# Camera-Based Vital Sign Estimation Techniques and Mobile App Development
## 3 Results
### SpO2 Detection Results  
The absorptivity (Ka) at the two wavelengths used to obtain the SpO2 is shown in Figure 12. Most of the absorptance values were approximately 1, and the change in the absorptivity (green line) of G for B was the least significant. In the experiment, we assumed that the change in the absorption rate would be slight because it was being measured for normal people with insignificant changes in their SpO2. Therefore, to measure the SpO2 more accurately, the error should be reduced by using the absorptivity of G with respect to B. For the image sequence used in the experiment, Figure 13 shows the SpO2 detection results based on the absorption rate for the two wavelengths, as well as the constants A = 125 and B = 29. These constants are related to the absorption rate from the SpO2 calculation expressed by Equation (5). The distribution of the SpO2 detection results based on the absorption of each wavelength is shown in Figure 14, and the estimate using the absorption rate of G for B was similar to the actual measured value. Figure 15 shows the MSE of the measured SpO2 and the SpO2 estimated by using the absorptivity at two wavelengths for 97 image sequences to obtain the absorptivity constants A and B. As shown in the figure, when A = 104 and B = 8, the MSE for the absorption rate of G for B was the lowest. Because the noncontact detection value was similar to the actual \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**iPPG Extraction** & **MAE BPM** & **RMSE BPM** & **PE\({}_{3,5}\)**, \%** & **SNR, dB** \\ \hline G [5] & 4.89 & 6.93 & 55 & \(-\)2.89 \\ GRD [5] & 3.37 & 4.83 & 69 & \(-\)0.73 \\ AGRD [6] & 3.89 & 5.16 & 63 & \(-\)1.09 \\ ICA [3] & 3.12 & 4.67 & 67 & \(-\)0.52 \\ CHROM [9] & 2.39 & 3.78 & 78 & \(-\)0.16 \\ POS [10] & 2.28 & 3.51 & 84 & 0.28 \\ LCTC [11] & 2.23 & 3.42 & 80 & 0.25 \\
3D CNN [12] & 2.02 & 3.29 & 86 & 0.23 \\ CA-POS (Proposed) & 2.13 & 3.38 & 82 & 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative performance comparison of seven HR detection algorithms. Figure 12: Absorptivity (K\({}_{\text{a}}\)) at two wavelengths used to obtain SpO2. measurement value, we concluded that noncontact oxygen saturation detection technology using a smartphone camera is sufficient for utilization in noncontact bio-signal detection. However, since the experiment was only performed on healthy people with insignificant changes in their SpO2, additional research is required to determine the optimal absorption rate constants (A and B) for various healthy and patient groups. Figures 16 and 17 show the SpO2 and RR detection results obtained using the image sequence employed in HR detection. The Bland-Altman plot in Figure 16 shows that the mean error for the SpO2 was 0.17 and the confidence interval was (\(-\)2.96, 3.30). Additionally, the Bland-Altman plot in Figure 17 shows that the mean error for the RR was 0.42 and the confidence interval was (\(-\)2.92, 3.75). It was confirmed that the noncontact estimated SpO2 and RR had a linear relationship with the actual measured SpO2 and RR. Table 7 presents a quantitative comparison of SpO2 detection performance among existing methods and the proposed CA-POS method approach using MAE and RMSE as evaluation metrics. In terms of MAE, the proposed method achieved the lowest error of 0.86, outperforming the other methods. Regarding RMSE, the proposed method also demonstrated the lowest value of 1.23, indicating the most stable performance in terms of prediction variance. These results validate the effectiveness of the proposed method in improving SpO2 estimation accuracy. Figure 14: SpO2 detection results estimated based on absorption of (**a**) R and G, (**b**) R for B, and (**c**) G for B.  
Figure 13: SpO2 detection results for absorption at two wavelengths (A = 125, B = 29). Figure 16: (a) Linear relationship between and (b) Bland–Altman plot of measured SpO2 and SpO2 estimated based on image sequences.