Automated Inclusive Design Heuristics Generation with Graph Mining

Inclusive design is a concept intended to promote the development of products and environments equally usable by all users, irrespective of their age or ability. This research focuses on developing a method to derive heuristics for inclusive design. The research applies the actionfunction diagram to model the interaction between a user and a product, design difference classification to compare a typical product with its inclusive counterpart, graph theory to mathematically represent the comparison relations, and graph data mining to extract the design heuristics. The goal of this research is to formalize and automate the inclusive-design heuristics generation process.

The rule generation allows statistical mining of the design guidelines from existing inclusive products. Formalization results show that, the rate of rule generation decreases as more products are added to the dataset. The automated method is particularly helpful in the developmental stages of graph mining applications for product design. The graph mining technique has capability for graph grammar induction, which is extended here to automate the generation of engineering grammars. In general, graph mining can be applied to extract design heuristics from any discrete and relational design data that can be represented as graphs.

Concept generation studies are conducted to validate the heuristics derived in this research for inclusive product design. In addition, an inclusivity rating is created and verified to evaluate the inclusiveness of the conceptual ideas. Finally, appreciation and awareness about inclusive design is important in an engineering design course, hence, a module is compiled to teach inclusive design methods in a capstone design course.

The results of the exploratory study and validation show that there is problem dependency in the application of the representation scheme. It cannot be stated with certainty at this point if the representation scheme is helpful for designing consumer products, where only the activities related to the upper body are involved. However, self-reported feedback indicates that the teaching module is effective in increasing the awareness and confidence about inclusive design.