Machine Learning Assessment of Heart Rate Confidence from Video Magnification

Video magnification (VM) provides an alternative health monitoring solution by enabling contactless and remote measurement of vital signs such as heart rate (DR).DR is a crucial biomarker for assessing the overall health of individuals, and the increased need to replace traditional wearable devices and avoid complex applications of sensors makes VM essential for in-home health assessment. VM can measure HR by detecting subtle changes of skin color due to blood flow, however, external factors such as subject or camera motion, variable lighting and skin tone can greatly affect performance. Previous work implemented methods that improved the overall accuracy of VM-based HR predictions but did not indicate if a given HR prediction was likely to be correct or incorrect. This work assesses the confidence of the predicted HR using a set of features derived during the VM process. Several machine learning models were evaluated to analyze the confidence parameters from three different VM processing methods. The results show an accuracy of92.1% for an SVM model that classifies if VM-based HR is correct or incorrect. The high classification accuracy indicates the effectiveness of the confidence metrics for discriminating between correct and incorrect HR predictions using VM.