计算机集成制造系统 ›› 2023, Vol. 29 ›› Issue (2): 616-627.DOI: 10.13196/j.cims.2023.02.022

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基于内隐测量和BP神经网络的产品色彩情感化设计

丁满,丁婷婷,宋美佳,张新新+,刘震   

  1. 河北工业大学建筑与艺术设计学院
  • 出版日期:2023-02-28 发布日期:2023-03-03
  • 基金资助:
    国家自然科学基金资助项目(52275243)。

Product color emotional design method based on implicit measurement and BP neural network

DING Man,DING Tingting,SONG Meijia,ZHANG Xinxin+,LIU Zhen   

  1. School of Architecture and Art Design,Hebei University of Technology
  • Online:2023-02-28 Published:2023-03-03
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.52275243).

摘要: 为准确获取用户对产品色彩的情感意象,有效缩小设计师与用户之间的情感认知差距,以电熨斗产品的色彩设计为研究对象,以内隐测量技术和BP神经网络为研究手段,开展用户情感认知的演化机理研究。通过眼动、脑电和行为实验分别提取被试面对不同色彩方案时的眼动指标、脑电信号和情感意象评价值,结合BP神经网络构建基于“产品色彩—生理信号—情感意象”三层关联结构的产品色彩情感意象评价模型。实验表明,所提方法能够准确获取用户对产品色彩的情感认知规律,为产品色彩情感化设计研究提供有效的理论依据和数据支撑。

关键词: 情感意象, 产品色彩设计, 眼动, 脑电, BP神经网络

Abstract: To accurately obtain users' emotional image of product colors and effectively narrow the gap in emotional cognition between designers and users,the product color design of electric iron was used to be the research object,and implicit measurement technology and Back Propagation Neural Network (BPNN) were used to be the research methods to carry out research on the evolution mechanism of users' emotional cognition.Through the eye movement experiment,Electroencephalography (EEG) experiment and behavioral experiment,the eye movement indicators,EEG signals and emotional evaluation values of participants when faced with different color design schemes were extracted respectively.Combined with BPNN,a product color emotional image evaluation model based on the three-layer correlation structure of “product color-physiological signal-emotional image” was built.The experimental results showed that the proposed method could accurately obtain users' emotional cognition law of product color,and provide effective theoretical basis and data support for the research of product color emotional design.

Key words: emotional image, product color design, eye movement, electroencephalogram, back propagation neural network

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