Towards Incorporating Technology to Enhance the Stereotype Production Method in Warning Symbol Design

Abstract

OCCUPATIONAL APPLICATION Warnings are an important component of hazard control strategies in use in all types of industry. Properly designed warning symbols improve the likelihood that a warning will be recognized and understood, and they may even increase the likelihood that the warning will be heeded. Unfortunately, many commonly used warning symbols are poorly understood and may not meet accepted criteria for comprehension testing. Improving the process by which symbols are developed could affect a variety of future symbol designs. We propose new technological enhancements to traditional warning symbol design strategies—including semantic annotation, mathematical clustering, and evolutionary computation—that may improve the effectiveness of symbols designed in the future. TECHNICALABSTRACT Background:Warning symbols must comm-unicate effectively to a wide range of people. To achieve this goal, two major challenges must be overcome. First, designers must acquire user input while minimizing any unnecessary design input needed from non-users. Second, designers must develop symbols with a high likelihood of communicating effectively to a population of users that may be diverse in culture, language, and country of origin. Purpose: We evaluated clustering algorithms as a means to enhance the judgment required by designers using the stereotype production method for symbol development. Methods: Sixty-six symbol sketches, 35 from U.S. participants and 31 from Indian participants, were evaluated for the warning referent “Hot Exhaust.” A panel of three certified safety professionals semantically annotated the sketches and developed a frequency matrix that associated the presence of graphical attributes with each sketch. Direct clustering and Simple K-Means clustering were performed on the matrix. Results: Mathematical clustering was successful in identifying population stereotypes. The Simple K-Means analysis of the combined nationality matrix produced five clusters, each characterized by a hypothetical centroid symbol analogous to the population stereotypes of the participant group. Only three of the original 35 attributes were contained among these centroids, meaning that the primary differentiators of the population stereotypes were these three primary attributes. Direct clustering found the same three primary attributes—“pipe/stack,” “emission lines,” and “flame.” Further, clustering the nationalities separately revealed that some attributes were universal between the two nationalities, while others seemed to have a culture or country-of-origin sensitivity. Conclusions: Clustering can be used by designers to group sketches into similar families and to identify the attributes of most interest for final symbols. Furthermore, some attributes appear to be “recessive” while others appear to be “dominant” with regard to culture.