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This paper proposes a method for visualizing and classifying the variation in the motions of a person when entering a passenger vehicle. Entering behaviors vary greatly between individuals, especially if the vehicle door is designed to have large clearance. The present study was conducted with the aim of supporting the design process of seats and front doors by visualizing possible variations of entering motions using a motion database, rather than calculating a single representative movement. The motion database is consist of different motions caused by various seats, and the motions are classified by mapping them into two-dimensional plane according to the similarities between them. A representative entering motion for a clustered motion strategy group is synthesized and visualized on the 2D distribution plane by interpolating existing motions in the database.

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.