## 3 Measurement of State Data Based on Non-Contact Image Analysis
### Respiration Signal Acquisition
The higher the precision of the ROI detector, the higher the quality of the signal contained in the pixel, but it is susceptible to noise, making it difficult to detect ROI even with small movements other than breathing. On the other hand, the higher the recall of the ROI detector, the more robust the ROI can be detected, but the quality of the signal included in the ROI is degraded, and pixels other than the respiration may be included in the ROI. To detect an appropriate ROI that can be used for analysis, precision and reproducibility must also be considered, so some noise may be included in the ROI detection result. When noise pixels are included in the ROI, when clustering is performed in two clusters, noise is included in each cluster, making it difficult to obtain an appropriate respiration signal. Therefore, it is necessary to utilize additional information that can separate the noise from the respiratory information cluster. The phase of the signal is opposite when the movement caused by the same breath changes from light to dark and from dark to light. This means that the movement induced by breathing has a symmetry with respect to the origin. Figure 12: ROI extracted through learning-based ROI detection model (Figure 11) and signal types for each area. Therefore, if one performs clustering by adding the origin-symmetric data to the original data (Figure 14), one can obtain the result shown in Figure 15 by this symmetry. Analyzing the type of data included in the cluster, it is determined that the two clusters have symmetry when the same type of data is included in another cluster. Therefore, noise can be removed by using this symmetric data cluster for respiration signal estimation. The technology to classify breathing signals by utilizing the symmetry of the signal as a feature shows excellent performance when it is a stable breathing signal, but when noise occurs in the breathing signal itself, the symmetry is broken and the performance is degraded. In particular, when the object is moving, noise, not breathing information, can be easily included in the breathing pixel. In the case of movement, stable breathing information must be maintained for as long as the time window to restore symmetry and to obtain correct breathing information again. Whenever movement occurs, there may  
Figure 14: Clustering of breathing pixel and noise data and the data contained in each cluster. Figure 13: Respiration vector visualization using clustering algorithm (Hierarchical clustering shows the best in extracting refined respiration signals by reducing destructive noise). Figure 15: Clustering of respiratory pixel and noise data with origin symmetric data and data contained in each cluster. be a delay in which correct breathing measurements cannot be made for this reason. It is shown in Figure 16 that unlike the previous method, in which all information of a certain time window was used, it was possible to continuously measure breath without delay by using only the motion of the most recent frame. Since the parameters of the ROI detection model are adjusted to accommodate some noise in consideration of the reproducibility, respiration pixels can be detected robustly against noise caused by movement, etc. If the detected breathing pixels are tracked by optical flow, it is possible to quantify the movement of the pixels, and among them, the breathing information can be estimated through up-and-down motion information directly related to breathing. Unlike the previous method, in which all information of a certain time window was used, it is possible to continuously measure breath without delay by using only the motion of the most recent frame. Typical causes of changes in blood flow are heart rate and respiration. As shown in Figure 17, changes in chest pressure caused by breathing can cause changes in blood flow.