  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
ex-posed 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.
