Eye tracking data understanding for product representation studies

Abstract

Within the mechanical engineering discipline, product representation studies have been used to inform engineers on the suitability of their product designs for prospective customers. However, these studies are mainly based in customers' oral responses leading engineers to modify the product design accordingly. In contrast, we consider the eye tracking data associated with customer judgments of 2D and 3D product representation studies. Eye tracking data contains unforeseen facts and patterns not captured through customers' oral responses. In this research, we conduct data analysis and present a set of features for analyzing similar eye tracking studies. These features include (1) question-based analysis, (2) question and category dependencies, (3) product and category dependencies, (4) gender impact and (5) experiment repeatability situations. In addition, a brief comparison of the 2D and 3D product representation experiments is described for each feature. Copyright 2014 ACM.