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.

翻译：由于人体及其神经肌肉稳定系统的复杂性，在运动过程中高效准确地预测人体运动并捕捉姿态一直具有挑战性。现有坐姿人体简化模型多为二维模型，且沿正中矢状面构建，仅能承受该平面内的激励。此类模型可捕捉前后及垂直方向的运动，但无法模拟因侧向载荷引起的更复杂三维运动。先进的三维全身主动人体模型（AHM），如MADYMO中的模型，可用于舒适性分析并研究驾驶过程中振动对人体的影响。然而，此类AHM因其复杂性而计算耗时极长。为有效分析运动舒适性，本文提出一种计算高效且精确的三维人体模型（EHM），其运行速度超过实时要求。该模型的姿态稳定参数通过头部、躯干及骨盆的平移与旋转三维振动数据进行调校。基于人体运动学对比分析AHM与EHM的差异。结果表明，采用双颈部关节、双躯干弯曲关节及一个脊柱压缩关节配置的EHM模型能准确预测人体运动学。