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This paper proposes a method for analyzing the ingress motions for different driver's seats. Because of the multiplicity of possible ingress strategies, a unique motion calculated by minimization of energy consumption is not sufficient for understanding the variety of motions. In addition, during ingress the human body is supported by the hand placed on the steering wheel, the legs move without collision with the side sill, and the head avoids the roof rail. Consequently, difficulty is expected in constructing a computational dynamics model for this complex motion that is sufficiently precise to predict human behavior. In order to understand ingress behavior without a detailed physical model of human motion, we utilize a motion distribution map based on the degree of similarity between motions.

The present paper deals with posture generation and its application to the selection of design alternatives using the DhaibaHand computational hand model. The operating postures used to push buttons are estimated for target points uniformly distributed on the button plane through the partial use of motion capture data. Two representative subjects (large and small) are used to examine the button disposition. The preferable area for button location is extracted by evaluating the generated postures in terms of reachability and posture margin.

The aim of this study was to develop a KANSEI model that estimates the impression ratings of various combinations of faces and spectacle frames. Such a KANSEI model can be used by a computer system at a retail shop to recommend a suitable frame, using physical characteristics of a consumer's face and spectacle frames. We selected 14 KANSEI words (e.g., “cheerful,” “intelligent") to describe the impression of each face/frame combination. We generated 96 CG images (8 representative face models × 12 frames). These images were shown to 75 female observers (students at a women's university), and impression ratings for 14 KANSEI words were obtained for each image using visual analog scales. Results of ANOVA indicated significant inter-face and inter-frame variance (p<0.01) for 11 KANSEI words. Thus, we have developed a computational model for estimation of impression ratings from physical characteristics of faces and frames.

In order to design a digital human model for computer graphics based on natural language, a mathematical model which estimates the scores of KANSEI words (overweight, thin, muscular etc.) from body dimensions was developed. Evaluation observers, consisting of 49 females and 64 males, watched image photographs of 24 young women and replied semantic scores of 21 KANSEI words for each photographed subject. The 24 photographed subjects were selected from over 200 subjects based on the somatotype. Analyzing the relationship between the scores and the 35 body dimensions of the photographed subjects, it was found that the sex differences of the observers were significant. Female observers evaluated body forms in greater detail and selected the dimensions to evaluate the scores of KANSEI words more carefully. Thus, we have developed a mathematical model, which estimates the scores of KANSEI words from body dimensions for the typical female observer.