Making tactile textures with predefined affective properties

A process for the design and manufacture of 3D tactile textures with predefined affective properties was developed. Twenty four tactile textures were manufactured. Texture measures from the domain of machine vision were used to characterize the digital representations of the tactile textures. To obtain affective ratings, the textures were touched, unseen, by 107 participants who scored them against natural, warm, elegant, rough, simple, and like, on a semantic differential scale. The texture measures were correlated with the participants' affective ratings using a novel feature subset evaluation method and a partial least squares genetic algorithm. Six measures were identified that are significantly correlated with human responses and are unlikely to have occurred by chance. Regression equations were used to select 48 new tactile textures that had been synthesized using mixing algorithms and which were likely to score highly against the six adjectives when touched by participants. The new textures were manufactured and rated by participants. It was found that the regression equations gave excellent predictive ability. The principal contribution of the work is the demonstration of a process, using machine vision methods and rapid prototyping, which can be used to make new tactile textures with predefined affective properties.