Refine Your Search

Search Results

Viewing 1 to 3 of 3
Technical Paper

Visualization and Classification of Strategy for Entering Car

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.
Technical Paper

Motion Distribution Map of Ingress to Driver's Seats

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.
Technical Paper

Relationship between KANSEI Words Describing the Human Body and Body Dimensions for Modeling Synthetic Actors

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.