proportion of users. Both a sufficiently large representative sample population and a workable prototype are required. For example, this approach forms the basis for the SAE International recommended practices, which are used for vehicle design.

These models are an improvement on manikin-based approaches in some ways, since they specifically model the outcome measure of interest (e.g., reach, eye location, driver-selected seat position), rather than trying to predict the population distributions of those outcomes from boundary cases defined by anthropometry. However, they require extensive human-subject data from a similar task scenario and they are essentially univariate, dealing with only a single outcome measure (e.g., reach) at one time. Population models are not easily adapted to other conditions, tasks, or populations; they may quickly become outdated as these qualities change over time.

Hybrid models expand on the population model approach by allowing the model to be extrapolated to populations different than the one from which the data were gathered. Hybrid models combine many of the virtues of the boundary manikin and population model approaches. Implementation of hybrid models is discussed on the Hybrid Models guidelines page.

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Posted on

September 28, 2017