MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring

Automated tasks, mainly in the biomedical field, help to develop new technics to provide faster solutions for monitoring patients’ health status. For instance, they help to measure different types of human bio-signal, perform fast data analysis, and enable overall patient status monitoring. Eulerian Video Magnification (EVM) can reveal small-scale and hidden changes in real life such as color and motion changes that are used to detect actual pulse. However, due to patient movement during the measurement, the EVM process will result in the wrong estimation of the pulse. In this research, we provide a working prototype for effective artefact elimination using a face movement compensated EVM (MC-EVM) which aims to track the human face as the main Region Of Interest (ROI) and then use EVM to estimate the pulse. Our primary contribution lays on the development and training of two face detection models using TensorFlow Lite: the Single-Shot MultiBox Detector (SSD) and the EfficientDet-Lite0 models that are used based on the computational capabilities of the device in use. By employing one of these models, we can crop the face accurately from the video, which is then processed using EVM to estimate the pulse. MC-EVM showed very promising results and ensured robust pulse measurement by effectively mitigating the impact of patient movement. The results were compared and validated against ground-truth data that were made available online and against pre-existing solutions from the state-of-the-art.