Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79711
Title: Dynamic mapping of design elements and affective responses : a machine learning based method for affective design
Authors: Li, Z
Tian, ZG
Wang, JW
Wang, WM 
Huang, GQ
Keywords: Affective design
Kansei engineering
Machine learning
Affective responses
Design elements
Issue Date: 2018
Publisher: Taylor & Francis
Source: Journal of engineering design, 2018, v. 29, no. 7, special issue, p. 358-380 How to cite?
Journal: Journal of engineering design 
Abstract: Affective design has received more and more attention. Kansei engineering is widely used to transform consumers' affective needs into product design. Yet many previous studies used questionnaire survey to obtain consumers' affective responses, which is usually in a small scale, not updated, time-consuming and labour-intensive. The life cycle of a product is getting shorter and shorter, social trends are changing unconsciously, which results in the change of consumers' affective responses as well. Therefore, it's necessary to develop an approach for collecting consumers' affective responses extensively, dynamically and automatically. In this paper, a machine learning-based affective design dynamic mapping approach (MLADM) is proposed to overcome those challenges. It collects consumers' affective responses extensively. Besides, the collection process is continuous because new users can express their affective responses through online questionnaire. The products information is captured from online shopping websites and the products' features and images are extracted to generate questionnaire automatically. The data obtained are utilised to establish the relationship between design elements and consumers' affective responses. Four machine learning algorithms are used to model the relationship between design elements and consumers' affective responses. A case study of smart watch is conducted to illustrate the proposed approach and validate its effectiveness.
URI: http://hdl.handle.net/10397/79711
ISSN: 0954-4828
EISSN: 1466-1837
DOI: 10.1080/09544828.2018.1471671
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