C
Carole Bouchard
Researcher at Arts et Métiers ParisTech
Publications - 79
Citations - 775
Carole Bouchard is an academic researcher from Arts et Métiers ParisTech. The author has contributed to research in topics: Kansei & User experience design. The author has an hindex of 15, co-authored 79 publications receiving 678 citations. Previous affiliations of Carole Bouchard include ParisTech & École Normale Supérieure.
Papers
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Journal ArticleDOI
Inspiration, images and design: an investigation of designers' information gathering strategies
TL;DR: The main outcomes demonstrate that traditional and electronic resources are not used in the same way by designers, and show that information gathering strategies are strongly influenced by designers' preference.
Journal ArticleDOI
Emotional activity in early immersive design: Sketches and moodboards in virtual reality
TL;DR: The goal of this paper is to present how specific early design needs can be fulfilled by immersive technologies, and to show the level to which an immersive experience is valid for early design tasks.
Journal ArticleDOI
Open-design: A state of the art review
TL;DR: A typology of open-design of tangible artifacts that distinguishes among three currently reported varieties of practice: do-it-yourself, meta-design, and industrial ecosystems is developed.
Proceedings ArticleDOI
TRENDS: a content-based information retrieval system for designers
TL;DR: A content-based image search engine has been elaborated, starting from recommendations taken from the methodology employed by the designers in their activity, to end with a complete system incorporating image retrieval technologies and various tools to extract relevant information from these images.
Journal ArticleDOI
In Search of Design Inspiration: A Semantic-Based Approach
Rossitza Setchi,Carole Bouchard +1 more
TL;DR: The proposed approach is illustrated with examples based on the software tool developed for the needs of two of the industrial collaborators involved in the TRENDS project, and the proposed approach differs significantly from earlier approaches as it does not rely on machine learning and the availability of tagged corpuses.