Abstract
For proper ergonomic evaluation using a digital human model simulation (DHMS) system such as RAMSIS®, postures of a humanoid for designated tasks need to be predicted accurately. The present study (1) evaluated the accuracy of driving postures of humanoids predicted by RAMSIS, (2) proposed methods to improve its accuracy, and (3) examined the effectiveness of the proposed methods. Driving postures of 12 participants in a seating buck were measured by a motion capture system and compared with those predicted by RAMSIS. Significant discrepancies (8.7° to 74.9°) between predicted and measured postures were observed for different body parts and driving tasks. Constraint addition and user-defined posture methods were proposed and their performance was assessed in terms of prediction accuracy. Of the two proposed methods, the user-defined posture method was found preferred by improving the accuracy of posture prediction by 11.5% to 84.9%. Both the posture prediction accuracy assessment protocol and user-defined posture method introduced in the study would be of use for practitioners to improve the accuracy of predicted postures of humanoids in virtual environments.
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