Today’s entrepreneurship education in Japan is mainly based on knowledge- and skill-oriented programs in the style of Silicon Valley in the United States. However, for university students belonging to Generation Z, the motivation for entrepreneurship tends to be not only wealth, but also altruistic “meaning” such as social contribution and connection with others. Self-awareness of the purpose and implications of entrepreneurship is necessary, but there are very few opportunities for university students to become self-aware in entrepreneurship education. However, Nonaka’s SECI model and Weick’s concept of Sensemaking are beginning to attract attention, and specific methodologies for self-awareness are desired. The author proposed a methodology to realize self-awareness from the viewpoint of Affective Engineering in the paper published in ISASE2020, and verified the effectiveness of affective sense of value extraction in the paper published in IJAE2020. In addition, the author proposed the rublic for self-assessment tool on “Sensemaking” by PBL participants in ISASE2021. In this paper, the author verified the effectiveness of the proposed rubric and sensemaking process in PBL.

This paper describes differences in product selection according to the location and situation of product use. Specifically, the authors examined the variation in earphone selection for each intended location and situation of use. Multiple comparison tests indicated that location had an effect on the types of earphones selected, depending on the situation of use. The similarity of the earphones selected for each location and situation was determined using quantification theory type III. The results showed that the similarity of the selected earphones differed according to both the location and situation. Moreover, the attributes that were important in the selection process were visualized using decision tree analysis in cases when similar products were selected. This work can contribute to providing decision support for product selection.

In this study, an unconstrained respiratory state classification system is proposed at each time step to detect the symptoms of sleep apnoea. An air mattress-type pressure sensor was developed to unconstrainedly measure the respiration signal during sleep. Based on the measurements, an algorithm that can classify respiratory states by applying a multidimensional probability distribution is proposed. Two types of validity experiments were conducted. In the first experiment, it was verified whether the respiration signal could be accurately measured by the developed pressure sensor. The results showed an average absolute error of 0.3 br/min. In the second experiment, the robustness of the classification accuracy to variations in the physical characteristics of the participants and recumbent positions was verified. The results showed an average F-value of 0.83 when extreme value distribution was applied. The classification accuracy of the proposed method outperformed the simple threshold method and the authors’ previous work.

The functional independence measure (FIM) is a metric of activities of daily living in stroke patients, but its accurate assessment requires experienced nurses. Therefore, we propose a system for estimating FIM scores easily at home based on angular velocities measured from specific bodily movements, performed only once, using gyro sensors. Here, we focus on the FIM motor score (mFIM) and determine whether “rising from a bed” or the “sit-to-stand movement” is the optimal specific bodily movement. We constructed a model considering the differences in convergence speeds between the movements of healthy people and those of stroke patients. We estimated the mFIM by performing regression analysis with the parameters utilized in the developed model as explanatory variables and mFIMs assessed by experienced nurses as objective variables. In our experiments, the mean absolute error of the angular velocity in the spine direction for rising from a bed was the smallest at 9.53.

Numerous studies in recent years have focused on the prediction of attractiveness using facial features. Researchers have reported high prediction accuracies using convolutional neural networks (CNNs). In this study, a model was built for predicting the attractiveness of faces using a CNN. In addition, the constructed model is used to visualize the features that underlie the prediction of attractiveness values using the gradient-weighted class activation mapping technique, and the trends are compared with a saliency map applying spectral residual and fine-grained methods. In addition, the relationship between image features that are important for attractiveness and facial features that affect human perception of attractiveness are discussed based on earlier findings in the field of psychology. The results support the psychological finding that sexual dimorphism influences the perception of facial attractiveness, a feature that is important for attractiveness prediction. The approach used here can help us understand the connection between facial features and the perception of attractiveness.

New lighting applications aspire to provide more benefits than simply allowing vision by improving mood, sleep quality, and daily performance. Based on the admission that luminaries appear differently when lit and non-lit, the study employed a dual-scale Kansei approach for the evaluation of wall sconces for creating of an intended lighting atmosphere in the dark while having a specific, invisible appeal at daylight. The study determined: (a) the relationship between the appeal of non-physical (emitted light pattern) and physical (shape and size) design attributes on the lit space when the sconce is switched on; (b) the effect of sconce shape and size on different interior impressions when lit and the sought out invisible impression when non-lit; and, (c) the personal lighting profile of one participant by the use of the selected set of sconces as a catalog. Factor analysis and partial least squares were applied for the statistical analysis.

Facial expressions convey emotions. However, not all people are good at understanding such expressions in daily communication. To address this issue, we herein use “SUGO-MIMI,” a lightweight device that does not require a power source to expand facial expressions. SUGO-MIMI expands eyebrow movements by connecting the eyebrows to thin plates, imitating cat ears attached to the headband with wires. As a result of analyzing the facial expressions and questionnaire responses of the participants who watched videos of a person wearing SUGO-MIMI, we observed that SUGO-MIMI can amplify some emotions that are thought to be desirable to express and that females tended to have more positive impressions of the SUGO-MIMI than males. Furthermore, in our experiment of conveying facial expressions, the emotion denoting happiness was conveyed better when wearing SUGO-MIMI.

The purpose of this study is to analyze the impression that a color image gives to a person and the color information contained in the image. In order to process a large amount of color images, we used a method of extracting representative colors from an image based on pixel information. In this study, we developed a method to combine image recognition technology using deep learning with representative color extraction technology, and analyzed the impression and color characteristics of design for store interior images. We collected color images of store interiors and keywords tagged with the images from web. Representative colors were extracted by a method using region division by deep learning. Furthermore, we quantized the characteristics of the representative colors included in the corresponding images. To examine those quantitative features of impressions, we conducted an experiment to compare subjective evaluation with calculated features and suggested that our calculated features reflect subjective evaluation.

This paper describes our educational project using remote collaboration related to the design and implementation of virtual Kawaii robots and spaces by Japanese and American university students. Because of the COVID-19 pandemic, we had to change our planned 7-week collaboration from in-person to virtual with a resultant change in the target product of our collaboration from real robots to virtual robots. Based on our new plan, students designed virtual spaces with robot pairs, proposed evaluation items for the robot pairs, evaluated their designs, and analyzed the results. The students designed each robot pair with the goal that one robot would be more kawaii and the other would be less kawaii due to a variation in a single attribute such as shape or color. The evaluation instrument used adjective pairs that were suitable to evaluate the affective values of the robot pairs and the virtual spaces the robots occupied. Through the design experience, students learned a lot about Kawaii Engineering and affective evaluation, which gave them a deeper understanding of Japanese culture from the viewpoint of Kansei/Affective Engineering. It is shown that understanding the role of “kawaii” for robotic gadget design is possible for students with various cultural background even under the virtual environment by employing our educational approach. This is a useful example of remote collaboration for education on Kansei Engineering. This paper is extended from the manuscript in the proceedings of ISASE 2021.