# A Real-Time Remote Respiration Measurement Method with Improved Robustness Based on a CNN Model
## 3 Materials and Methods
### RoI Detection  
In the proposed approach, we adopt a method to detect the location of specific body parts (face, torso, etc.) within an image where changes caused by respiration can be observed in order to automatically detect RoIs. In this section, we discuss the existing methods and their limitations in detail, along with some directions to improve their performance. In this section, we describe the proposed methods to improve existing RoI detection techniques by focusing on stably detecting the chest region as the RoI, because movement caused by respiration is most visible in the rising and falling of the chest [72]. To perform stable detection, a distinct feature is needed to identify the difference between the object to be detected and other objects. Face detection is relatively easy owing to the biologically distinct characteristics of human facial features [73], but few studies considered this approach because the characteristics of the chest area can vary depending on the pattern of clothing worn. Previous studies attempted to detect RoI by simply assuming the area below the face to be the chest area after detecting the face, but this is highly likely to select an incorrect area owing to factors such as the subject's posture. Hence, estimating respiratory motion information properly is difficult if only a portion of the chest region is included in the RoI, and stable measurement may not be possible due to background noise. To overcome these limitations to achieve continuous and stable chest region detection, we adopt a technique to estimate the main landmark location of the human body to detect RoIs.  
Recently, technology to detect landmarks in the human body made significant progress [74]; in particular, Google announced BlazePose as a lightweight landmark detection technology that enables real-time operation even in mobile environments [75]. BlazePose detects the area of the face, which has the most distinctive characteristics in the human body, but little change in shape; then, it determines the area to detect landmark points based on the area of the face. Compared with existing body landmark detection technologies, BlazePose is designed to remove heatmap branches during inference and enables  
Figure 3: Overall process of the proposed method.  
points to be directly predicted. Therefore, our proposed approach exhibits extremely fast performance in landmark detection on single objects and achieves a computational speed that enables real-time computation even in mobile environments with limited computing power. In addition, the proposed approach shows robust performance even with some points missing, such as when only the upper body appears in an image, by adding visibility to the inference results of the model. Considering that no significant difference in accuracy is observed compared to conventional methods, our proposed method uses landmark points detected on the human body through BlazePose to estimate RoIs. Because the chest area can be defined as the area between both shoulders and between the shoulder and pelvis, the chest RoI for respiration measurements can be defined in terms of a bounding box determined by the shoulder and pelvic points, as shown in Figure 4.