# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 4 Results and Discussion
### Evaluation Measures  
In order to validate the results of our proposed solution, we conducted a series of experiments to prove the performance of MC-EVM. The evaluation and validation steps were:  
* First, validate our choice of the use of the channel of the red color to calculate the pulse (Figure 3);
* Check the validity of pulse measurement with and without the face detection model (Figure 4);
* Validate the results of MC-EVM against other solutions from the related works (Table 2);
* Validate the face detection model (Figure 5);
* Validate the results using the ground-truth data with the collected videos (Table 3);
* Validate the displacement of the detected face among the all the frames of all the videos (Figure 6);
* And finally, plot and discuss the results of the calculated pulse with and without face detection against the ground truth data (Figure 7). Figure 4: EVM without face detection (**a**) vs. MC-EVM with face detection results (**b**). \begin{table} \begin{tabular}{c c c c c c c} \hline \hline  & **MC-EVM** & **A1** & **A2** & **A3** & **A4** & **A5** \\ \hline \begin{tabular}{c} Number of \\ subject people \\ \end{tabular} & \(11\) & \(11\) & \(1\) & \(6\) & \(2\) & \begin{tabular}{c} Datasets: PURE, \\ UBFC, and \\ COHFACE \\ \end{tabular} \\ \hline \begin{tabular}{c} Needs a \\ thermal camera? \\ \end{tabular} & No & Yes & Yes & No & No & No \\ \hline \begin{tabular}{c} Needs RGB camera \\ \end{tabular} & Yes & Yes & Yes & Yes & Yes \\ \hline \begin{tabular}{c} Real-time or offline? \\ \end{tabular} & Both * & Offline & Offline & Offline & Offline & Real-time \\ \hline \begin{tabular}{c} Extracted vital signs \\ \end{tabular} & Pulse & \begin{tabular}{c} Heart rate, \\ Respiratory rate, \\ Temperature \\ \end{tabular} & Pulse & \begin{tabular}{c} Respiratory \\ Rate \\ \end{tabular} & Pulse & Heart Rate (HR) \\ \hline \begin{tabular}{c} Absolute max error \\ \end{tabular} & \begin{tabular}{c} 4.11\% \\ (1.85 ppm) \\ \end{tabular} & \begin{tabular}{c} 24.38\% \\ \end{tabular} & \begin{tabular}{c} 1.89\% \\ (only 1 \\ person) \\ \end{tabular} & \
\begin{tabular}{c} 15.56- \\ 50.28\% \\ \end{tabular} & / &
\begin{tabular}{c} PURE: 1.16 ppm, \\ UBFC: 1.56 ppm, \\ COHFACE: \\ 2.10 ppm \\ \end{tabular} \\ \hline \hline \end{tabular}   
* On a real-time basis, the users must always measure during at least the last 3–10 s of the video. Figure 5: Confusion matrix with 80% threshold (**left**) vs. 60% threshold (**right**). Figure 6: Displacement and the normalized displacement of the face detection bounding box. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & **Amazfit Band 7** & **MC-EVM Pulse** & **Difference with** & **EVM Pulse Without** & **Difference Without** \\  & **Pulse** & **with Face Detection** & **Face Detection** & **Face Detection** & **Face Detection** \\ \hline
1 & 81 & 79.44 & \(-1.56\) (\(-1.93\)%) & 84.73 & 3.73 (4.60\%) \\ \hline
2 & 93 & 91.88 & \(-1.12\) (\(-1.20\)%) & 96.84 & 3.84 (4.13\%) \\ \hline
3 & 52 & 54.12 & 2.12 (4.08\%) & 54.12 & 2.12 (4.08\%) \\ \hline
4 & 65 & 62.33 & \(-2.67\) (\(-4.11\)%) & 62.33 & \(-2.67\) (\(-4.11\)\%) \\ \hline
5 & 84 & 83.83 & \(-0.17\) (\(-0.20\)%) & 85.87 & 1.87 (2.23\%) \\ \hline
6 & 86 & 84.33 & \(-1.67\) (\(-1.94\)%) & 86.39 & 0.39 (0.45\%) \\ \hline
7 & 91 & 87.31 & \(-3.69\) (\(-4.05\)%) & 82.71 & \(-8.29\) (\(-9.11\)\%) \\ \hline
8 & 123 & 124.39 & 1.39 (1.13\%) & 128.83 & 5.83 (4.74\%) \\ \hline
9 & 105 & 107.71 & 2.71 (2.58\%) & 107.71 & 2.71 (2.58\%) \\ \hline
10 & 73 & 70.93 & \(-2.07\) (\(-2.84\)%) & 70.93 & \(-2.07\) (\(-2.84\)\%) \\ \hline
11 & 100 & 98.81 & \(-1.19\) (\(-1.19\)\%) & 98.81 & \(-1.19\) (\(-1.19\)\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results validation against the ground truth pulse data. The results of MC-EVM are compared to other solutions from the related works section according to the error of estimation of the pulse in \"bpm\" and in percentage in addition to the number of recorded videos and the extracted bio-signals.