## 3 Results
### Machine Learning Analysis for Pressure Estimation
#### 3.2.2 Analysis of Correlation Between Consolidated Temperature and Pressure Prediction
\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. & 37.8672 & 2304.44 & 46.2579 & \(-\)0.7488 & 0.6112 & 1.3117 \\ Dummy Regressor & 37.8672 & 2304.44 & 46.2579 & \(-\)0.7488 & 0.6112 & 1.3117 \\ Bayesian Ridge & 39.0378 & 2378.554 & 47.1246 & \(-\)0.818 & 0.6213 & 1.3153 \\ Passive Aggressive Regr. & 41.3224 & 2650.283 & 49.1166 & \(-\)0.8453 & 0.6669 & 1.369 \\ AdaBoost Regressor & 40.0842 & 2665.721 & 50.02 & \(-\)1.1158 & 0.6383 & 1.3454 \\ Random Forest Regr. & 40.9052 & 2627.747 & 49.4219 & \(-\)1.1796 & 0.6472 & 1.301 \\ K Neighbors Regr. & 43.6027 & 3003.919 & 52.6696 & \(-\)1.3401 & 0.6822 & 1.4252 \\ Gradient Boosting Regr. & 46.0786 & 3136.678 & 53.7797 & \(-\)1.5054 & 0.6705 & 1.2397 \\ Extra Trees Regr. & 42.7361 & 2879.329 & 52.3997 & \(-\)1.6931 & 0.6889 & 1.3874 \\ Elastic Net & 46.6453 & 3204.647 & 55.4159 & \(-\)1.7926 & 0.7383 & 1.5432 \\ Extreme Gradient Boost. & 50.896 & 3660.532 & 57.8543 & \(-\)1.9084 & 0.7317 & 1.4089 \\ Huber Regressor & 51.3562 & 3689.396 & 59.3965 & \(-\)2.5813 & 0.8133 & 1.5922 \\ Decision Tree Regr. & 60.6246 & 5272.278 & 69.8224 & \(-\)3.4066 & 0.8352 & 1.7657 \\ Lasso Regr. & 51.0932 & 4061.626 & 60.3932 & \(-\)3.4134 & 0.9889 & 1.5364 \\ Lasso Least Angle Regres. & 51.3896 & 4068.553 & 60.5001 & \(-\)3.6598 & 0.9164 & 1.5353 \\ Orthogonal Matching Purs. & 49.5623 & 4642.656 & 62.5531 & \(-\)3.8977 & 0.8912 & 1.5586 \\ Ridge Regression & 50.7713 & 4263.169 & 62.1715 & \(-\)4.1066 & 1.01 & 1.6337 \\ Linear Regression & 53.8113 & 4777.258 & 65.7032 & \(-\)4.9478 & 1.0016 & 1.6863 \\ Least Angle Regr. & 6.31 \(\times\) 10\({}^{35}\) & inf & inf & \(-\)inf & 23.5834 & \(6.13\times 10^{33}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regression model metrics: 5 th metatarsal average peak plantar pressure and temperature consolidated. \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. & 21.6342 & 781.5449 & 24.3457 & \(-\)4.1466 & 0.3499 & 0.3726 \\ Elastic Net & 23.205 & 975.5359 & 26.9775 & \(-\)4.509 & 0.3894 & 0.3974 \\ Extreme Gradient Boost. & 22.6037 & 879.9069 & 26.2291 & \(-\)4.8219 & 0.3769 & 0.3893 \\ Passive Aggressive Regr. & 17.9418 & 512.5657 & 20.8999 & \(-\)5.027 & 0.3086 & 0.2925 \\ Bayesian Ridge & 19.1977 & 610.182 & 22.0609 & \(-\)5.2521 & 0.326 & 0.3344 \\ AdaBoost Regressor & 21.5522 & 826.1589 & 24.9973 & \(-\)5.3733 & 0.358 & 0.3591 \\ Huber Regressor & 25.1347 & 1149.741 & 29.2435 & \(-\)6.1187 & 0.4382 & 0.4092 \\ Random Forest Regr. & 21.5135 & 782.1238 & 24.7137 & \(-\)6.4551 & 0.3547 & 0.3785 \\ Gradient Boosting Regr. & 23.9051 & 1007.286 & 27.9547 & \(-\)6.6773 & 0.3961 & 0.393 \\ Decision Tree Regr. & 26.5163 & 1247.107 & 31.3636 & \(-\)8.7018 & 0.455 & 0.4326 \\ Light Gradient Boost. Mch. & 19.7522 & 641.3545 & 23.1027 & \(-\)9.1444 & 0.3378 & 0.3345 \\ Dummy Regressor & 19.7522 & 641.3545 & 23.1027 & \(-\)9.1444 & 0.3378 & 0.3345 \\ Lasso Least Angle Regr. & 28.9892 & 1527.706 & 33.0902 & \(-\)11.1096 & 0.4667 & 0.4874 \\ Lasso Regression & 29.0708 & 1528.006 & 33.1449 & \(-\)11.2433 & 0.4685 & 0.4889 \\ K Neighbors Regr. & 23.5397 & 834.7291 & 27.003 & \(-\)11.4923 & 0.3894 & 0.4338 \\ Ridge Regression & 26.8591 & 1437.279 & 32.0659 & \(-\)12.3371 & 0.5478 & 0.4719 \\ Linear Regression & 28.1566 & 1517.806 & 33.3176 & \(-\)16.3879 & 0.6098 & 0.4869 \\ Orthogonal Matching Purs. & 29.692 & 1476.125 & 33.879 & \(-\)16.5291 & 0.6524 & 0.5192 \\ Least Angle Regr. & 1.36 \(\times\) 10\({}^{36}\) & inf & inf & \(-\)inf & 62.5706 & 1.69 \(\times\) 10\({}^{34}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Regression model metrics: lateral midfoot peak plantar pressure and temperature consolidated.