We investigated the effects of the silhouette and design details on the perceived sense of high quality for pajamas. Pajama images with various dimensions and details were created through apparel simulation and evaluated for their sense of high quality using the semantic differential method on a seven-point scale. It was found that adding piping to pajamas led to a sense of high quality and the placement of a pocket affected the sense of high quality. In addition, samples with patterns having smaller body width, shoulder width, sleeve width, pant length, and hip circumference than the standard pattern with little wrinkling tended to be perceived as being of higher quality. The identified characteristics of high-quality pajama silhouettes and details provide valuable insights for manufacturers.

The objective of this study was to investigate the differences and similarities between human-perceived fabric stretchability and the mechanical property values of fabrics, considering weave, knitting, and other structural variations and materials. Therefore, we created an elasticity scale based on mechanical properties and calculated a numerical elasticity evaluation of fabrics as stretching sensation of fabric (SSF) by comparing the elasticity of several fabrics with different fibers, structures, and yarn thicknesses. The relationship between these factors and the mechanical properties influencing fabric stretchability, as well as the impact of fabric structure and material on perceived stretchability, were analyzed. Results indicated that sensory texture perception differed between machine measurements and human evaluations, depending on the weaving and knitting methods. Additionally, distinct trends were observed between woven and knitted fabrics based on fiber type, which significantly influenced stretchability assessments.

In this study, as part of the basic research to develop a package design evaluation support system, we aimed to clarify the effects of design elements such as pattern size, spacing, position, and color on consumers’ Kansei in transparent tomato packages. Using tomatoes, which are widely consumed in the market, as the target product, we created and presented packages in which the above design elements were quantitatively varied based on visual characteristics, as well as conducted visual Kansei evaluation experiments with five Kansei evaluation indices. The results revealed that changes in the pattern size and color had an impact on consumers’ Kansei. Furthermore, we found optimal design conditions for each evaluation.

Heart rate is a crucial indicator of physical health, measured manually or via photoplethysmography (PPG) devices. This study assesses the performance of Elliptic, Chebyshev1, and Butterworth filters in processing remote photoplethysmography (rPPG) signals, focusing on noise reduction and accuracy with varying skin tones. Data from 19 subjects aged 20 to 32 years, from diverse ethnic backgrounds, are collected using video recordings and fingertip PPG sensors. Video data are preprocessed to isolate the face region, and rPPG signals are extracted and post-processed for amplitude normalization and frequency filtering. Results indicate that the Elliptic filter achieves the lowest root mean square error (RMSE) of 1.751 and the highest signal-to-noise ratio (SNR) of 5.536. The Chebyshev1 filter has an RMSE of 1.768 and an SNR of 3.066, while the Butterworth filter shows the highest RMSE of 3.411 and an SNR of 1.699. Among rPPG methods, ICA and PCA offer the best accuracy.

This study explored the relationship between evaluations of a proactive attitude toward learning-Japan’s learning assessment criteria-and biometric data such as blink count, head sway, and electroencephalogram (EEG) signals. We previously developed a system that quantitatively evaluates self-study using biometric data and improved it by optimizing system threshold adjustments to accommodate different school environments and student groups. This study examined the application of EEG evaluation to different learning environments. Furthermore, the system evaluation thresholds were parameterized and multivariate optimization was used to enhance accuracy and reduce the user burden. Blink count-based evaluations during calculation tasks demonstrated potential for producing learning attitude assessments comparable to teacher judgments. Potential overfitting in noncalculation task evaluations and EEG assessments indicate the need for further verification with a participant sample larger than that considered in this study.