Meta-KANSEI Modeling with Valence-Arousal fMRI Dataset of Brain

Shi, Fuqian; Dey, Nilanjan; Ashour, Amira S.; Sifaki-Pistolla, Dimitra; Sherratt, R. Simon

doi:10.1007/s12559-018-9614-5

Cited by 23 publications

(11 citation statements)

References 42 publications

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“…Thus, Kansei is useful in influencing implicit users' insight related to the product design element and incorporating these insights with a new product [26]. Recently, people have shown interest in the application of Kansei in various fields such as industrial products, healthcare, education and e-commerce [27]- [29]. Furthermore, Kansei has been proven successful in measuring human emotions toward services and products such as in designs of eyewear, popup box and e-commerce websites desired by users [30], [31].…”

Section: Kansei Methodologymentioning

confidence: 99%

Emotional Evocative User Interface Design for Lifestyle Intervention in Non-communicable Diseases using Kansei

Razali¹,

Noor²,

Zainudin³

et al. 2021

IJACSA

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The advancement of technology has led to the development of an artificial intelligence-based healthcare-related application that can be easily accessed and used to assist people in lifestyle intervention for preventing the development of noncommunicable diseases (NCDs). Previous research suggested that users are demanding a more emotional evocative user interface design. However, most of the time, it has been ignored due to lack of a model that could be referred in developing emotional evocative user interface design. This creates a gap in the user interface design that could lead to the ineffectiveness of content delivery in the NCD domain. This paper aims to investigate emotion traits and their relationship with user interface design for lifestyle intervention. Kansei Engineering method was applied to determine the dimensions for constructing emotional evocative user interface design. Data analysis was done using SPSS statistic tool and the result showed the emotional concepts that are significant and impactful towards user interface design for lifestyle intervention in NCD domain. The outcome of this research shall create new research fields that incorporate multi research domain including user interface design and emotions.

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Section: Kansei Methodologymentioning

confidence: 99%

Emotional Evocative User Interface Design for Lifestyle Intervention in Non-communicable Diseases using Kansei

Razali¹,

Noor²,

Zainudin³

et al. 2021

IJACSA

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“… Among regression algorithms the usual choices are: linear regression [ 173 , 174 , 175 ], Lasso Regression [ 176 , 177 ], Logistic Regression [ 178 , 179 , 180 ], Multivariate Regression [ 181 , 182 ], and Multiple Regression Algorithm [ 183 , 184 ]. Among clustering algorithms the most common choices in biometrics or neuroscience research are: K-Means Clustering [ 185 , 186 , 187 ], Fuzzy C-means Algorithm [ 188 , 189 ], Expectation-Maximization (EM) Algorithm [ 190 ], and Hierarchical Clustering Algorithm [ 188 , 191 , 192 ]. Among reinforcement learning algorithms the most common choices are: deep reinforcement learning [ 193 , 194 , 195 ] and inverse reinforcement learning [ 196 ].…”

Section: Brain and Biometric Affect Sensorsmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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“…Most studies use a semantic differential scale or Likert scale to design questionnaires and obtain Kansei evaluations from survey results. Besides, a few scholars pay attention to physiological response [51,52]. They believe that the physiological signal is more reliable than the defined score.…”

Section: Kansei Engineeringmentioning

confidence: 99%

Big Data driven Product Design: A Survey

Quan¹,

Li²,

Zeng³

et al. 2021

Preprint

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With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements; product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design.Keywords product design • big data • Kansei engineering • QFD • generative design * Correspondence.

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Meta-KANSEI Modeling with Valence-Arousal fMRI Dataset of Brain

Cited by 23 publications

References 42 publications

Emotional Evocative User Interface Design for Lifestyle Intervention in Non-communicable Diseases using Kansei

Emotional Evocative User Interface Design for Lifestyle Intervention in Non-communicable Diseases using Kansei

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Big Data driven Product Design: A Survey

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