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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.

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 influence of the head shape on intracranial responses under impact was investigated by using Finite Element Method. Head shape models of 52 young adult male Japanese were analyzed by Multi Dimensional Scaling (MDS), and a 2 dimensional distribution map of head shapes was obtained. Five finite element models of the Japanese head were constructed by a transformed finite element model of an average European adult male (H-Head model) using Free Form Deformation (FFD) technique. The constructed models represent the 5th and 95th percentile of the first 2 scales obtained by MDS. The same acceleration pulse was applied to the H-Head model and the five finite element models. The cause of the difference was considered to be differences in pressure distribution in the brain caused by the differences in the head shape. Variation in the head shape should be taken into account in simulating the effects of impact using a finite element model.

A protocol for validating anthropometric accuracy of computer manikins using a boundary family was proposed. Three commercial computer manikin systems were validated by this method, and errors were calculated for 4 dimensions (thumb tip reach, dactylion height, dactylion height, overhead and span) measured on 9 representative body forms. The validation protocol was applicable to all three systems. Results were reproducible, and the operation was not difficult. Whereas, definitions of measurements used to generate a body form in the software were unclear. The number of errors (race × representative body forms × measurements) to be evaluated may be large, but visualization and statistics can help in understanding the results.

An auto-landmarking method for foot measurement was developed. With most of the conventional methods, landmarks of the face and the body were estimated using the curvature, textures and statistics information of body dimensions. In this method, landmarks of the foot were estimated by deformation of the generic foot model. Variation of foot shape was represented by the spatial distortion based on the Free Form Deformation (FFD). The generic foot model was deformed and fit into a new measured foot. Landmarks on the deformed generic foot model were projected to the surface of the measured foot. The mean absolute error of the estimated landmarks was 2.8 [mm]. The error of this method is appropriate to distinguish foot shapes for selecting shoes on a retail store, however it is insufficient for anthropometric database.

A way to calculate representative forms from given set of forms was developed, in which surface data is modeled by polygons based on landmarks. Inter-individual distances are defined as distortions in FFD control points. By calculating inter-individual distances for all possible pairs of forms, a distribution of 3D forms in n-dimensional space is obtained using MDS. Each MDS dimension represents independent shape factors. Forms with specific MDS scores such as (0.5,0,0,0), (1,0,0,0) in standard deviation units are calculated as weighted averages of all actual forms. An FFD transformation grid is calculated that represents systematic form transformation along an MDS dimension. Forms with different scores for only the first MDS dimension and average scores (=0) for other MDS dimensions are calculated using these transformation grids.