## 4 Discussion
It should also be noted that shoes dedicated to people suffering from diabetes were worn during the measurements. Thus, the exact movements of the foot within the shoe could only be approximated. In the study by [PERSON] et al. [32], a 3D gait analysis was performed with the insoles worn in sandals so that the markers could be directly applied to the skin. However, in this case, the plantar pressure distributions in the diabetic shoe could not have been used to adjust the stiffness of the insole as recommended [13; 17]. To detect the timing of the early and mid-stance phases, the first high point and low point of the vertical ground reaction force were used. The early mean stance phase was 30.5% and the late mean stance phase was 51.2% of the stance phase. These values are consistent with those in the literature [62], demonstrating that the data of our FE simulation are realistic. When transferring the boundary conditions from the multibody simulation to the FE method, inaccuracies may have occurred; e.g., due to inaccuracies in the implementation of the heel coordinate system in the FE method. In order to minimize this in the future or to further reduce the workload, a coupling of the musculoskeletal multibody modelling with the FE method may be performed. This could also simplify dynamic FE analyses [77; 78; 79]. The validation showed that the percentage deviations between the experimental and simulated plantar peak pressure values are high, but the localization of the plantar pressure distribution is similar. However, the different resolutions between the experimental and simulated pressure measurement images must be taken into account. While Pedar pressure measurement insoles capture pressure within approximately 2 cm\({}^{2}\) areas, the FE method calculates nodal contact pressure [23; 35; 63; 66]. A crucial reason for the discrepancies may be due to the neglect of the relative motion of the bones. In addition, the damping property of joints and ligaments was excluded due to the fusion of all bones. Because of the discrepancies between the experimental and simulated plantar pressure distributions, the prediction of absolute changes in plantar pressure distribution due to a change in the stiffness of the insole using the FE model is not fully possible. However, it is assumed that a qualitative statement can be made about the influence of a change in the insole material on the plantar pressure distribution. Therefore, the computer-based method can be used to identify the location of high plantar pressure values in patients with diabetic foot, and it is assumed that a reduction in Young's modulus at these locations leads to a reduction in the plantar pressures using, accordingly, 3D-printed insoles. By means of a parameter analysis, the stiffness of the individual insole should be adjusted at local points where high plantar pressure values are present. This is because both experimental and numerical studies show a positive effect of local soft plugs on plantar pressure redistribution. Furthermore, [PERSON] and [PERSON] [80] found that placement of insoles after plantar pressures has a better effect on peak pressure reduction than placement after bony structures. In this work, the elastic modulus of 0.5 MPa, 1.0 MPa, and 1.5 MPa was investigated, which corresponds to a Shore A hardness of approximately 9, 20, and 29, respectively, according to the conversion formula of [PERSON] and [PERSON] [81]. Due to the high resolution of the simulated plantar pressure distribution, the corresponding local areas on the insole could be easily identified, partitioned, and materials assigned, which is another advantage of the FE method over manual stiffness adjustment using experimental data. The results of the FE method show that the greatest plantar peak pressure reduction can be achieved using the elastic modulus of 0.5 MPa. Here, percentage reductions between 16% and 26% were achieved in the different areas. [PERSON] et al.