# A Female Multi-Body Head-and-Neck Model and Validation for Rear Impact Simulations
## 6 Discussions
It is thought that there is no ideal average volunteer response to define because some parts of the volunteer response can be closer to the top limit, and some parts of the response can be closer to the bottom limit of the corridors. Therefore, it is believed by the authors that biofidelity evaluation should be mostly based on engineering judgment and expert opinion in the end [51; 52]. It is a well-known fact that it is quite difficult to successfully validate human-body models (even the most sophisticated ones) against volunteer responses, as documented in the references [46; 47; 48; 49; 50; 51; 52; 53]. Considering the published literature [53; 54; 55; 56; 57; 58; 59], the female head-neck model presented in this paper shows superior accuracy and highly biofidic responses. Moreover, since multi-body modeling technique is used, the model is computationally efficient. A typical laptop computer with 4 CPU requires only about 35 s to simulate a 250 ms motion of the developed head-neck model together with its animation. The model has the capability of mimicking or simulating active muscle response by utilizing damping functions, which can be easily tuned for varying degrees of muscle activation levels once relevant volunteer data is available. This modeling approach has been successfully applied in other studies by [PERSON] [3; 4]. Considering the time-varying damping functions which mimic muscle activity patterns, the model utilizes open-loop control in essence. It should be noted that the presented head-neck model is to be used for dynamic impact applications only. The rotational stiffness and rotational damping functions embedded into the intervertebral joints are calibrated through a process of fine-tuning during validation by running simulations in series. There are several constraints on building the model and calibrating the system as follows. Firstly, the model must be biofidelic, have correct anthropometry, correct range of motion, and posture; thus, it must mimic the biomechanical impact response of female volunteers. The torque-angle curves in the stiffness function must be monotonic such that it becomes viable to build a physical counterpart of the model in the form of a dummy. Furthermore, the damping functions must be constructed in such a way that they follow realistic EMG signal shapes to mimic muscle activity patterns. Hence, the calibration and validation of the model is not purely a mathematical approach; it requires a true interpretation of human dynamic response to impact. In tuning the model properties, the number of simulations to be run is restricted wisely for computational efficiency based on engineering judgment and experience of the authors in human motion simulation. A sensitivity analysis for case 3 is also performed to see the effects of the scale factors used in shaping the torque-angle curve in the stiffness function shown in Figure 5. The results of this sensitivity analysis are presented in Appendix A. The base value of the scale factor is determined as 0.65, which provided biofidelic behavior in the simulations. For scale factors in between 0.55 and 0.75, the model behavior is robust without showing significant differences in the responses in comparison to the responses with a scale factor of 0.65. When the scale factor is lowered to 0.40 and raised to 0.90, there are appreciable differences in the responses, as shown in Figure A1 in Appendix A. It can be seen that the scale factor used in shaping the torque-angle curves is not the main factor in the responses of the model because the torque-angle curves simulate the quasi-static response of the neck only without muscle activation. Therefore, muscle activation plays a predominant role in the impact response of the head-neck. Muscle activation, which is mimicked by the damping function, is the key to obtaining biofidelic behavior of the model and limit head-neck rotation.