# Posture Recognition Using Ensemble Deep Models under Various Home Environments
CNNs are designed by integrating a feature extractor and a classifier into a network to automatically train them through data and exhibit the optimal performance for image processing [18]. There are many pre-trained deep models based on the CNN, such as VGGNet [19], ResNet [20], DenseNet [21], InceptionResNet [22], and Xception [23]. These pre-trained models can be used for transfer learning. Transfer learning reuses the parameters of the models, which are trained from a large-scale database using high-performance processing units for a long time. Transfer learning can be effectively used in cases where a neural network needs to be trained with a small database and there is insufficient time to train the network with new data [24]. There are many studies on posture estimation based on deep learning. [PERSON] [25] used a CNN to extract several scale features of body parts. These features include a high-order spatial model sourced from a Markov random field and indicate the structural constraint of the domain between joints. [PERSON] [26] proposed a feedback algorithm employing a top-down strategy called iterative error feedback. It carries the learning hierarchical representation of the CNN from the input to the output with a self-modifying model. [PERSON] [27] concentrated on local information by reducing the receptive field size of the fast R-CNN (Region based CNN) [28]. Thus, partial detection was converted into multi-label classification and combined with DeepCut to perform bottom-up inference. [PERSON] [29] proposed residual learning [20] that includes more context by increasing the size of the receptive field and the depth of the network. [PERSON] [30] proposed a methodology for classifying poses from binary human silhouettes using a CNN, and the method was improved by image features based on modified Zernike moments for fisheye images. The training set is composed of a composite image created from a 3D human model using the calibration model of the fisheye camera. The test set is the actual image acquired by the fisheye camera [31]. To increase performance, many studies have used ensemble deep models at various applications. [PERSON] [32] designed an ensemble stacked auto-encoder based on sum and product for classifying horse gaits using wavelet packets from motion data of the rider. [PERSON] [33] studied an ensemble of convolutional neural networks trained with different activation functions using sum rule to improve the performance in small- or medium-sized biomedical datasets. [PERSON] [34] studied deep learning based on 1D ensemble networks using an electrocardiogram for user recognition. The ensemble network is composed of three CNN models with different parameters and their outputs are combined into single data. Traditionally, to recognize posture, it was necessary to obtain the coordinates of the body points or inertial data. This was achieved using a depth camera such as Kinect, image processing through a body model, or devices for capturing motion connected to the body; regarding the latter, it is a nuisance to wear these sensors with care in everyday life. Posture recognition, using images that do not require a sensor attached to the body, does not have this problem. Since posture recognition is performed using images, it can be applied to inexpensive cameras, and the device used for acquiring experimental data also has an inexpensive feature even though it supports a depth camera. In conventional machine learning, there is a limitation to recognizing posture directly using only an image [12; 35; 36; 37]. However, owing to advancements in deep learning, good performance in posture recognition can be achieved using only one image. In the present study, several pre-trained CNNs were employed for recognizing the posture of a person using various preprocessed 2D images. To train the deep neural network for posture recognition, a large number of posture images was required.