# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 3 Materials and Methods
### Motion-Based Respiration Measurement Method  
We consider two methods to improve the stability of existing respiration measurement methods. The first is to suppress noise generated by movement not caused by breathing in the RoI. In an uncontrolled environment, the RoI of the measurement subject may include objects with other movements in addition to the chest and abdomen. Because breathing movements are relatively large, the movements of these objects must be suppressed so that the noise does not affect the motion signal. However, distinguishing breathing from noise based on the calculated amount of movement is difficult because the quantitatively measured amount of movement varies depending on the distance between the measured object and the camera, the amount of breathing, and the type of clothing worn. The second method is to discover additional characteristics that can be used for motion analysis. Most existing methods for estimating respiratory information by quantitatively measuring movements consider only vertical movement as indicating breathing. However, because respiration is a movement that causes expansion and contraction of the chest and abdomen, it is accompanied by movement in the left and right directions as well as in the vertical direction. In some cases, the left and right movements may not be observed due to breathing movements, or they appear larger, so a more stable measurement may be possible when left and right movement characteristics are considered. Therefore, to improve on the stability of existing methods, we consider distinguishing motion noise from fine motion caused by breathing and use additional features to measure motion. In this study, we propose a learning-based CNN model that estimates motion through texture changes in the RoI. The structure of the proposed model is shown in Figure 5. The model extracts the features of an image through convolution (Conv1, Conv2, and Conv3 layers), which extracts the characteristics of the image by receiving RoI images of  
Figure 4: Example of chest RoI detection results based on body landmark point: (**a**) if only a portion of the chest area is in the image; (**b**) if all the chest area is in the image. two adjacent video frames as input and calculates the difference in the features to obtain the feature maps (feature maps 1, 2, and 3). Further convolution then extracts expressive features in estimating motion information from these feature maps containing difference information between the two images and outputs a two-dimensional output that expresses the degree of exhalation and inhalation movement as values between \(-1\) and \(1\). Because the output refers to the degree of motion of the two RoI image inputs, the average of the output is calculated to calculate the average degree of motion and is used as the average motion information. All convolutions, except convolution (Conv4-2), that output results include the ReLU activation function, and the Conv1, Conv2, and Conv3 layers include batch normalization. Although the model consists of convolution throughout the entire process, the size of the input image is fixed at 200 \(\times\) 200 pixels for computational efficiency and characteristic normalization. Details of the parameters of the model are summarized in Table 2.  
Because the initial layer of the CNN extracts low-level features, such as texture and edge, as the convolutions are repeated, the structural characteristics of the object are extracted. The model utilizes the complex structural characteristics of images [76]. However, the proposed method is not designed to use complex structural characteristics, but rather to estimate motion through changes in low-level features, such as texture. Therefore, relatively few layers and model parameters are needed. In addition, we adopt a shortcut structure so that the features extracted in the first layers can contribute to the last layer. This type of proposed model has several advantages.