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Evaluation of Predicted Driving Postures in RAMSIS Digital Human Model Simulation  

Park, Jang-Woon (Division of Mechanical and Industrial Engineering, Pohang University of Science and Technology)
Jung, Ki-Hyo (Division of Mechanical and Industrial Engineering, Pohang University of Science and Technology)
Chang, Joon-Ho (Department of Industrial and Manufacturing Engineering, The Pennsylvania State University)
Kwon, Jeong-Ung (Corporate Research and Development Division HYUNDAI-KIA MOTORS)
You, Hee-Cheon (Division of Mechanical and Industrial Engineering, Pohang University of Science and Technology)
Publication Information
IE interfaces / v.23, no.2, 2010 , pp. 100-107 More about this Journal
Abstract
For proper ergonomic evaluation using a digital human model simulation (DHMS) system such as $RAMSIS^{(R)}$, the postures of humanoids 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 a method to improve its accuracy, and (3) examined the effectiveness of the proposed method. The driving postures of 12 participants in a seating buck were measured by a motion capture system and compared with their corresponding postures predicted by RAMSIS. Significant discrepancies ($8.7^{\circ}$ to $74.9^{\circ}$) between predicted and measured postures were observed for different body parts and driving tasks. Two methods (constraints addition and user-defined posture) were proposed and their effects on posture estimation accuracy were examined. Of the two proposed methods, the user-defined posture method was found preferred, reducing posture estimation errors by 11.5% to 84.9%. Both the posture prediction accuracy assessment protocol and user-defined posture method would be of use for practitioners to improve the accuracy of predicted postures of humanoids in virtual environments.
Keywords
digital human model simulation; driving posture prediction; posture prediction accuracy; user-defined posture; RAMSIS;
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