# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring  
[PERSON]  
1 BioTech Research Center, Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, 1034 Budapest, Hungary 12 John [PERSON] Neumann Faculty of Informatics, Institute of Biomatics, Obuda University, 1034 Budapest, Hungary; [EMAIL_ADDRESS]
sensors or equipment other than a smart device such as measuring the pulse using the Eulerian Video Magnification (EVM). EVM is a non-invasive technology that can estimate pulse without special equipment. It can reveal small-scale and hardly visible changes in real life such as color changes (for pulse estimation) and motion changes (for breathing monitoring). The EVM process intends to magnify changes from the source video/images input to be detectable by the human eye. For instance, the change in the face color over time reflects the changes while the blood is flowing on the blood vessels, and, therefore, it reflects the heart contraction/pulse over time [1]. Another example is to magnify the body motion (chest movement) while sleeping from the source video/images over time and then extract the muscle movement during the breathing activity [2,3]. One of the main problems that faced EVM is the noise due to small-scale patient movements. EVM intends to magnify the small changes in face color, however, it will also magnify the noise which will then makes it a challenging task to detect the peaks and estimate the pulse. This is an excellent research topic to help to build a solution that can overcome the limitations and the impact of this noise on the final result of the EVM process. We collected the related research works in Section 2 and categorized them into three different groups. First, human bio-signals monitoring and mainly the pulse. In this section, we investigate the importance of pulse and other bio-signals monitoring such as the research paper by [PERSON] et al. [4] that showed the usage of pulse rate monitoring for the assessment of a virtual reality (VR)-based aerobic exercise program, and the research that was published by [PERSON] et al. [5] in 2024 which highlighted the advancements in wearable triboelectric nanogenerators (TENGs) for the purpose of pulse waves monitoring. Second, we show the different use cases of EVM in the field of patient monitoring. EVM is currently used in much biomedical research to extract human pulse, breathing rhythm, and even for other purposes [6, 7, 8, 9, 10, 11] using color and motion changes, respectively. Currently, much of this research needs high computational capabilities and is not yet suitable to be used by self-care patients [10, 11, 12, 13, 14, 15, 16]. Moreover, these solutions could result in wrong detection in cases where the subject person is moving during the measurement. Therefore, we need a compensation tool to extract the region of interest (ROI) from the images (using primarily an object tracking solution, as the detection result could be biased by the background), then feed it into the EVM process to extract the needed bio-signal parameters. Finally, we collected the relevant research works in the field of face detection and face tracking using artificial intelligence and machine learning. The main models that we discussed are the Single-Shot multi-box Detector (SSD) [17] and EfficientDet-Lite0 [18] models that we used in our research due to their life versions for devices with low computational capabilities. This research proposes an algorithm that makes it possible to compensate the face movement on the images provided for the EVM process using a trained Single-Shot multi-box Detector (SSD) [17] and/or EfficientDet-Lite0 [18] models. The used face detection models (SSD and EfficientDet) extract the face from input images and feed it into the EVM process to magnify color changes over time. Since these processes operate at different frequencies, synchronization issues can lead to incorrect pulse detection.