People need to have a comfortable experience in vehicles nowadays. However, they are continuously exposed to vibrations from the vehicle. Madymo active human models (AHM) can be used for comfort analysis and to learn how vibrations influence the human body in several aspects. However, existing AHM are very time-consuming due to their complexity, and the correspondence with human comfort data is only reasonable. In order to more effectively analyze motion comfort, a computationally efficient simplified human model (SHM) is developed and validated. The human body model has 36 degrees of freedom (DoF) considering the following segments: pelvis, two thighs, two lower legs, two feet, lower torso, upper torso and head. The model is validated in fore-aft, lateral and vertical vibrations. The model's postural stabilization parameters are tuned manually, by gradient search and grid search in sequence. Manually tuning gives a group of initial values of parameters for further optimization. According to the results, failure of the gradient search illustrates that this optimization problem is non-smooth. At the same time, grid search gives a relatively better result but also shows that the current cost function does not perfectly represent a good fit and needs improvement. A comparison between AHM and the SHM shows that the SHM has a similar or even better fit for most signals while being a largest factor 116 faster. A comparison of parameters between SHM and multibody human models in the literature shows that the structure of models affects the values of the same parameters. A comparison of different time steps illustrates that shorter time step does not necessarily give higher accuracy for this SHM. Suggestions such as adding muscles and changing body shapes are given for further improvement.