# Camera-Based Vital Sign Estimation Techniques and Mobile App Development
## 3 Results  
### HR Detection Results  
We analyzed the HR detection results for the facial ROI in the image captured by the smartphone camera as shown in Figure 9. The upper signal, \(x(n)\), in each subfigure displays the average value of all the pixels in the ROI on the time axis, and \(X(f)\) was obtained using zero padding and the FFT. As explained above, a BPF, \(H(f)\), with a passband of 0.7 to 4.0 Hz and a Hamming window was used to detect the HR frequency band. In addition, the lower signal, \(Y(f)\), in each subfigure is the spectrum resulting from BPF processing, where the HR frequency is the one with the peak value. Figure 9a-h contains the pulse frequency detected according to the facial angle. Figure 9: HR detection results for various face ROIs. The red boxes indicate the detected face areas. Table 5 shows the noncontact HR detection results for 508 image sequences acquired using a smartphone camera under various conditions (face positions, illumination levels, distances from the camera, etc.). When the difference between the HR detected in a noncontact manner and the HR measured by the pulse oximeter was three or less, the HR was judged as normal; otherwise, it was judged as an error. In the experiments performed, the accuracy of the HRs obtained through noncontact detection and considered normal exceeded 88%. In addition, the remeasurement rate was approximately 10%. This implies that the noncontact HR detection method has a certain error rate and that the remeasurement algorithm proposed herein is extremely useful. The correlations between the noncontact estimated HRs and the actual measured HRs for 457 video sequences, excluding HRs requiring remeasurement, are shown in Figures 10 and 11. In Figure 10, the orange line indicates the HR measured by the pulse oximeter, and the gray line indicates the HR estimated without contact using the smartphone camera. The actual measured HR and noncontact estimated HR were extremely similar. Figure 11a shows that the distribution of the actual measured HR vs. the noncontact estimated HR was linear. The error rate for significant differences between the noncontact estimated HRs was approximately 2%. Therefore, noncontact HR detection using a smartphone camera is suitable for use in real life. Figure 11b shows the Bland-Altman plots of the real HRs and those predicted using the proposed method. Bland-Altman plots are used to assess the level of agreement between two measurement methods. The difference between the two measurements is plotted against the mean of the two measurements, and a high density around the central'mean difference' line within the 'limits of agreement' indicates strong agreement between the two schemes. The Bland-Altman plot revealed that the mean difference in the HR was \(-\)0.94, with a confidence interval of (\(-\)4.09, 2.56). In the plot, the difference and mean results were extremely close together and appeared to overlap. In order to quantitatively compare the performance of the proposed HR detection method and existing HR detection methods, the mean absolute error (MAE) and root mean  
Figure 10: Noncontact HR detection results for image sequences excluding those requiring remeasurement (orange line: actual measured value; blue line: noncontact estimated value). \begin{table}
\begin{tabular}{c c c c c} \hline  & **Normal** & **Remeasurement** & **Error** & **Sum** \\ \hline Detection result & 447 & 51 & 10 & 508 \\ \hline Probability [\%] & 88.0 & 10.0 & 2.0 & 100 \\ \hline \end{tabular}
\end{table} 
Table 5: Noncontact HR detection results. square error (RMSE) of the \(i\)-th actual HR, \(HR_{i}^{ref}\), and the HR measured in N experiments, \(HR_{i}\), were calculated as follows [20]. \[\text{MAE}=\frac{1}{N}{\sum_{i}}{\left|{HR_{i}-HR_{i}^{ref}}\right|} \tag{7}\]  
\[\text{RMSE}=\frac{1}{N}\sqrt{{\sum_{i}}{\left({HR_{i}-HR_{i}^{ref}}\right)^{2}}} \tag{8}\]  
The percentage of epochs (PEs) required for the HR estimation error to be less than 3.5 BPM was calculated as follows.