In recent years, many traditional techniques have been declining, raising the question of how to carry on traditional culture. As a way to bring traditional culture closer to people, Hiwadabuki (cypress bark roofing), a traditional Japanese technique, was adapted to create decorative panels featuring its distinctive pattern. The purpose of this study was to clarify the appropriate shape of the Hiwadabuki-patterned panel when used as an art panel. The experiment was conducted to compare preferences for Hiwadabuki-patterned panels with different aspect ratios. The results showed that the unique pattern of the panels did not have a significant influence on the results. Furthermore, it was demonstrated that multiple groups with different preferred aspect ratios exist. The relationship between these groups and gender or age groups was also discussed. While incorporating traditional patterns helps make traditional skills more accessible, the findings offer valuable insights into panel shape selection.

Studies on green fashion consumption have rarely examined the way design influences consumersʼ green attitudes. This study aims to fill that theoretical gap by exploring the effect of design typicality on consumersʼ Perceived Greenwashing (PGW). Personal relevance, such as Green Consumption Value (GCV) and Fashion Knowledge (FK), is also examined. Using ANOVA, we confirmed that design typicality and GCV positively influence PGW, but FK does not directly influence PGW. Instead, FK interacts with both design typicality and GCV to form a more complex relationship.

When people get lost, what kind of person do they want to ask for directions? Previous studies have clarified the relationship between facial features and the impressions they create. However, how person’s characteristics influence such impressions remains unclear. In this study, we conducted experiments using stimuli consisting of twelve 3D humanoid models that differed in gender, facial expression, and behavior, and experimental environment was created in a Virtual Reality (VR) space to investigate the characteristics that make a person want to ask for directions. The results indicate that female humanoid models with smiling and expressionless faces are more likely to be asked for directions than those with negative facial expressions, based on Friedman and Nemenyi tests. The findings of this study are intended to be applied to the design of tourism guide avatars in Japan.

This study introduces a planning support system for culinary walking tours focused on specific cuisine genres such as curry or ramen. Unlike conventional gourmet platforms where users explore menus after selecting a restaurant, the proposed system centers the planning process on individual dishes, recommending the next dish based on flavor similarity, diversity, and walking distance. It also enhances accessibility by automatically extracting dish names, descriptions, and images from unstructured review texts and images, allowing users to consider dishes even without structured menus. This dish-first, multi-restaurant approach reflects the exploratory and comparative nature of culinary walking tours. In a controlled user study, the proposed system tended to receive higher subjective ratings than Tabelog, which may indicate possible advantages in perceived diversity and enjoyment in this exploratory setting. This work contributes to the field of personalized food recommendation by modeling dish-level selection behavior and leveraging unstructured data to support distance-aware decision support during user-driven culinary exploration.

Recent advancements in three-dimensional (3D) garment simulation have improved virtual fitting accuracy, yet discrepancies between avatars and actual human bodies remain a challenge in comfort evaluation for different postures. This study examines the effect of avatar-human shape differences on perceived comfort and clothing pressure in 3D garment simulations. A 3D-scanned posable avatar was generated using body data for 10 adult females. Shape differences in standing (A-pose) and forward-reaching (F-pose) postures were analyzed through contour comparisons. Physical trials and simulations were conducted using a basic upper garment with sleeves. Clothing pressure was measured using air packs in actual fittings and pressure maps in simulations. Results showed that simulation effectively replicated comfort evaluation for the A-pose whereas there were differences due to the deformation of joints for the F-pose. Differences in perceived comfort were largely determined by fabric deformation due to avatar shape differences. These findings emphasize the need to improve avatar accuracy in joint regions to improve dynamic comfort assessment in 3D simulations.

This study examined how chord structure (triads vs. seventh) and musical mode (major vs. minor) influence emotional impressions and neural responses in listeners. Using two four-chord progressions that differed in harmonic ambiguity, participants rated their affective responses and underwent frontal EEG recording of alpha activity. Minor triads received the highest pleasantness ratings, whereas major seventh chords tended to receive lower ratings in the present context, and minor seventh chords did not elicit the enhanced emotional responses expected. EEG results showed that sevenths, especially major seventh chords, were associated with alpha suppression, suggesting increased cortical engagement or expectancy processing. This suppression may reflect attentional demands related to harmonic uncertainty. These findings support the idea that harmonic complexity interacts with tonal context to shape emotional perception and frontal alpha activity. Thus, the reduced pleasantness observed here should be interpreted as a context-dependent effect, particularly for the major seventh that deviated from the surrounding harmonic framework.

In this study, we identified four consumer lifestyle types based on psychometric assessments and examined how purchasing priorities and preferred wristwatch styles differed across these types. Using questionnaire data and image-based clustering of wristwatch designs, we found that lifestyle was associated with systematic differences in the relative importance of purchasing priorities, and that these priorities were, in turn, related to preferences for specific wristwatch clusters. Overall, the results suggest a hierarchical pattern from lifestyle (value level) to purchasing priorities (consequence level) and then to wristwatch clusters (attribute level), which may provide useful implications for marketers and designers seeking to align product design, sales approaches, and communication strategies with the values and preferences of distinct consumer segments.

This study proposes a regression-based emotion estimation method by mapping postural features onto Russell’s circumplex model using graph neural networks (GNNs). By extracting both geometric and structural features from skeletal coordinates and embedding them into a two-dimensional emotion space, we achieved visual and interpretable emotion representations. Numerical experiments on two public datasets (MASR and MEED) demonstrated that postural features, particularly those along the arousal axis, effectively reflect emotional states. Furthermore, mapping to a circular emotion space reveals that emotions like fear and sadness exhibit distinct postural patterns, whereas anger and happiness often overlap with surprise. These results suggest that our method enables a nuanced understanding of postural emotion expression and supports visual analysis of emotional similarities and distinctions.

From a design research perspective, affective engineering’s disciplinary scope remains ambiguous due to its interdisciplinary nature. To situate the Int. J. Affect. Eng. (IJAE) in the broader design research landscape, we analysed 229 IJAE papers within a corpus of 61,673 design research papers published in 38 Web of Science-indexed journals. We show that IJAE has the lowest citation impact in this set. We further reveal IJAE’s lowest country diversity and predominantly Japan-based authorship despite the international scope implied by the journal’s title. Using UMAP on title-and-abstract text embeddings, we show that IJAE’s topic distribution is comparatively broad and centred mainly on interaction design and ergonomics. Finally, we identify a compact Kansei-related cluster (“Kansei Island”). Overall, we identify IJAE as a Japan-led, low-impact, thematically omnivorous interaction design journal, anchored by a compact Kansei-related core.

This study proposes a facial expression generation method that comprehensively covers the emotional spectrum defined by the Russell Circumplex Model of Affect. We employ StyleGAN to synthesize face images tailored to specific ages and genders, and apply GANimation to generate 49 expressions per face, guided by a rule set that combines facial muscle movements-Action Units (AUs)-according to their patterns of co-activation and mutual exclusivity. Expressions were generated for four synthetic face models, and subjective impression ratings were collected via a Visual Analog Scale (VAS). Our method generated all 49 expressions without artifacts; however, impression ratings varied with the age and gender of the target face. Further analysis revealed that while some AUs consistently influenced impressions across models, others varied depending on the face. The proposed method enables flexible and culturally adaptive facial expression design, with potential applications in personalized affective computing and multicultural human-computer interface design.

As generative AI technologies such as large language models (LLMs) become more common in daily life, it is increasingly important to evaluate AI-generated content and human responses using real-world examples. Understanding the quality and psychological acceptance of such content is essential for the responsible use of these technologies. This study focused on endorsements for actual commercial books. We compared four generation processes: human-only, AI-only, AI-generated with human revision, and human-created with AI revision. We also examined how disclosing the generation process affected user evaluations. The results showed that AI-generated content enhanced purchase intention more than human-created content. However, when the content was identified as AI-generated, participants tended to show resistance. This resistance was reduced when humans were also involved in the generation process. These findings suggest that human-AI collaboration produces content broadly acceptable across diverse user groups and may serve as a standard approach for future content generation.

In recent years, it has become necessary to establish methods for quantitatively evaluating human Kansei and impressions to meet the needs of consumers. In this study, a method for quantitatively evaluating impressions of apparel products using online reviews is proposed. Four impression scales (“Dark-Bright,” “Tight-Loose,” “Formal-Casual,” and “Simple-Fancy”) were defined, and an impression dictionary was constructed that includes impression words containing degree adverbs, extreme impression words, and negation words. Validation using large-scale data confirmed that negation words accurately captured impression polarity, while degree adverbs and extreme impression words reflected impression intensity. Statistical analysis showed that scales related to physical characteristics (such as color and fit) were sensitive to modifiers, whereas the Simple-Fancy scale remained stable. In the future, model accuracy will be further validated and the applicability of the method to other fields will be explored.

This study examined how musical mode (major/minor) and tempo (fast/slow) influence emotional impressions and neural oscillations during music listening. Using a sister-piece paradigm, we generated closely matched variants from well-known classical pieces so that only mode and/or tempo differed. Twenty participants listened to 16 excerpts while EEG was recorded from eight scalp sites, then rated each excerpt on 23 adjectives. Factor analysis revealed three robust dimensions: Peaceful Comfort, Energetic Arousal, and Melancholic Darkness. Mode primarily modulated the first and third factors, tempo the second, with some interaction on the third. EEG analyses focused on theta-band (4-7 Hz) power, showing site-specific sensitivity to both variables, especially in frontal and temporal regions. Crucially, the sister-piece design isolated mode- and tempo-driven contributions to Peaceful Comfort and Energetic Arousal, while indicating that Melancholic Darkness needs an added parameter. With convergent EEG markers, this provides a scalable, reproducible framework for affective-sound research.