Recently, as a potential source of stationery products, bamboo has been continuously excavated for its commercial value in sustainable development. The research on the design of the bamboo pen holder, based on the concept of sustainable design, is not only a symbol of Chinese culture but also a reflection of perceptual factors underlying college students’ preferences. This study aims at user-centered product design by integrating Design Thinking (DT) and Kansei Engineering (KE). We proposed the KE–DT framework which allows designers to coordinate the user’s feelings as design elements. With various college student questionnaires, expert interviews, and cluster analysis, 7 Kansei words were selected to describe the sentimental value first. Using statistical analysis, the users’ emotional preference for each Kansei word was determined. Finally, the best shape of the pen holder was obtained by Sequential Quadratic Programming (SQP), and the stereoscopic product was displayed. A new round of questionnaire surveys was conducted with the optimized pen container and the previous group. The results indicated that curvature had a significant effect on the preference for bamboo stationery in terms of user and designer cognitive differences. Furthermore, the bamboo pen holder designed by the KE–DT framework proved widely popular.

Emotional recognition is a pivotal research domain in computer and cognitive science. Recent advancements have led to various emotion recognition methods, leveraging data from diverse sources like speech, facial expressions, electroencephalogram (EEG), electrocardiogram, and eye tracking (ET). This article introduces a novel emotion recognition framework, primarily targeting the analysis of users’ psychological reactions and stimuli. It is important to note that the stimuli eliciting emotional responses are as critical as the responses themselves. Hence, our approach synergizes stimulus data with physical and physiological signals, pioneering a multimodal method for emotional cognition. Our proposed framework unites stimulus source data with physiological signals, aiming to enhance the accuracy and robustness of emotion recognition through data integration. We initiated an emotional cognition experiment to gather EEG and ET data alongside recording emotional responses. Building on this, we developed the Emotion-Multimodal Fusion Neural Network (E-MFNN), optimized for multimodal data fusion to process both stimulus and physiological data. We conducted extensive comparisons between our framework’s outcomes and those from existing models, also assessing various algorithmic approaches within our framework. This comparison underscores our framework’s efficacy in multimodal emotion recognition. The source code is publicly available at https://figshare.com/s/8833d837871c78542b29.

This article, from the perspective of structural features, focuses on in-car user interface icons and explores the impact of different icon structural features on visual search efficiency. Initially, we categorised the icons into four groups based on structural features: individual structure icons (ISI), enclosed structure icons (ESI), horizontal structure icons (HSI) and vertical structure icons (VSI). Subsequently, we conducted a visual search experiment with structure as the sole variable, recording participants' behaviours and eye-tracking data. Finally, data analysis was conducted using methods including analysis of variance and logistic regression. The results indicate that differences in icon structural features significantly affect visual search efficiency, showcasing significant intergroup differences. HSI exhibit the highest visual search efficiency, while ESI show the lowest efficiency. ISI have shorter response times but the lowest matching accuracy. VSI only perform better than ESI. These findings hold significant implications for optimising icon design and enhancing visual search efficiency.

In the era of emotional consumption, it has become a hot topic that commodities meet consumers’ emotional needs. As a necessity of life, the car also needs to meet the needs of consumers. To achieve that consumers can purchase cars according to their emotional needs, we need to label cars with emotional words. The car’s appearance is the crucial medium of emotional information transmission, especially the car’s color is an essential emotional factor. As the first impression of products, color affects people’s emotional attitude. Therefore, introducing color features into the training process of sample marking is an excellent idea for intelligent labeling of a large number of product emotions. This paper proposes a semi‐supervised learning method, Color‐SSL, based on color data augmentation to realize the label of car emotion. Color‐SSL takes FlexMatch as the framework of a semi‐supervised learning model and augments data by extracting subject color. Compared with the baseline method, the accuracy of this method improved by 3.2%, 8.3%, 8.6%, and 1.4% with 10, 50, 100, and 200 training samples and 1000 test samples. The results show that Color‐SSL obtains the best emotion‐label result (94%). In addition, this study publishes pictures of emotional car datasets with high resolution, orthogonal perspective, and uniform background.

For the group's cognitive-kansei information, that is, the user's kansei expression of products, the existing research, and practical application are relatively limited. This paper combines big data with cognitive-kansei information acquisition in kansei engineering and extracts cognitive-kansei information from a large number of texts. This method can improve the efficiency of collecting cognitive-kansei information, can truly reflect the group's cognition, and has objectivity. Taking UAV as an example, the effectiveness of the method of group cognitive-kansei information acquisition based on big data is verified.