## 3 Model Development and Validation
### Establish the IPSO-SVR Prediction Model  
From the final 162 screened sets of data, 130 sets were selected as the training sample set, and the remaining 32 sets were used as the validation sample set. To quantify the accuracy of the prediction model, root mean square error (RMSE), mean absolute error \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Different Backrest Angles**} \\ \cline{2-5}
**Parameters** & \multicolumn{2}{c}{**100–110\({}^{\circ}\)**} & \multicolumn{2}{c}{**100–120\({}^{\circ}\)**} & \multicolumn{2}{c}{**110–120\({}^{\circ}\)**} \\ \cline{2-5}  & **Z** & **Sig** & **Z** & **Sig** & **Z** & **Sig** \\ \hline Back peak object pressure & \(-\)6.159 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** \\ Back peak contact pressure & \(-\)11.06 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** \\ Back contact area & \(-\)21.21 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** \\ Back contact pressure & \(-\)21.222 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** & \(-\)20.215 & \(<\)0.01 ** \\ Hip contact area & \(-\)21.181 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** \\ Hip strength & \(-\)7.675 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** & \(-\)21.222 & \(<\)0.01 ** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Post hoc two-by-two comparison of different backred angles at 68 cm interval. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Different Backrest Angles**} & \multicolumn{2}{c}{**Cardinal**} & \multicolumn{2}{c}{**Significance**} \\ \cline{2-5}
**Parameters** & \multicolumn{2}{c}{**100\({}^{\circ}\)**} & \multicolumn{2}{c}{**110\({}^{\circ}\)**} & \multicolumn{2}{c}{**120\({}^{\circ}\)**} & \multicolumn{2}{c}{**Values**} & \multicolumn{2}{c}{**(_p_)**} \\ \hline Back peak object pressure & 36.66 \(\pm\) 1.89 & 36.17 \(\pm\) 1.93 & 31.23 \(\pm\) 1.48 & 920.28 & \(<\)0.01 ** \\ Back peak & 38.82 \(\pm\) 2.01 & 37.75 \(\pm\) 1.52 & 32.31 \(\pm\) 1.52 & 924.753 & \(<\)0.01 ** \\ contact pressure & 392.11 \(\pm\) 8.76 & 410.83 \(\pm\) 11.04 & 479.41 \(\pm\) 13.29 & 1188.12 & \(<\)0.01 ** \\ Back contact & 14.58 \(\pm\) 0.47 & 13.85 \(\pm\) 0.29 & 13.53 \(\pm\) 0.21 & 1088.813 & \(<\)0.01 ** \\ Hip contact area & 745.07 \(\pm\) 10.23 & 730.21 \(\pm\) 6.32 & 679.49 \(\pm\) 8.32 & 1178.403 & \(<\)0.01 ** \\ Hip strength & 977.24 \(\pm\) 29.26 & 982.31 \(\pm\) 20.22 & 926.33 \(\pm\) 19.46 & 911.213 & \(<\)0.01 ** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pressure distribution at a spacing of 68 cm. (MAE), and goodness of fit (R2) were applied as evaluation metrics in this paper. Usually, RMSE can be used to measure the deviation between the test value and the true value, which is a common index of prediction accuracy; MAE can avoid the problem of errors cancelling each other, accurately reflecting the size of the actual prediction error; R2 can verify the extent to which the regression model fits the data and effectively prove the extent to which the validation set can be predicted by the training set. Nine variables, including gender, height, weight, back peak object pressure, back peak contact pressure, back-contact area, back-contact pressure, hip-contact area and hip strength, were set as the input layer, and the subjective evaluation score was used as the output layer.