# Posture Recognition Using Ensemble Deep Models under Various Home Environments
## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Single Model (\%)**} & \multicolumn{1}{c}{**Type 0**} & \multicolumn{1}{c}{**Type 1**} & \multicolumn{1}{c}{**Type 2**} & \multicolumn{1}{c}{**Type 3**} & \multicolumn{1}{c}{**Type 4**} \\ \hline \multirow{3}{*}{SimpleCNN} & Training & 91.50 & 70.82 & 81.12 & 85.42 & 68.87 \\  & Validation & 75.65 & 58.70 & 63.74 & 57.98 & 59.34 \\  & Testing & 45.80 & 61.03 & 67.18 & 60.85 & 63.48 \\ \hline \multirow{3}{*}{VGG16} & Training & 89.40 & 88.44 & 91.72 & 85.87 & 85.76 \\  & Validation & 74.50 & 74.62 & 76.92 & 72.65 & 74.59 \\  & Testing & 63.44 & 70.12 & 76.64 & 70.78 & 75.31 \\ \hline \multirow{3}{*}{VGG19} & Training & 90.09 & 91.17 & 89.56 & 83.06 & 86.18 \\  & Validation & 74.51 & 74.22 & 77.18 & 73.12 & 74.93 \\  & Testing & 64.87 & 69.82 & 78.35 & 71.46 & 74.94 \\ \hline \multirow{3}{*}{ResNet50} & Training & 60.03 & 71.56 & 60.67 & 67.48 & 68.05 \\  & Validation & 58.44 & 69.10 & 59.92 & 65.61 & 65.64 \\  & Testing & 55.60 & 66.83 & 57.92 & 65.55 & 61.81 \\ \hline \multirow{3}{*}{DenseNet201} & Train & 62.17 & 70.52 & 62.53 & 70.65 & 59.44 \\  & Validation & 60.68 & 67.56 & 61.27 & 68.44 & 58.34 \\  & Test & 54.51 & 64.16 & 55.88 & 66.51 & 50.68 \\ \hline \multirow{3}{*}{InceptionResNetV2} & Train & 44.98 & 55.63 & 55.10 & 59.68 & 56.01 \\  & Validation & 43.86 & 53.67 & 54.71 & 59.39 & 55.90 \\  & Test & 40.28 & 49.35 & 53.20 & 59.61 & 52.48 \\ \hline \multirow{3}{*}{Xception} & Train & 43.56 & 57.12 & 51.18 & 57.21 & 56.52 \\  & Validation & 42.34 & 54.73 & 49.62 & 55.98 & 54.15 \\ \cline{1-1}  & Test & 34.74 & 50.03 & 46.09 & 54.66 & 52.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification performance of the CNNs without data augmentation under transfer learning (11 classes). Figure 16: Structure of the simple CNN. \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 SimpleCNN & 25.36 & 48.16 & 53.88 & 51.63 & 51.54 \\ VGG16 & 48.95 & 62.63 & 67.34 & 63.65 & 66.79 \\ VGG19 & 51.18 & 61.94 & 69.89 & 64.71 & 66.95 \\ ResNet50 & 53.12 & 66.30 & 62.76 & 60.36 & 64.90 \\ DenseNet201 & 55.01 & 62.59 & 61.03 & 61.37 & 57.10 \\ InceptionResNetV2 & 43.79 & 56.24 & 55.60 & 54.68 & 58.26 \\ Xception & 36.16 & 52.27 & 53.69 & 50.03 & 55.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance of the CNNs without data augmentation under transfer learning (10 classes). \begin{table}
\begin{tabular}{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 Raw & 56.29 & 74.40 & 79.53 & 72.92 & 78.46 \\ VGG16 & 75.52 & 79.82 & 88.05 & 83.36 & 85.84 \\ VGG19 & 76.23 & 80.67 & 88.86 & 83.67 & 86.47 \\ ResNet50 & 74.91 & 84.35 & 78.73 & 82.39 & 80.43 \\ DenseNet201 & 70.87 & 79.73 & 77.08 & 81.73 & 73.60 \\ InceptionResNetV2 & 57.96 & 68.76 & 72.99 & 74.71 & 74.08 \\ Xception & 52.08 & 66.72 & 66.19 & 67.19 & 69.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance of the CNNs without data augmentation under transfer learning (5 classes). Figure 17: Confusion matrix for VGG19 with preprocessing of type 2 without data augmentation. Next, the experiment was performed in the same way, but data augmentation was added for the training. The data were augmented with a rotation range of 10, a shear range of 10, a zoom range of 0.2, and a horizontal flip of \"true\". The highest accuracies with data augmentation under transfer learning were 76.54% for VGG19 and type 2 preprocessing with the posture data of 11 classes, 67.47% for VGG19 and type 2 preprocessing with the posture data of 10 classes, 87.92% for VGG19 and type 2 preprocessing with the posture data of five classes, and 85.44% for DenseNet201 and type 2 preprocessing with the posture data of four classes. Table 7 presents the classification performance of the CNNs with data augmentation under transfer learning.