Takaya Yuizono

Crafts are products with both functional and aesthetic properties. Through emotional design, we can enhance not only the functionality of craft products, but also their aesthetic appeal. This study focused on ceramic teapots as a case study, selecting 20 samples from top-selling Chinese and Japanese ceramic teapots on JD.com. Employing machine learning methods, this study explored the relationship between the design features of ceramic teapots and the emotional preferences of young consumers. A classification model was developed and new teapots were designed for validation. The main findings are as follows: 1) The shape of the teapot lid, spout, handle, and body, as well as the color, decoration, and usability of the teapot, significantly influence young people’s emotional preferences. 2) Model evaluation revealed that the accuracies of the binary and ternary classification models established through random forest reached 91.0% and 81.3%, respectively. 3) The proportion of newly designed teapots that aligned with young people’s emotional preferences reached 91.1% (binary classification) and 64.5% (ternary classification), which is higher than the results of the original 20 teapots. In conclusion, this study demonstrates that the relationship between the perceived image and design features of top-selling Chinese and Japanese ceramic teapots can effectively guide the design of ceramic teapot forms and successfully convey the intended image. The established model can be used to evaluate the emotional quality of newly designed teapots, providing data-driven support for designers to create ceramic teapots that resonate with market preferences.

Learning and understanding of art are increasingly understood as dynamic processes in which emotion and cognition unfold over time. However, classroom-based evidence on how structured temporal intervals and guided prompts reshape students’ emotional experience remains limited. This study addresses these gaps by quantitatively examining changes in emotion over time in a higher education institution. Employing a comparative experimental design, third-year undergraduate art students participated in two structured courses, where emotional responses were captured using an emotion recognition approach (facial expression and self-reported text) during two sessions: initial impression and delayed impression (three days later). The findings reveal a high consistency in dominant facial expressions and substantial agreement in self-reported emotions across both settings. However, the delayed impression elicited greater emotional diversity and intensity, reflecting deeper cognitive engagement and emotional processing over time. These results reveal a longitudinal trajectory of emotion influenced by guided reflective re-view over time. Emotional dynamics extend medium theory by embedding temporal and affective dimensions into TBMA course settings. This study proposes an ethically grounded and technically feasible framework for emotion recognition that supports reflective learning rather than mere measurement. Together, these contributions redefine TBMA education as a temporal and emotional ecosystem and provide an empirical foundation for future research on how emotion fosters understanding, interest, and appreciation in higher media art education.

Urban green spaces (UGSs) play a vital role in promoting emotional well-being and supporting public health. Emotional responses to landscapes are closely linked to visual behavior, reflecting how individuals engage with environmental features. This study proposes and applies a multimodal sensing framework to examine how the types and proportions of landscape elements influence emotional and visual responses in immersive virtual environments. Sixty participants experienced 360° panoramic simulations of three UGS types—urban parks, community gardens, and ecological wetlands—featuring seven landscape elements: trees, shrubs, flowering trees, grass, herb plants, water landscapes, and artificial objects. Data were collected using electrodermal activity (EDA), pulse rate (PR), eye-tracking metrics, and self-reported emotional ratings. Visual behavior indices were analyzed in relation to landscape composition. Results show that both visual behavior and emotional responses were significantly influenced by landscape elements. Herb plants enhanced attention and positive emotions across compositions. Flowering trees exhibited a dual effect—eliciting physiological arousal while promoting relaxation. Water landscapes captured immediate attention and evoked positive emotional experiences, whereas artificial objects required greater visual effort but yielded limited emotional benefits. These findings provide actionable, data-driven insights for the design of emotionally supportive urban green spaces in high-density environments.

This study explored the relationship between surrounding avatars and time perception in a virtual reality (VR) gymnasium. Previous research has highlighted that motion speed and exercise intensity significantly influence time perception. In VR, time perception is shaped by various factors, such as an avatar’s embodiment at different levels. However, the specific effects of the surrounding avatar on time perception in a VR gymnasium context remain unclear. Thus, this study focuses on two key attributes of the surrounding avatar: (1) motion speeds and (2) exercise intensity. Participants in a VR gymnasium either rode a stationary bike or sat on one while observing avatars performing exercises in the virtual environment(VE). They were then asked to estimate the duration judgment and the feeling of the passage of time for each task. The results revealed that when the surrounding avatars exercised at a faster motion speed, participants perceived the duration of time as longer and felt that time passed more quickly. Additionally, high-intensity exercise led participants to perceive the passage of time as faster.