## 3 Results
### Correlation Analysis and Modeling  
The construction of the modified model needs to be based on real parameters. The PR, PV, and material KES data were then used for a bivariate analysis using SPSS 25. The critical value of the overall Pearson's correlation coefficient (two-tailed test) was \(r\geq 0.76\), with a significance level of 0.05%. Table 6 demonstrates the mean values of correlation coefficients in multiple tensile states of the material. For easy observation, only the average correlation coefficients with a high significance value (\(p\) value \(<0.05\)), such as the thickness characteristic TM, are shown in the table. As can be seen in Figure 4, in the rigid part, the KES test data for the T0 and TM variables had a significant positive correlation with PR of 5...20%, whereas in the flexible part, the thickness properties of the RC, T0, and TM variables of the materials have significant positive correlations with PV of 5...20% of the PV. Moreover, the elastic properties of the LT and EMT variables have significant negative correlations with pressure. Based on this relationship, the PV test values of flexible parts can be optimized. It is important to note that the correlation coefficient in Figure 4 represents only the correlation between the pressure at different material elongation values and the KES test data and does not represent a causal relationship. Therefore, more in-depth studies are needed to determine the significance and implications of these relationships. In addition, due to the testing limitations, these results are only applicable to specific fabric types and testing conditions. The thickness and elasticity of the elastic material have a great influence on the pressure, followed by tension; it can be judged that the analysis results of the PR, PV, and KES data are reasonable. The pressure data of key parts of the human body (within 20%) and the KES parameters were further analyzed, and the statistics are shown in Figure 5.  
Figure 4: Correlation between pressure under different stretching conditions and KES data. From the results in Figure 5, it can be seen that the correlation results between the PV and KES data are more significant. The correlation between the thickness characteristic of TM and PR and PV of several parts, such as ankles, waist, hips, and legs, was strong, with correlation coefficients ranging from 0.85 to 0.96. In addition, the correlation between the elastic properties of EMT and PR and PV of several parts, such as the thighs, hips, ankles, and waist, was strong, with correlation coefficients ranging from 0.85 to 0.91. The thickness and elastic properties of the materials are significantly correlated with the pressure values of multiple body parts, and the tensile and thickness properties of materials in the virtual system also have a greater influence on the pressure values. The strong correlation between the PV and KES data can be used to analyze the pressure value of the flexible parts. Six groups of pressure values that need to be corrected are marked in Figure 6, and there are significant correlations between the six groups of PR and the KES parameters, TM, T0, and LT, etc., in the state of tensile change. Therefore, the model can be constructed based on the real KES data correction. The regression analysis between the highly correlated variables was carried out using SPSS, and the prediction calculation model of PR (elongation \(\leq\) 20%) was obtained. \[\widehat{PR}\ =\ 1.118/\mathrm{LT}\ -\ 0.437 \tag{1}\]  
\[\widehat{PR}\ =\ 1.311\ \mathrm{T0}\ -\ 0.494 \tag{2}\]  
\[\widehat{PR}\ =0.916-\ 2.801\ \mathrm{T0}^{2}\ +\ 2.677\ \mathrm{T0}^{3} \tag{3}\]  
\[\widehat{PR}\ =0.303\ \mathrm{HGS}^{0.608} \tag{4}\]  
\[\widehat{PR}\ =e^{(-0.972-0.262/\mathrm{HGS})} \tag{5}\]  
\[\widehat{PR}\ =\ 0.441\ \mathrm{G}^{0.485} \tag{6}\]  
Figure 5: Correlation between average pressure and KES data.