Study
User-Centred DesignHigh ImpactStrong effect

Kansei Engineering + Data Mining = Personalized Product Co-design

Integrating Kansei Engineering with data mining enables the creation of co-design systems that can automatically generate product forms tailored to individual user preferences.

International Journal of Machine Learning and Computing · 2019

01

Key Findings

  • 01Kansei Engineering effectively captures user emotional responses to design elements.
  • 02Data mining can successfully translate Kansei data into actionable design parameters.
  • 03A co-design system integrating these methods can generate personalized product forms.
02

Application

Design takeaway

Incorporate user emotional responses and leverage data analytics to create adaptive design systems that facilitate personalized product co-creation.

How to apply

Develop a digital platform where users can provide emotional feedback on design elements, which is then processed by algorithms to suggest or generate customized product variations.

Project actions

  • 01Clearly define the emotional attributes you want to explore (e.g., 'exciting', 'calm', 'sophisticated').
  • 02Consider how you will collect and quantify user emotional responses.
  • 03Explore different data mining algorithms to find the best fit for pattern recognition in your design data.
03

Method & Evidence

AimCan Kansei Engineering, augmented by data mining, create an effective system for co-designing personalized product forms?
MethodMixed-methods research combining qualitative user feedback with quantitative data analysis.
ProcedureKansei Engineering principles were used to collect user emotional responses to various design attributes. Data mining techniques were then applied to these responses to identify patterns and develop predictive models for generating personalized product forms.
ContextProduct design and development, particularly for consumer goods where aesthetic and emotional appeal are critical.

Variables

IVKansei Engineering data (user emotional responses to design attributes), Data Mining algorithms.
DVPersonalized product form generation, User satisfaction with co-designed product.
CVTarget user group characteristics, Specific product category being designed, Design attributes being evaluated.
04

Strengths & Limitations

Strengths

  • +Novel integration of Kansei Engineering and data mining for co-design.
  • +Focus on emotional aspects of design, leading to potentially higher user engagement.
  • +Potential for automated personalization at scale.

Limitations

The complexity of emotional responses can be difficult to fully capture and quantify. The effectiveness of the data mining models relies heavily on the quality and quantity of the input data.

Reliability & validity

Reliability could be assessed by re-testing users after a period to check for consistent emotional responses. Validity would be addressed by ensuring the design attributes chosen genuinely reflect the intended emotional outcomes and that the data mining accurately predicts preferences.

Think critically

To what extent can 'emotional data' truly represent a user's holistic preference, and what are the ethical considerations of designing products based on inferred emotional states?

05

Design Principles

"Design for emotional resonance and personalization through data-driven co-creation."

This approach moves beyond generic product design by allowing for deep personalization, directly addressing user emotions and aesthetic desires. It empowers users to actively participate in the design process, leading to higher product satisfaction and market relevance.

06

What This Means for Your Design

Imagine a system that learns what you like about products (like how a certain color makes you feel happy) and then uses that information to help you design your own version of a product, making it just right for you.

How to use in your project

  • 1.Use this research to justify the use of Kansei Engineering and data analysis in your design project for understanding user emotions and preferences.
  • 2.Reference the integration of these techniques as a method for achieving personalized design outcomes.
07

Add to My Project

08

Quick Cite

(2019). Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System. International Journal of Machine Learning and Computing. https://doi.org/10.18178/ijmlc.2019.9.1.767 Retrieved from https://designdex.org/study/46ebc8a0-2c47-44d8-aa42-712fbd1d7d5b/kansei-engineering-data-mining-personalized-product-co-design

Paragraph starter

This design project draws inspiration from research integrating Kansei Engineering and data mining (Sakornsathien et al., 2019) to develop a personalized product co-design system. By systematically capturing and analyzing user emotional responses to design attributes, the aim is to create a design process that is deeply user-centered and capable of generating bespoke product forms tailored to individual aesthetic and emotional preferences.

09

Source

International Journal of Machine Learning and Computing

Application of Kansei Engineering and Data Mining in Developing an Ingenious Product Co-design System

journal · 2019

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Questions about this research

What does the research say about kansei engineering + data mining = personalized product co-design?
Incorporate user emotional responses and leverage data analytics to create adaptive design systems that facilitate personalized product co-creation. Evidence: International Journal of Machine Learning and Computing (2019).
Why does "Kansei Engineering + Data Mining = Personalized Product Co-design" matter for design?
This approach moves beyond generic product design by allowing for deep personalization, directly addressing user emotions and aesthetic desires. It empowers users to actively participate in the design process, leading to higher product satisfaction and market relevance.
How can designers apply this research?
Incorporate user emotional responses and leverage data analytics to create adaptive design systems that facilitate personalized product co-creation.
What were the main findings?
Kansei Engineering effectively captures user emotional responses to design elements.. Data mining can successfully translate Kansei data into actionable design parameters.. A co-design system integrating these methods can generate personalized product forms.
What research method was used?
Mixed-methods research combining qualitative user feedback with quantitative data analysis..
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2019 journal from International Journal of Machine Learning and Computing.
What should I do differently in my next project?
Develop a digital platform where users can provide emotional feedback on design elements, which is then processed by algorithms to suggest or generate customized product variations.
What are the limitations?
The accuracy of the system is dependent on the quality and representativeness of the initial Kansei data collected from the target group.
Is there evidence that kansei engineering affects design outcomes?
The study demonstrates that by combining methods for understanding user emotions (Kansei Engineering) with techniques for analyzing large datasets (Data Mining), it's possible to build systems that help users co-create products perfectly suited to their individual tastes. This approach moves beyond generic product desi Source: International Journal of Machine Learning and Computing (2019).
Where does this data mining research apply?
Product design and development, particularly for consumer goods where aesthetic and emotional appeal are critical. It sits within user-centred design research on designdex.org.

Related research topics

kansei engineering design research · evidence on kansei engineering · does kansei engineering improve design outcomes · data mining studies for designers · kansei engineering and data mining findings · user-centred design research evidence