Research on Sound Imagery of Electric Shavers Based on Kansei Engineering and Multiple Artificial Neural Networks

The electric shaver market in China reach 26.3 billion RMB by 2021. Nowadays, addition to functional satisfaction, consumers are increasingly focused on the emotional imagery conveyed products with multiple-senses, and shavers not only shaped attract consumers, but their product sound also conveys a unique imagery. Based Kansei engineering artificial neural networks, this research explored of shavers. First, we collected wide sample (230 types) obtained consumers’ perceptual vocabulary (85,710 items) through web crawler. multidimensional scaling method cluster analysis were used condense into 34 representative samples 3 groups words; then, semantic differential was assess users’ evaluation values. design elements (including item category) classified using Heardrec Devices ArtemiS 13.6 software, and, finally, multiple linear non-linear correlation prediction models (four between values established quantification theory type I, general regression network, back propagation genetic algorithm-based BPNN. validated paired-sample t-test, all them had good reliability, among which BPNN best accuracy. In research, four constructed. aim apply higher accuracy imagery, while giving specific accurate metrics references.