Quantification of perceptual design attributes using a crowd
Editor: Udo Lindemann, Srinivasan V, Yong Se Kim, Sang Won Lee, John Clarkson, Gaetano Cascini
Author: Ren, Yi; Burnap, Alex; Papalambros, Panos
Institution: University of Michigan, United States of America
Crowdsourcing processes can be used for design concept creation and evaluation. They also provide opportunities to study and model quantitatively how humans deal with design problems. This paper explores the use of crowdsourcing to evaluate a perceptual design attribute and to create new design concepts using this attribute. As an example, we study how perceived automobile car safety can be modeled with respect to exterior car shape design using an efficient statistical learning algorithm. Experiments with subjects using Amazon's Mechanical Turk uncover several practical issues that must be addressed when applying machine learning methods to create safe-looking car designs using crowdsourced input.