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人机交互情感设计与创新文化创意产品设计。

Human-computer interaction emotional design and innovative cultural and creative product design.

作者信息

Gao Zhimin, Huang Jiaxi

机构信息

College of Fine Arts and Design, Huaihua University, Huaihua, China.

出版信息

Front Psychol. 2022 Sep 27;13:982303. doi: 10.3389/fpsyg.2022.982303. eCollection 2022.

Abstract

To make the interface design of computer application system better, meet the psychological and emotional needs of users, and be more humanized, the emotional factor is increasingly valued by interface designers. In the design of human-computer interaction graphical interfaces, the designer attaches great importance to the emotional design of the interface, and enhances the humanized design of the interface, which cannot only improve the comfort of the interface, but also improve the fun of the interface, to ensure the psychological and emotional needs of users can be better satisfied. It may acquire information that is favorable to innovative design by utilizing cluster analysis algorithm to tackle the problem of complicated cultural information, and then utilize cellular genetic algorithm to carry out creative design of cultural items. It increases the availability of cultural and creative goods. The classic cluster analysis technique offers the maximum data clustering effect of 53.3%, according to the findings of this paper's experiments. While the improved cluster analysis algorithm has the highest data clustering effect of 90%. It can be seen that the improved cluster analysis algorithm can effectively perform cluster analysis on a large amount of data in cultural and creative products. It thus finds out the most suitable designer's creative information, which helps designers create better products.

摘要

为了使计算机应用系统的界面设计更好,满足用户的心理和情感需求,更加人性化,情感因素越来越受到界面设计师的重视。在人机交互图形界面设计中,设计师十分重视界面的情感设计,加强界面的人性化设计,这不仅能提高界面的舒适度,还能增加界面的趣味性,以确保更好地满足用户的心理和情感需求。通过利用聚类分析算法处理复杂的文化信息问题,可能获取有利于创新设计的信息,然后利用细胞遗传算法进行文化产品的创意设计。这提高了文化创意产品的可用性。根据本文实验结果,经典聚类分析技术的最大数据聚类效果为53.3%。而改进后的聚类分析算法的数据聚类效果最高可达90%。可以看出,改进后的聚类分析算法能够有效地对大量文化创意产品数据进行聚类分析。从而找出最适合设计师的创意信息,有助于设计师创造出更好的产品。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a03/9552701/da302b23a433/fpsyg-13-982303-g001.jpg

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