# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 2 Related Works
### Eulerian Video Magnification for Pulse Extraction  
Pulse monitoring presents a key aspect in patient health monitoring as it allows one to give an overview of heart activity over a defined period of time. It helps to show the number of heartbeats in a minute. This involves the use of specialized devices (blood pressure sensors) to measure the number of heart beats per minute (bpm). However, a technique called Eulerian Video Magnification (EVM) allows researchers to monitor patients' pulses without sensors for pulse measurement. Eulerian Video Magnification aims to magnify small changes of color and/or motion over time to make them more visible and perceptible. This technique decomposes the input video (or set of images) into many frequency bands using a temporal filter (which is used to enhance subtle temporal variations). Then, it amplifies the needed frequency bands using a spatial filter (which is used to enhance spatial frequencies), and finally combines back the updated frequency bands, which reflects the detected and magnified result, to produce a Eulerian magnified video which could be made by adding the magnified signal to the original frames one by one. Nowadays, EVM is used either for the detection of vital signs (such as the pulse) or the detection of small movements in manufacturing or in the robotics field [1]. EVM was originally proposed for two different types of application area: color and motion changes magnification. As for color magnification, this serves to reveal the small changes over time according to the color of a given area from the video. The same is valid for motion magnification except that it is used to amplify the changes of small motion between video frames. The color change magnification works by detecting the change in face color over time with every heartbeat when the blood flows over the face blood vessels [1; 22; 23]. Further research on this topic introduced a new video magnification technique to magnify the small motion changes even in the presence of large-scale motions [24]. Furthermore, a new learning-based method for motion magnification was proposed in [25], which allowed artifacts to be reduced and showed better results for noise handling by using a learning-based technique which is learning the temporal filter from the used data. According to previous research, EVM is used for pulse monitoring using color change magnification. The only two limitations that should be considered are:  
* The computational limitations of the device. * The need to keep the patient from making any large-scale face movements. Many researchers have been working to provide smartphone-based solutions to make it possible for contactless, non-invasive, and real-time pulse monitoring such as in [6; 7; 8; 9; 10; 11]. A solution was provided to calculate the heart rate, respiratory rate, and temperature from the forehead of the patient [12]. Another mobile-based solution was implemented to conduct tympan membrane mobility monitoring using video-otoscopy based on Eulerian Video Magnification [13]. A thermal video-based EVM was implemented in [14] to provide a solution for heart rate prediction in a contactless technique for mobile devices. In [11], the authors proposed a novel method for heart failure detection and monitoring in a non-invasive way using an EVM application. Furthermore, an attempt to reduce the amplified noise in the EVM process for the input video was introduced in [15]. This uses an approach that works for both color and motion magnification in videos and for both linear and phase-based EVM. It allows us to process the resulting video of the EVM process to carry out noise cancellation. A comparison of the desktop-based and phone-based EVM processes was conducted by implementing both types and validated using an oximeter device in [26]. Moreover, a micromotion monitoring system for the movement of the hand was introduced in [16] to help reveal important health information such as veins pulse.