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This paper proposes a method for constructing a three-dimensional (3D) link model of a representative hand model, and to change its posture. The link data were derived using bone mesh models extracted from MR (magnetic resonance) images of 18 subjects. The length of each link was estimated by stepwise multiple regression analysis of 98 hand measurements. A 3D link for the representative hand model was generated by deforming the generic hand model using the estimated link length. The proposed method was validated by constructing models for six actual subjects. Link lengths and the distances between salient points were compared.

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 using a limited number of measured postures to estimate the hand posture necessary to push a button on an automobile steering wheel by the thumb tip. The estimation is divided into two processes. First, the approximate posture to push a button is calculated by interpolation using measured reference postures. Then, the posture estimate is optimized so that the thumb tip reaches a given goal as nearly as possible. The accuracy of the proposed method is validated by comparing the estimated postures with measured ones that are not used as reference postures.

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.

Recently, as CAD systems have spread, style design with digital mockups has been used. But physical mockups are still used to evaluate the ergonomic design of products. However, the conventional methods of the evaluation using a physical mockup of a product are inefficient in an upstream style design process, for it takes time and money. So, our research purpose is to develop a system for ergonomics design, which enables ergonomic assessment for a handheld information appliance without “real” subjects and physical mockups by integrating a digital hand with a product model and its task operation model.

We have developed a generic model of the human shoulder named “DhaibaShoulder.” In order to generalize the shoulder model formed by the orbital surface of the center of rotation of the shoulder, we clarified relationships between: the position of the center of rotation in the torso coordinates system, the shape of the orbital surface, and the torso anthropometric data. Using these results, our shoulder model can deform in accordance with anthropometric data and can determine the center of rotation in proportion to the posture of the upper arm. If the model is applied to a digital manikin, an actual reach envelope is simulated more accurately than is possible with other commercial digital manikins.

Dhaiba (Digital Human Aided Basic Assessment system) is a visualization software platform implemented on VirTools®. This software integrates a whole body model (DhaibaMan) with functional centers of rotation for 17 joints, a detailed hand model (DhaibaHand) with 31 Degree of Freedom (DoF) and databases of anthropometry and motions. The platform supports the design of human-compatible products such as cell phones, packages and cars. Models of human motions with strategy differences, subjective assessment of usability, deformation and friction of fingertips will be integrated.

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.

Characterization of the human hand motion necessary to manipulate hand-held equipment requires accurate capture and reconstruction of the arbitrary subject's hand motions with respect to the object. The present study used an individual link structure modeled from optical motion capture data to perform this task and compared the results against those obtained by medical imaging. Posture data was captured while grasping several cylinders, and the captured data were utilized to generate new postures to grasp cylinders of arbitrary diameter.

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.

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.

We developed a new method to estimate the functional center of rotation of the shoulder joint. Using this method, we examined the relationship between the posture of the upper arm and the displacements of the estimated center of rotation of the shoulder joint. The displacements were calculated from the flexion-extension angle and abduction-adduction angle of the upper arm. In this study, we propose a shoulder model for digital manikins that simulates reach envelopes more accurately than that for the existing digital manikins.

We propose some methods to automatically estimate the position of orbitale and tragion from raw 3D scanner data. The 3D scanner captures point cloud and mesh data representing the body surface. To utilize the data, we also need underlying anatomical landmarks. The tragion position is estimated using three methods, by detecting the saddle shaped area, by setting an average position relative to ear robe, and by 3D template matching. Orbitale position is estimated by regression analysis using the position of eyes and subnasale as independent variables. The methods are tested and the results are compared using 20 Japanese head form data.

We propose producing a body model consisting of anatomical landmarks used in product design from point clouds from a three-dimensional scanner. We previously proposed producing a foot model automatically by deforming the template of known landmarks using free-form deformation (FFD), but shapes of actual toe tips vary widely from the template. Here we propose extracting five landmarks on the toes of a foot model.

In order to create digital models representing the variation in hands of adult Japanese, 82 measurements were taken for 103 subjects using traditional methods. Factor analysis of the measurements was performed, and the first 2 factors were used to calculate measurements for 9 members of the boundary family. A 3D digital model was created for each family member by deforming a generic model by minimizing the total difference of 40 measurements between the generic model and the target hand model. The generic model with 36 degrees of freedom was created from medical images of a subject captured in 4 different posture.

Automatically generating humanlike grasp postures of the digital hand is a key issue for the virtual ergonomic assessment of the industrial products. In this paper we propose a new optimization-based approach for generating the realistic grasp posture. As an objective function, we use the number of the contact points, the fit of the specific part of the hand surface for the feature edges of the product surface and the margin for the constraints on the joint angle limits of the figures. The experimental studies on the grasp posture generation for the digital camera indicate that more realistic grasp posture could be generated using the proposed optimization-based method than the one using our former method.