# Posture Recognition Using Ensemble Deep Models under Various Home Environments
## 4 Experimental Results
### Experimental Results

Table 9: Classification performance of the CNNs with data augmentation under transfer learning (5 classes). Figure 18: Confusion matrix for VGG19 with preprocessing of type 2. The CNNs were trained via nontransfer learning. VGG19, ResNet50, ResNet101, and InceptionResNetV2 were considered for nontransfer learning. The first three models were trained with a batch size of 60, three epochs, and the Adam optimizer [43]; the last model was trained with a batch size of 20, one epoch, and the Adam optimizer. The highest accuracies were 93.32% for InceptionResNetV2 and type 2 preprocessing with the posture data of 11 classes without data augmentation under nontransfer learning. Table 11 presents the classification performance for 11 classes without augmentation under nontransfer learning. Table 12 presents the training time of a single model for 11 classes without augmentation under nontransfer learning. Figure 19 shows the training process for InceptionResNetV2 and type 2 preprocessing, which exhibited the best performance for the posture data of 11 classes without data augmentation under nontransfer learning. Figure 20 shows the activations for InceptionResNetV2 and type 2 preprocessing. Figure 21 shows the activations of the last ReLU for ResNet50 and type 2 preprocessing. Figure 19: Training process for InceptionResNetV2 with preprocessing of type 2: (**a**) accuracy; (**b**) loss. Figure 20: Activations for InceptionResNetV2 with preprocessing of type 2, which exhibited the best performance for the posture data of 11 classes without data augmentation under nontransfer learning: (**a**) activations of first convolution; (**b**) activations of last convolution. The trained CNN models from Table 11 are combined using the majority of outputs and average of scores. First, the five CNNs trained with different input types are combined under VGG19, ResNet50, ResNet101, and InceptionResNetV2 as EV19 TNet, ER50 TNet, ER101 TNet, and EIR2 TNet, respectively. Table 13 describes the classification performance of ensemble deep models designed by input types. Secondly, the four CNNs trained with different models are combined under input types from 0 to 4 as ET0 MNet, ET1 MNet, ET2 MNet, ET3 MNet, and ET4 MNet, respectively. Table 14 indicates the classification performance of ensemble deep models designed by the pre-trained CNNs. Table 15 indicates the training time of the ensemble system for 11 classes without augmentation under nontransfer learning. In ensemble systems, the highest accuracy is 95.34% of InceptionResNetV2s using average of scores. Figure 24 visualizes the classification performance of ensemble deep models from the results listed in Tables 13 and 14. Here, 'BestSingle' is the best result of single models listed in Table 11. As shown in Figure 24, the ensemble deep model by InceptionResNetV2s with average method showed the best classification performance in comparison to other models. Figure 23: Comparison of the average total accuracies for the different models under transfer learning. Figure 22: Comparison of the average total accuracies for the different types of preprocessing under transfer learning.