## 3 Results
### Machine Learning Analysis for Pressure Estimation
#### 3.2.1 Pressure Estimation at Individual Anatomical Points
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** & **RMSLE** & **MAPE** \\ \hline Extra Trees Regr. & 42.6667 & 2665.684 & 48.1738 & \(-0.8203\) & 0.2074 & 0.1917 \\ Random Forest Regr. & 45.6522 & 2938.924 & 50.5072 & \(-1.0423\) & 0.218 & 0.2057 \\ AdaBoost Regr. & 45.5731 & 2933.01 & 51.091 & \(-1.1171\) & 0.2212 & 0.2052 \\ Extreme Gradient Boost. & 50.0482 & 3324.043 & 54.9778 & \(-1.3777\) & 0.2399 & 0.2268 \\ Bayesian Ridge & 45.1373 & 2821.918 & 50.9436 & \(-1.5651\) & 0.2218 & 0.2073 \\ Dummy Regressor & 45.1358 & 2821.794 & 50.9427 & \(-1.5652\) & 0.2218 & 0.2073 \\ Light Gradient Boost.Mach. & 45.1358 & 2821.794 & 50.9427 & \(-1.5652\) & 0.2218 & 0.2073 \\ Orthogonal Matching Purs. & 46.3616 & 2878.473 & 51.2707 & \(-1.6113\) & 0.2229 & 0.2115 \\ Gradient Boosting Regr. & 46.6507 & 3403.991 & 54.3723 & \(-1.6691\) & 0.2319 & 0.2048 \\ K Neighbors Regr. & 49.9279 & 3561.278 & 56.7779 & \(-1.8124\) & 0.2451 & 0.231 \\ Elastic Net & 46.6024 & 3122.97 & 53.1349 & \(-1.8358\) & 0.2296 & 0.2069 \\ Lasso Least Angle Regr. & 51.2852 & 4159.062 & 59.577 & \(-2.2904\) & 0.2611 & 0.2229 \\ Lasso Regression & 51.3096 & 4161.013 & 59.5913 & \(-2.2912\) & 0.2612 & 0.223 \\ Ridge Regression & 55.8088 & 5147.188 & 65.1659 & \(-2.866\) & 0.3025 & 0.2412 \\ Huber Regressor & 62.6196 & 8839.295 & 77.4237 & \(-5.3749\) & 0.2903 & 0.2608 \\ Decision Tree Regr. & 67.4958 & 6356.389 & 77.734 & \(-5.5824\) & 0.3223 & 0.2948 \\ Passive Aggressive Regr. & 75.5966 & 8443.785 & 86.2818 & \(-7.3284\) & 0.3946 & 0.3159 \\ Linear Regression & 100.326 & 20,864.63 & 119.8718 & \(-22.0593\) & 0.435 & 0.4494 \\ Least Angle Regr. & 1385.571 & 7,262,251 & 1595.164 & \(-5175.72\) & 1.3363 & 6.3554 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression model metrics: heel peak plantar pressure. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** & **RMSLE** & **MAPE** \\ \hline Light Gradient Boost. Mach. & 59.1336 & 5961.295 & 69.5438 & \(-0.7524\) & 0.3315 & 0.307 \\ Dummy Regressor & 59.1336 & 5961.295 & 69.5438 & \(-0.7524\) & 0.3315 & 0.307 \\ Bayesian Ridge & 59.1476 & 5962.777 & 69.5575 & \(-0.754\) & 0.3316 & 0.3071 \\ Elastic Net & 65.731 & 7520.082 & 77.2534 & \(-1.6133\) & 0.3521 & 0.327 \\ Orthogonal Matching Purs. & 64.355 & 6624.744 & 74.2898 & \(-1.8195\) & 0.349 & 0.3254 \\ Extra Trees Regressor & 59.0005 & 5953.229 & 68.2479 & \(-2.8548\) & 0.3307 & 0.3114 \\ AdaBoost Regressor & 61.2344 & 6188.385 & 72.3118 & \(-3.0504\) & 0.3522 & 0.3163 \\ Random Forest Regressor & 65.8635 & 7280.352 & 78.2418 & \(-3.33\) & 0.3709 & 0.3446 \\ Ridge Regression & 70.6273 & 9422.834 & 84.8931 & \(-3.4673\) & 0.3803 & 0.3492 \\ Lasso Least Angle Regr. & 70.6124 & 9466.471 & 84.8651 & \(-3.5245\) & 0.3794 & 0.3512 \\ Lasso Regression & 70.8137 & 9498.548 & 85.0506 & \(-3.542\) & 0.3805 & 0.3518 \\ Gradient Boosting Regr. & 68.614 & 8445.285 & 84.826 & \(-4.0885\) & 0.4049 & 0.367 \\ K Neighbors Regressor & 74.8539 & 8246.182 & 87.825 & \(-7.5013\) & 0.4041 & 0.3986 \\ Huber Regressor & 86.2494 & 12,962.42 & 104.4957 & \(-8.203\) & 0.4775 & 0.4194 \\ Passive Aggressive Regr. & 97.7131 & 14,739.44 & 107.2971 & \(-10.8507\) & 0.4947 & 0.4661 \\ Extreme Gradient Boosting & 73.9059 & 9137.847 & 87.0243 & \(-12.7287\) & 0.3982 & 0.3953 \\ Decision Tree Regr. & 91.1842 & 14,836.03 & 109.1365 & \(-14.7182\) & 0.509 & 0.4827 \\ Linear Regression & 123.951 & 31,724.05 & 157.4115 & \(-16.7257\) & 1.1418 & 0.5985 \\ Least Angle Regr. & 1358.865 & 495,2249 & 1583.866 & \(-1376.8\) & 1.5223 & 6.971 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Regression model metrics: 3 rd metatarsal average peak plantar pressure.