Existing models of vibration transmission through the seated human body are primarily two-dimensional, focusing on the mid-sagittal plane and in-plane excitation. However, these models have limitations when the human body is subjected to vibrations in the mid-coronal plane. Three-dimensional (3D) human models have been primarily developed for impact analysis. Recently, we showed that such a 3D active human model can also predict vibration transmission. However, existing 3D body models suffer from excessive computational time requirements due to their complexity. To effectively analyze motion comfort, this research presents a 3D computationally efficient human model (EHM), running faster than real-time, with scope for real-time vehicle and seat motion control to enhance comfort. The EHM is developed by considering various combinations of body segments and joint degrees of freedom, interacting with multibody (MB) and finite element (FE) seat compliance models. Postural stabilization parameters are estimated using an optimization process based on experimental frequency-dependent gain responses for different postures (erect/slouched) and backrest support (low/high) conditions. The model combines two postural control mechanisms: 1) joint angle control capturing reflexive and intrinsic stabilization for each degree of freedom with PID controllers, including integration to eliminate drift, and 2) head-in-space control minimizing 3D head rotation. Interaction with a compliant seat was modeled using deformable finite elements and multibody contact models. Results showed the importance of modeling both compressive and shear deformation of the seat and the human body. Traditional stick-slip multibody contact failed to reproduce seat-to-human vibration transmission. Combining efficient body modeling principles, innovative postural adaptation techniques, and advanced seat contact strategies, this study lays a robust foundation for predicting and optimizing motion comfort.

Head motion is a key determinant of motion comfort and differs substantially from seat motion due to seat and body compliance and dynamic postural stabilization. This paper compares different human body model fidelities to transmit seat accelerations to the head for the assessment of motion comfort through simulations. Six-degree of freedom dynamics were analyzed using frequency response functions derived from an advanced human model (AHM), a computationally efficient human model (EHM) and experimental studies. Simulations of dynamic driving show that human models strongly affected the predicted ride comfort (increased up to a factor 3). Furthermore, they modestly affected sickness using the available filters from the literature and ISO-2631 (increased up to 30%), but more strongly affected sickness predicted by the subjective vertical conflict (SVC) model (increased up to 70%).

Active muscles are crucial for maintaining postural stability when seated in a moving vehicle. Advanced active 3D non-linear full body models have been developed for impact and comfort simulation, including large numbers of individual muscle elements, and detailed non-linear models of the joint structures. While such models have an apparent potential to provide insight into postural stabilization, they are computationally demanding, making them less practical in particular for driving comfort where long time periods are to be studied. In vibrational comfort and in general biomechanical research, linearized models are effectively used. This paper evaluates the effectiveness of simplified 3D full-body human models to capture comfort provoked by whole-body vibrations. An efficient seated human body model is developed and validated using experimental data. We evaluate the required complexity in terms of joints and degrees of freedom for the spine, and explore how well linear spring-damper models can approximate reflexive postural stabilization. Results indicate that linear stiffness and damping models can well capture the human response. However, the results are improved by adding proportional integral derivative (PID) and head-in-space (HIS) controllers to maintain the defined initial body posture. The integrator is shown to be essential to prevent drift from the defined posture. The joint angular relative displacement is used as the input reference to each PID controller. With this model, a faster than real-time solution is obtained when used with a simple seat model. The paper also discusses the advantages and disadvantages of various models and provides insight into which models are more appropriate for motion comfort analysis. For designers and researchers in the automotive and seating industries, the findings given in this paper provide useful insights that will help them improve the comfort and safety of both vehicle occupants and seats.

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.

In future automated vehicles we will often engage in non-driving tasks and will not watch the road. This will affect postural stabilization and may elicit discomfort or even motion sickness in dynamic driving. Future vehicles will accommodate this with properly designed seats and interiors, whereas comfortable vehicle motion will be achieved with smooth driving styles and well-designed (active) suspensions. To support research and development in dynamic comfort, this paper presents the validation of a multi-segment full-body human model, including visuo-vestibular and muscle spindle feedback, for postural stabilization. Dynamic driving is evaluated using a “sickening drive”, including a 0.2 Hz 4 m/s² slalom. Vibration transmission is evaluated with compliant automotive seats, applying 3D platform motion and evaluating 3D translation and rotation of pelvis, trunk and head. The model matches human motion in dynamic driving and reproduces fore–aft, lateral and vertical oscillations. Visuo-vestibular and muscle spindle feedback are shown to be essential, in particular, for head–neck stabilization. Active leg muscle control at the hips and knees is shown to be essential to stabilize the trunk in the high-amplitude slalom condition but not with low-amplitude horizontal vibrations. However, active leg muscle control can strongly affect 4–6 Hz vertical vibration transmission. Compared to the vibration tests, the dynamic driving tests show enlarged postural control gains to minimize trunk and head roll and pitch and to align head yaw with driving direction. Human modelling can enable the insights required to achieve breakthrough comfort enhancements, while enabling efficient developments for a wide range of driving conditions, body sizes and other factors. Hence, modelling human postural control can accelerate the innovation of seats and vehicle motion-control strategies for (automated) vehicles.