# Prediction of Health Problems Using Deep Learning Images and Bio-Signals
## 4 Experimentation and Discussion  
By means of the model with a weight adjusted through dataset learning, as stated in 2.1 above, images were acquired in real time from a thermal image camera and classified to identify individuals with fever. In order to analyze the HRV, UBioMacpa developed byBiosense Creative Co., Ltd. (Seoul in Republic of Korea) were utilized. Ten individuals were divided into groups with or without fever symptoms for the experiment. In Table 1 shown below, Table 1 represents the control group, which is the normal body temperature group; Table 1 represents the experimental group identified as having fever symptoms; and Figure 5 displays the results. HRV parameters--pulse variability, mean beat per min (meanBPM), SDNN, and RMSSD--were compared to analyze correlations. Table 1 displays the measured values of ten control subjects and ten experimental subjects, for a total of twenty subjects. On average, the pulse variability of the control group was 37.65, mean beat per min was 75.26, SDNN was 64.83, and RMSS was 42.24. The pulse variability value of the experimental group was 31.91, mean beat per min was 91.00, SDNN was 42.34, and RMSSD was 25.32. As shown in Table 1 and Figure 5, the experimental group showed a lower level of pulse variability, SDNN, and RMSSD than the control group. Only the mean beat per min was higher than that of the latter. Figure 5: Bio-signal measurement result graph. (**a**) Control group (normal body temperature); (**b**) experimental group (fever body temperature). Figure 6 shows the heart beat distributions and health conditions of the control group and experimental group. Figure 6a shows the control group, and Figure 6b shows the experimental group. As shown in these figures, the control group showed a distinctive triangular form of heart beat distribution, which indicates a good health condition. In contrast, the experimental group's heart beat distribution is not of a clear triangular form, which indicates that stress was accumulated and the objects were not in a good health condition. The left side of the graph indicates the state of stress and irregular pulses in reference to the balance of autonomous nerves. The control group is in the range of normal health conditions, while the experimental group shows symptoms of chronic stress and the initial stage of irregular pulses. The green table on the right of Figure 6 is a table showing the autonomic nerve balance. The image was extracted using the left heart rate distribution graph in Figure 6 and the mean BPM, SDNN, and RMSSD in Table 1. According to the positions of the green square pointer in Figure 6a,b, the stress and health status of the subject can be checked. A small green square pointer indicates stability when it is centered on the red bounding box. The green square pointer is based on the red bounding box, and the closer it is to the upper left, the more the parasympathetic nerve is activated, and the lower the right, the sympathetic nerve is activated. Hyperactivity of the parasympathetic nerve can be predicted to show signs of health abnormalities such as arrhythmia, as well as psychologically complex states such as anxiety and fear. Conversely, the activation of the sympathetic nerve can be predicted as a cause of chronic stress and disease, accompanied by a state of physical complexity as the state of tension and excitement of the body continues to be maintained. It was confirmed that the experimental group in Figure 6b was generally located in arrhythmia and acute stress rather than chronic stress. On the heart rate distribution graph, it was confirmed that the control group had a long bar graph and was centralized, while the experimental group had a short bar graph and was dispersed.