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Multisensory Device 'Sentory' Demonstration Video

Multisensory Exhibition Framework & Guidelines for Scientific Artifacts

Idea Snatch Up Idea Snatch Up은 게임화를 통해 4가지 전략적 이종산업 융합 혁신 패턴을 학습하고 제조의 서비스화가 가능한 아이디어 도출을 도와주는 툴킷이다. 단시간에 협력적으로 많은 아이디어를 창출하고, 이종산업간의 융합 가능성 영역을 확장하는 것을 목표로 한다. Idea Snatch Up은 보드게임 형식이며 주요 카드는 사례카드와 산업군 카드다. 사례 카드는 Add Functionality, Offset Weakness, Customer Experience, Pivot Interindustry 4가지 관점에서 실제 이종산업 융합 사례를 소개하며 새로운 아이디어 도출을 도와준다. 산업군 카드는 제조와 서비스 관련 분야에 대한 내용을 담고 있으며 해당 카드를 이용하여 이종산업 융합 서비스 아이디어를 도출한다.

Can Serious Games Assess Decision-Making Biases? Comparing Gaming Performance, Questionnaires, and Interviews Kyoungwon Seo, Hokyoung Ryu , Jieun Kim Abstract : The limitations of self-report questionnaires and interview methods for assessing individual differences in human cognitive biases have become increasingly apparent. These limitations have led to a renewed interest in alternative modes of assessment, including for implicit and explicit aspects of human behavior (i.e., dual-process theory). Acknowledging this, the present study was conducted to develop and validate a serious game, “Don Quixote,” for measuring specific cognitive biases: the bandwagon effect and optimism bias. We hypothesized that the implicit and explicit game data would mirror the results from an interview and questionnaire, respectively. To examine this hypothesis, participants (n = 135) played the serious game and completed a questionnaire and interview in a random order for cross-validation. The results demonstrated that the implicit game data (e.g., response time) were highly correlated with the interview data. On the contrary, the explicit game data (e.g., game score) were comparable to the results from the questionnaire. These findings suggest that the serious game and the underlying intrinsic nature of its game mechanics (i.e., evoking instant responses under time pressure) are of importance for the further development of cognitive bias measures in both academia and practice. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000485. © 2018 Hogrefe Publishing.

Professor Kim Ji Eun won the iF Design Award 2018 with her deep learning-based multi-pill identifier Professor Kim Ji-eun and her Med-HCI research team from Imagine X lab (med.hanyang.ac.kr), successfully developed 'OPILL' which can automatically identify the information of the pill, and won the iF Design Award 2019. This OPILL is a deep learning and imaging process technology-based pill identification device that can provide the pill’s information by recognizing the shape, color, and imprints of the pills (up to 4 pills). It is expected that this device will efficiently sort the drug wastes in the waste collection process and facilitate redistribution of unused drugs in a safer manner.