# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 5 Conclusions  
This paper has shown the ability to gain better results for pulse monitoring using the Movement-Compensated Eulerian Video Magnification (MC-EVM) with color change magnification technique after using a process of face detection for small movement compensation. Our solution reduces the noisy signal that could appear in case of user movements. The results are validated in two steps: first to validate the results against the original EVM implementation with face detection and face cropping, and second to validate them against ground-truth data collected from 11 different target persons anonymously. Our solution showed great results for both the face detection and pulse monitoring processes. However, further research should include the automatic detection of low- and high-frequency cut-off ranges and the automatic detection of amplification factors according to lighting conditions to better predict the actual pulse of users.  
Conceptualization, A.B.; methodology, A.B.; software, A.B.; validation, A.B. and M.K.; formal analysis, A.B.; investigation, A.B.; resources, A.B.; data curation, A.B.; writing--original draft preparation, A.B.; writing--review and editing, A.B. and M.K.; visualization, A.B.; supervision, M.K.; project administration, M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2019-1.3.1-KK-2019-00007 \"Innovacios szolgaltato bazis letrehozasa diagnoszitkai, terapias es kutatasi cela kiberrovosi rendszerek feljesztesere\" national project.  
Not applicable.  
**Data Availability Statement:** The data used in this research for validation purposes can be found in Kaggle at the following link: [[https://www.kaggle.com/datasets/abdallahbenhamida/mc-evm-videos-dataset](https://www.kaggle.com/datasets/abdallahbenhamida/mc-evm-videos-dataset)]([https://www.kaggle.com/datasets/abdallahbenhamida/mc-evm-videos-dataset](https://www.kaggle.com/datasets/abdallahbenhamida/mc-evm-videos-dataset)) (accessed on 24 October 2024).  
**Acknowledgments:** The authors would like to thank the 2019-1.3.1-KK-2019-00007 \"Innovacios zsolidatto bazis letrehozasa diagnoszitkai, terapias es kutatasi cela kiberovosi rendserek feijzesleerese\" for its financial support, and, furthermore, we would like to thank the AIAM (Applied Informatics and Applied Mathematics) Doctoral School of Obuda University, Budapest, Hungary, and the Hungarian Research Network (HUN-REN), Budapest, Hungary, for their support in this research.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Figure A2: (**a**) MC-EVM pulse result for subject 2 (Pulse: 91.88 bpm). (**b**) EVM pulse result for subject 2 (Pulse: 96.84 bpm). Subject ID: 2; Alpha: 100; Low freq.: 1.5; High freq.: 1.75.  
Figure A3: (**a**) MC-EVM pulse result for subject 3 (Pulse: 54.12 bpm). (**b**) EVM pulse result for subject 3 (Pulse: 54.12 bpm). Subject ID: 3; Alpha: 100; Low freq.: 0.75; High freq.: 0.95.  
Figure 6: (a) MC-EVM pulse result for subject 6 (Pulse: 84.33 bpm). (b) EVM pulse result for subject 6 (Pulse: 86.39 bpm). Subject ID: 6; Alpha: 100; Low freq.: 1.41; High freq.: 1.45.  
Figure A7: (a) MC-EVM pulse result for subject 7 (Pulse: 87.31 bpm). (b) EVM pulse result for subject 7 (Pulse: 82.71 bpm). Subject ID: 7; Alpha: 100; Low freq.: 1.44; High freq.: 1.54.  
Figure A9: **(a)** MC-EVM pulse result for subject 9 (Pulse: 107.71 bpm). **(b)** EVM pulse result for subject 9 (Pulse: 107.71 bpm). Subject ID: 9; Alpha: 100; Low freq.: 1.72; High freq.: 1.8.