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We evaluated the accuracy of two types of 3D body scanners and scan-derived measurements using three types of test objects: a gauge; an anthropomorphic dummy; and human subjects. To provide the background data to show the necessity of such evaluation, repeatability of landmarking by a measurer and the repeatability of the landmark locations determined manually using a mouse were evaluated. The results showed that (1) repeatability of scan-derived body dimensions were not always better than manual measurements obtained by traditional methods, (2) the errors in landmarking by a measurer were not always the main cause of measurement errors in scan-derived measurements, (3) accuracy of whole-body scanners was not uniform within the scanning volume, and (4) evaluation using a dummy could underestimate the measurement errors in the human scan-derived data. This protocol may be available for other scanner systems.

In order to adapt actual motion data of a person to a digital manikin, we developed a method of estimating the functional joint rotation centers for the whole manikin body. By using this method, the positions of joints in motion can be estimated if the length of each segment of the whole body remains constant. As a result, when the digital model of the whole body moves, the trajectory of the most distal end is more similar to that of an actual human than the trajectory of the whole body model estimated using a conventional method.

In this study, we developed a method for estimating an arrangement of functional joint rotation centers (FCRs) from a set of body dimensions. For this purpose, we measured body dimensions and positioning motions for 20 subjects, and calculated their FCRs. Using principal component analysis, information on the arrangement of the FCRs was represented by six principal components. Multiple regressions functions to estimate scores of these principal components from body dimensions were calculated. The arrangement of the FCRs could be reconstructed from principal component scores, which were estimated from body dimensions. The arrangement of the FCRs was estimated for members of boundary family of a digital manikin system.

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.

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.

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 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.