Constructive simulation of creative concept generation process in design: a research method for difficult-to-observe design-thinking processes

Taura, Toshiharu; Yamamoto, Etsuhide; Fasiha, Mohd Yusof Nor; Goka, Masanori; Mukai, Futoshi; Nagai, Yukari; Nakashima, Hideharu

doi:10.1080/09544828.2011.637191

Cited by 50 publications

(55 citation statements)

References 31 publications

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“…Sketching is the main method of idea generation. As a quick visual representation, sketches comprise both form and function (Temple 1994;Taura et al 2012). They provide external memories of designers' thoughts, enabling functional issues to be recalled at any time and communicated with others (Suwa et al 1998).…”

Section: Previous Studiesmentioning

confidence: 99%

Collaborative sketching in crowdsourcing design: a new method for idea generation

Sun

Wei

Shi

et al. 2014

Int J Technol Des Educ

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Design integrates concepts and solves problems. Crowdsourcing design imports vast knowledge and produces creative ideas. It publishes design tasks, collects dozens of contributors' ideas and reward the best. Contributors in crowdsourcing design work individually when generating ideas. However, those who collaborate could make better use of crowd's knowledge, which might produce ideas of higher quality. By analyzing the advantages and disadvantages of crowdsourcing design, this article proposes a collaborative crowdsourcing design method that integrates crowd's sketching processes. This method uses a tree to arrange crowd's ideas and enable flexible modifications of prior ideas. A demonstration system named Sketchfans is developed, and the results of a crowdsourcing sketching experiment using this system are presented. The experimental results validate that this method is effective; participants rely heavily on the idea tree for inspiration, and the best ideas appear around the ends of branches on the idea tree. Moreover, participants displayed unique development patterns. They often developed highquality ideas from initial ideas that were regarded as poor quality. The demonstration system Sketchfans, supports this method well. Finally, we optimize Sketchfans based on analysis of participants' activities and feedback.

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Section: Previous Studiesmentioning

confidence: 99%

Collaborative sketching in crowdsourcing design: a new method for idea generation

Sun

Wei

Shi

et al. 2014

Int J Technol Des Educ

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“…The fact that brain, hand and eye actions are tightly connected in the sketching process [55,56,5] suggests that bioelectric interfaces potentially extract normal sketching patterns directly from forearm EMG signals. Analogously, the SVM classifier was able to recognize seven one-stroke shapes with nearly perfect accuracy.…”

Section: Discussionmentioning

confidence: 99%

Recognition of sketching from surface electromyography

Chen

Zhong

Gong

et al. 2017

Neural Comput & Applic

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The main objective of this study is to recognize sketching precisely and effectively in human computer interaction. A surface electromyography (sEMG) based sketching recognition method is proposed. We conducted an experiment in which we recorded the sEMG signals from the forearm muscles of two participants who were instructed to sketch seven basic one-stroke shapes. Subsequently, seven features of the sEMG time domain were extracted. After reducing data dimensionality using principal component analysis, these features were used as input vectors to a sketching recognition model based on Support Vector Machines (SVM). The performance of this model was compared to two other recognition models based on Multilayer perceptron (MLP) neural networks and Self Organization Feature Map (SOFM) neural networks. The average recognition rates for the seven basic one-stroke shapes of two participants achieved by the SVM-based and MLP-based models were both 98.5% in the test set, which were slightly superior to the performance of the SOFM classifier. Our results demonstrate the feasibility of converting forearm sEMG signals into sketching patterns.

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“…Semantic analysis approach can help to depict human thinking as a kind of network wherein a concept can lead to many other related concepts. In design problem solving, semantic analysis in allows the representation, modelling and quantification of idea generation (Georgiev et al, 2010;Taura et al, 2012;Yamamoto et al, 2009), information processing activities (Cash et al, 2014) and other high level mental processes. In the last decade, semantic approaches that use natural language processing such as lexical chain analysis have been employed to differentiate discontinuities in agreement in design problem solving (Dong, 2009).…”

Section: Semantic Analysis and Design Problem Solvingmentioning

confidence: 99%

“…On the other hand, to the best of our knowledge, not many semantic approach-based researches were carried out to investigate design creativity in real-world settings. Few exceptions are Georgiev and Georgiev (2018), Georgiev, Nagai, and Taura (2010), Georgiev and Taura, (2014), Taura et al (2012) and Yamamoto et al (2009). Existing methods related to the study of creativity, such as linkography (Goldschmidt, 2014), are laborious, effortful, and narrow in the sense that are largely focused on design ideas, rather than on the semantic content of the verbalisations.…”

Section: Introductionmentioning

confidence: 98%

Semantic Measures for Enhancing Creativity in Design Education

Georgiev

Casakin

2019

Proc. Int. Conf. Eng. Des.

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Analysing verbal data produced during the design activity is helpful to gain a better understanding of design creativity. To understand exchange of information in terms of creative outcomes, a semantic analysis approach was used to measure the semantic content of communications between students and teachers. The goal was to use this tool to analyse design conversations, and to investigate their relation to design creativity, assessed in terms of originality, usability, feasibility, aesthetics, elaboration, overall value and overall creativity. Abstraction, Polysemy, Information Content and Semantic Similarity were employed to explore 35 design conversations from the DTRS10 dataset. Main findings suggest that a significant relationship exists between Information Content and Originality, and between Information Content and Overall creativity of the produced design outcomes. Significant relations were also found between Abstraction, Polysemy, Information Content, and Feasibility, as well as between Semantic Similarity and Overall Value of the outcomes. Implications for the use of semantic measures for encouraging creativity in the design studio are discussed.

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Constructive simulation of creative concept generation process in design: a research method for difficult-to-observe design-thinking processes

Cited by 50 publications

References 31 publications

Collaborative sketching in crowdsourcing design: a new method for idea generation

Collaborative sketching in crowdsourcing design: a new method for idea generation

Recognition of sketching from surface electromyography

Semantic Measures for Enhancing Creativity in Design Education

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