Abstract
Due to the complexity of the human body and its neuromuscular stabilization, it has been challenging to efficiently and accurately predict human motion and capture posture while being driven. Existing simple models of the seated human body are mostly two-dimensional and developed in the mid-sagittal plane exposed to in-plane excitation. Such models capture fore-aft and vertical motion but not the more complex 3D motions due to lateral loading. Advanced 3D full body active human models (AHMs), such as in MADYMO, can be used for comfort analysis and to investigate how vibrations influence the human body while being driven. However, such AHMs are very time-consuming due to their complexity. To effectively analyze motion comfort, a computationally efficient and accurate three dimensional (3D) human model, which runs faster than real time, is presented. The model's postural stabilization parameters are tuned using available 3D vibration data for head, trunk and pelvis translation and rotation. A comparison between AHM and EHM is conducted regarding human body kinematics. According to the results, the EHM model configuration with two neck joints, two torso bending joints, and a spinal compression joint accurately predicts body kinematics.
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We acknowledge the support of Toyota Motor Corporation.
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Desai, R., Cvetković, M., Wu, J., Papaioannou, G., Happee, R. (2023). Computationally Efficient Human Body Modelling for Real Time Motion Comfort Assessment. In: Scataglini, S., Harih, G., Saeys, W., Truijen, S. (eds) Advances in Digital Human Modeling . DHM 2023. Lecture Notes in Networks and Systems, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-031-37848-5_32
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DOI: https://doi.org/10.1007/978-3-031-37848-5_32
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