Combining style generative adversarial networks with particle swarm optimisation-support vector regression to design affective social robot for public health intervention

In order to improve the attractiveness of social robot serving health interventions in public workspace, we propose a product design method with the combination of Style-generative adversarial network (StyleGAN) model and particle swarm optimisation-support vector regression (PSO-SVR). This paper aims to explore the modelling generation method of social robots for health intervention based on artificial intelligence generated content (AIGC) and a mapping model between the shape characteristics and users’ visual perception based on Kansei Engineering (KE). Firstly, to address the defects of typical KE over-reliance on existing samples, we introduce the StyleGAN model in AIGC to learn and train existing robots’ shape samples and generate new robots’ shape sample images. Secondly, the morphological deconstruction method is used to deconstruct the shape features of the new robot sample. Factor analysis (FA) is also used to reduce dimension and cluster emotional words and establish a Likert scale between the shape features of robots and users’ emotional vocabulary. Finally, particle swarm optimisation-support vector regression (PSO-SVR) is used to establish a mapping model between the shape features of social robots and the emotional images of users, thus obtaining the most attractive shape combination scheme. The research results showed that the StyleGAN model in AIGC can be used to assist industrial designers in creative expression and provide rich sample sources for KE; and the PSO-SVR of machine learning can be used to build a mapping model among users’ visual feelings, emotional images and shape features. In the end, we designed an attractive social robot for public health intervention.