# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 3 Materials and Methods
### Motion-Based Respiration Measurement Method
First, the model can be designed as a lighter network to reduce the amount of computation required and enable faster and more efficient training. Deep learning-based respiration estimation shows excellent performance in terms of accuracy, but high-end devices are es \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Kernel** & **Input** & **Output** & **Stride** & **Input Size** & **Output Size** \\ \hline Conv1 & \(3\times 3\) & 3 & 32 & 2 & \(200\times 200\) & \(100\times 100\) \\ Conv1-1 & \(3\times 3\) & 32 & 32 & 1 & \(100\times 100\) & \(100\times 100\) \\ Conv1-2 & \(3\times 3\) & 32 & 64 & 2 & \(100\times 100\) & \(50\times 50\) \\ Conv2 & \(3\times 3\) & 32 & 64 & 2 & \(100\times 100\) & \(50\times 50\) \\ Conv2-1 & \(3\times 3\) & 64 & 64 & 1 & \(50\times 50\) & \(50\times 50\) \\ Conv2-2 & \(3\times 3\) & 64 & 128 & 2 & \(50\times 50\) & \(50\times 50\) \\ Conv3 & \(3\times 3\) & 64 & 128 & 2 & \(50\times 50\) & \(25\times 25\) \\ Conv3-1 & \(3\times 3\) & 128 & 128 & 1 & \(25\times 25\) & \(25\times 25\) \\ Conv3-2 & \(3\times 3\) & 128 & 128 & 1 & \(25\times 25\) & \(25\times 25\) \\ Conv4-1 & \(3\times 3\) & 128 & 128 & 1 & \(25\times 25\) & \(25\times 25\) \\ Conv4-2 & \(3\times 3\) & 128 & 1 & 1 & \(25\times 25\) & \(25\times 25\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model parameter of the proposed motion-based respiration measurement CNN. Figure 5: Proposed CNN architecture for respiration measurement. sential when real-time processing is required, owing to the large number of computational resources required. However, the proposed method can perform real-time calculations even in edge devices, such as mobile or embedded systems. In addition, the receptive field is also correspondingly small, which enables efficient calculation with less data. The receptive field can be defined as the size of the space of input neurons that can affect a single output neuron, which is defined as the size of the input image that can affect the output value [77]. The size of the receptive field is proportional to the extent to which context information can be utilized; a larger receptive field is useful for solving complex problems, but can easily lose detailed spatial characteristics of an image [78, 79, 80]. As the model has more contextual information available, more data with guaranteed diversity are required to prevent overfitting. The proposed model can be trained effectively owing to its lightweight design. In addition, the small receptive field of the model and the fact that it outputs motion information on the receptive field, which is a partial region of the image, in the form of a two-dimensional image, allows the information to be estimated independently. As the model has more contextual information, a sufficient dataset with guaranteed diversity is required to prevent overfitting. Because the proposed method has a small receptive field, it can learn intensively from the texture information of a local area of an image; thus, efficient augmentation can be achieved even with relatively little data. Second, the existing methods use only movement in a predetermined specific direction as respiration information after calculating the motion information. However, because the proposed model calculates the amount of movement based on the change in the image without making a specific hypothesis on the breathing movement, the result can be derived by using the overall elements present in the image. In addition, this characteristic of the proposed method, which can handle more information, can also help to distinguish noise information included in the RoI.