Leveraging AI and Multivariate Analysis to Convert Product Requirements into Product Specifications

Abstract: The objective of this work is to enable designers to easily determine the optimal product dimensions to accommodate diverse user populations. Artificial intelligence (AI) is integrated with data on a population’s body size and shape (anthropometry) and a custom analysis function to convert natural-language design requirements to technical design specifications. This leverages a strength of AI to help designers to overcome the challenges of unfamiliarity with anthropometric terminology. Simultaneously, it mitigates some limitations of AI by performing the analysis in an environment specifically designed for this task. Ultimately, it allows human factors engineers and ergonomists to easily explore design trade-offs in a multivariate design space.In human factors and ergonomics, considering a wide range of anthropometry is essential to ensuring that products are usable and safe. However, the process of determining the dimensions of a product to fit a target percentage of the population is often challenging for designers, particularly when dealing with multiple product dimensions (multivariate design). Unlike univariate design, which typically focuses on optimizing a single dimension, multivariate design involves balancing multiple variables, each affecting the accommodation in different ways. While some tools for assisting in multivariate design exist, such as the Multivariate Anthropometry Testing Tool from the Human Factors and Ergonomics Society (HFES), they necessitate an anthropometry-focused approach. This approach requires designers to identify the relevant anthropometric measures for each product variable and understand each measure’s relation to the product.Artificial intelligence (AI) has great potential in making human-centered design more accessible for designers. Current Generative Pre-trained Transformer (GPT) models can identify public datasets, such as the ANSUR II data, which contain detailed anthropometry from military personnel. The models are also able to match product dimensions to relevant anthropometric measures, inferring the relationships between a design variable and the associated anthropometry. However, the existing models struggle in two critical ways. First, they fail to reliably extract information from online datasets and often report incorrect data for design recommendations. Second, they are unable to conduct multivariate analyses. When asked to size a product in more than one dimension, the current models will report a series of independent univariate solutions, which is known to overestimate overall accommodation.To address these challenges, this work introduces the incorporation of function calling in GPT to improve multivariate accommodation analysis. Function calling allows GPT to trigger a backend process that directly accesses ANSUR II anthropometric data and accurately computes accommodation based on multiple dimensions. Because the function correctly performs multivariate analyses, the GPT is able to provide several design recommendations that meet the overall target, allowing the designer to select the most appropriate one. By allowing the GPT to infer user intent and adjust function arguments, this approach overcomes key limitations of existing tools and GPT models, enabling more efficient and accurate design solutions.

Keywords: Human-centred design, ergonomics, multivariate design, artificial intelligence, anthropometry

DOI: 10.54941/ahfe1006918

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