# Treadmill Deck Performance Optimization Design Based on Muscle Activity during Running
## 4 Discussion  
In order to evaluate the effectiveness of the objective optimization function, we will compare the simulation results with the experimental data of previous scholars. First, we will compare the simulated GRF calculated in this paper with the GRF values measured by [PERSON] et al. [17] in actual running and the results of [PERSON] and [PERSON]'s research [28]. In both the simulation results and the experimental results, there is a range of hardness (safe area) within which the GRF remains constant. Outside the safe area, the GRF will deviate from the default value. [PERSON] [8] and [PERSON] [31] have also observed that by varying the hardness of the sports surface or shoe, the ground reaction force does not change significantly within a certain range. Second, we compared the model predictions (Figure 3k,l) with [PERSON] and [PERSON]'s experimental observations of vibration levels [20]. We can see that consistent with experimental observations, the simulated vibration amplitude changes only insignificantly, even if the hardness of the sports surface changes considerably. Thirdly, we compared the simulated stiffness and damping coefficients of lower-limb soft tissue with the previous experimental results. Previous experimental tests by [PERSON] and [PERSON] [32] have shown that damping plays a greater role than stiffness in muscle tuning. According to these studies, the change of stiffness coefficient is much smaller than that of damping coefficient. We also observed the same trend in our simulation results (see Figure 3e,f). Compared with the results of the LNZN model, our model shows that the maximum impact peak value is about 1500 N, which is only slightly higher than the expected impact peak value. Our results show that the GRF peak value does not change much over the whole range of hardness parameters. It is also worth noting that our results show that the size of the safe area is significantly reduced after exercise fatigue. This suggests that the design of the active shock absorption system of the treadmill needs to consider this point. Especially for runners with exercise fatigue, our results suggest increasing the hardness of the treadmill deck a little to provide more impact energy recovery, thus providing better protection and comfort for runners. When compared to 3D human motion models [33], the model utilized here has certain advantages and some drawbacks. The 3D model contains critical information such as joint angle and joint torque, which are required for both GRF and fatigue simulation. However, in the 3D model, the human body is simplified as a system of rigid masses, disregarding the influence of wobbling masses and thus not accounting for the dynamic stiffness of muscles. In addition, both the 3D model and the previous MSD human motion model did not take into account the influence of treadmill deck parameters, which is a component of the model employed in our investigation. Also, the MSD modelling approach gives us better physical insight into the phenomena. The MSD model used in this paper is just a simplified model. The model mainly considers the vertical GRF of a standard human; however, the horizontal GRF may also have an important effect on human movement. Furthermore, within this model, the variation of stiffness and damping characteristics among individual muscles is overlooked, and the established safe area is derived from simulations. Each person's central nervous system may exhibit different behaviors. We must also recognize that human muscle movement is an extremely complex process that is influenced by many factors, such as physical activity level, age, gender, etc. These individualized factors can lead to differences in muscle activity between individuals, and it is clear that the models considered in this paper have not been able to incorporate these factors.