# Posture Recognition Using Ensemble Deep Models under Various Home Environments
## 4 Experimental Results
### Experimental Results
Figure 18 shows the confusion matrix for VGG19 and type 2 preprocessing, which exhibited the best performance for the posture data of 11 classes with data augmentation under transfer learning. Tables 8-10 present the accuracies of the CNNs for the posture data of 10, 5, and 4 classes, respectively, with data augmentation under transfer learning. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Single Model (\%)** & **Type 0** & **Type 1** & **Type 2** & **Type 3** & **Type 4** \\ \hline Raw & 46.40 & 69.94 & 74.51 & 70.31 & 74.99 \\ VGG16 & 69.71 & 77.73 & 85.25 & 82.17 & 83.32 \\ VGG19 & 70.71 & 78.64 & 86.34 & 82.63 & 84.51 \\ ResNet50 & 78.25 & 88.50 & 86.99 & 83.46 & 87.08 \\ DenseNet201 & 75.36 & 82.73 & 85.59 & 82.44 & 83.35 \\ InceptionResNetV2 & 64.67 & 77.96 & 79.46 & 75.49 & 83.11 \\ Xception & 57.41 & 72.38 & 76.00 & 67.58 & 75.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance of the CNNs without data augmentation under transfer learning (4 classes). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Single Model (\%)** & **Type 0** & **Type 1** & **Type 2** & **Type 3** & **Type 4** \\ \hline \multirow{3}{*}{SimpleCNN} & Training & 69.89 & 66.35 & 65.54 & 62.11 & 67.34 \\  & Validation & 62.92 & 59.47 & 63.02 & 43.42 & 61.97 \\  & Testing & 42.70 & 52.63 & 64.35 & 50.00 & 60.15 \\ \hline \multirow{3}{*}{VGG16} & Training & 82.07 & 82.77 & 86.78 & 81.40 & 82.16 \\  & Validation & 73.73 & 73.02 & 76.40 & 72.37 & 74.29 \\  & Testing & 61.62 & 67.35 & 76.05 & 68.84 & 72.01 \\ \hline \multirow{3}{*}{VGG19} & Training & 82.69 & 83.98 & 85.57 & 82.80 & 83.27 \\  & Validation & 73.13 & 73.21 & 76.74 & 72.64 & 74.14 \\  & Testing & 62.86 & 67.39 & 76.54 & 69.71 & 73.67 \\ \hline \multirow{3}{*}{ResNet50} & Training & 58.33 & 67.89 & 57.76 & 66.10 & 62.82 \\  & Validation & 56.74 & 64.85 & 55.84 & 63.07 & 61.36 \\  & Testing & 53.43 & 60.67 & 49.63 & 63.04 & 54.15 \\ \hline \multirow{3}{*}{DenseNet201} & Training & 68.33 & 71.84 & 73.12 & 67.96 & 67.00 \\  & Validation & 66.35 & 17.27 & 70.03 & 18.34 & 63.61 \\  & Testing & 57.04 & 66.41 & 64.25 & 61.08 & 54.24 \\ \hline \multirow{3}{*}{InceptionResNetV2} & Training & 50.22 & 59.04 & 58.68 & 58.99 & 60.40 \\  & Validation & 48.10 & 15.05 & 56.78 & 15.15 & 58.42 \\  & Testing & 44.58 & 52.55 & 52.91 & 56.69 & 57.27 \\ \hline \multirow{3}{*}{Xception} & Training & 44.31 & 57.97 & 54.08 & 55.36 & 57.00 \\  & Validation & 42.85 & 15.09 & 09.51 & 14.30 & 55.22 \\ \cline{1-1}  & Testing & 35.15 & 52.54 & 45.63 & 53.53 & 50.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification performance of the CNNs with data augmentation under transfer learning (11 classes). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Single Model (\%)** & **Type 0** & **Type 1** & **Type 2** & **Type 3** & **Type 4** \\ \hline \hline \multirow{2}{*}{SimpleCNN} & 24.20 & 38.35 & 49.95 & 40.17 & 46.41 \\   VGG16 & 46.99 & 61.19 & 66.78 & 61.65 & 63.88 \\   VGG19 & 48.94 & 60.45 & 67.47 & 62.40 & 65.12 \\   ResNet50 & 48.85 & 64.00 & 58.69 & 58.22 & 59.42 \\ DenseNet201 & 54.66 & 63.75 & 64.24 & 59.67 & 60.11 \\ InceptionResNetV2 & 46.22 & 55.96 & 53.21 & 53.93 & 59.68 \\ Xception & 38.02 & 54.36 & 50.43 & 47.67 & 53.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification performance of the CNNs with data augmentation under transfer learning (10 classes). \begin{table}
\begin{tabular}{c c c c c c|c|c|c|c|c|c|c|c} \hline \hline
**Single Model (\%)** & **Type 0** & **Type 1** & **Type 2** & **Type 3** & **Type 4** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} Raw \\ VGG16 \\ \end{tabular} } & 51.43 & 60.43 & 78.71 & 61.96 & 73.37 \\   VGG16 & 72.37 & 78.44 & 87.52 & 82.19 & 83.76 \\   VGG19 & 74.44 & 78.19 & 87.92 & 82.49 & 84.83 \\ ResNet50 & 69.54 & 78.70 & 73.47 & 78.49 & 75.94 \\ DenseNet201 & 71.00 & 80.70 & 81.17 & 79.79 & 75.83 \\ InceptionResNetV2 & 61.67 & 71.71 & 73.74 & 72.34 & 77.13 \\ Xception & 52.50 & 68.65 & 66.38 & 66.55 & 68.93 \\ \hline \hline \end{tabular}
\end{table}