# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 4 Results and Discussion
### Results Validation  
Our results are compared to pre-existing solutions according to the accuracy, error of validation between the EVM pulse and the ground-truth pulse, number of subject people, and the used devices. The results of this comparison are shown in Table 2 where: A1 is the results presented in [12], A2 is the results presented in [14], A3 is the results presented in [53], A4 is the results presented in [7], and A5 is the results presented in [54], which provides a heart rate assessment using video-based inputs. In order to prove why we used the red color channel we used the video of subject number 7 in our dataset. The average value of all the Blue, Green, Red (BGR) channels is shown in Figure 3 where it is clear that the red channel dominates in all the frames with a higher average than both the green and the blue channels. Therefore, we decided to use the red color channel instead of the green and the blue channels. However, in another piece of research, it could be a good idea to compare this with other approaches, such as by:  
* Selecting the channel that has a bigger range of change;
* Taking the average of all channels;
* Changing it to the HSV color space and showing the influence on the results. The proposed algorithm showed good results compared to the original EVM implementation while using it with the face detection process. The difference between the original EVM without face detection and our solution is shown in Figure 4, which shows the mean red color and the estimated pulse with and without face detection for subject number 7 in our collected data. We selected this subject person as it shows the biggest difference in pulse estimation with and without face detection. Figure 7: The estimated pulse vs. correct pulse comparison (**left**); error percentage between the estimated pulse and correct pulse (**right**). We can see that the estimated pulse with face detection is better than the estimated pulse without face detection, knowing that the correct pulse from the Amazfit Band 7 is 91 bpm. In addition, the face detection process resulted in more precise results because the minimum and maximum mean red colors are bigger than those without face detection, which reflects the truth because the background of the video should not influence the result, therefore, by removing the background, we should obtain bigger red color values as we are focusing only on the face. Furthermore, the small noise peaks between every two peaks. Finally, in the case of small-scale noisy signals, this could be solved by using the last three to five beats in real-time to always calculate the actual pulse on a real-time basis. In order to prove the validity of the face detection model, we evaluated the results of detection and we obtained the following performance factors using a threshold of 80%:  
* Accuracy: 85.57%;
* Precision: 99.45%;
* Recall: 85.57%;
* F1 Score: 91.99%. With a threshold of 60%, we recorded the following results:  
* Accuracy: 94.21%;
* Precision: 98.45%;
* Recall: 94.21%;
* F1 Score: 96.28%. Figure 5 shows the confusion matrices resulting from the evaluation of the face detection model where the true negative detections refer to the set of faces that were correct but the model was not able to detect them due to the used thresholds of 80% (left) and 60% (right). We performed a series of tests on our recorded videos from 11 different subjects, and the results were validated against the ground truth data measured using the \"Amazfit Band 7\" device. The results are shown in Table 3.  
In order to better present all the results achieved in this research, the figures of pulse detection of all the individual subjects with face detection are presented in the Appendix A in Figure 10(a), Figure 10(a), Figure 10(a), Figure 10(a), Figure 11(a), Figure 11(a), Figure 11(a), Figure 11(a), Figure 11(a), and Figure 11(a).