Developing quantitative metrics to predict users' perceptions of interface design

Users' perceptions of an interface play an important role in influencing their acceptance of a product. Currently, however, designers lack an objective method to predict users' perceptions and to guide decisions during the design process. We propose a statistical image analysis method that takes the user interface as an input and computes image features that can be used to inform design. Based on these metrics, we then implemented a supervised learning algorithm to predict users' perceptions. We tested this method with a case study involving car infotainment systems. The results showed that the algorithm can capture users' perceptual ratings quite well, in particular for the perceptions of clutter vs. organized, and aggressive vs. calm. We discuss the implications of this computational method for UI design.