Non-Contact Vital Signs Estimation Ml and Dl Techniques: A Comprehensive Survey

Abstract

In the past decade,health care monitoring has emerged as the most promising area of research in the field of medicine.Vital sign signals have rapidly become a dominant factor with the recent advancement of healthcare applications. The Human Vital Signs (HVS), which are utilized for predicting medical and physical health issues earlier, are the essential ones that reveals the actual health status of the patients.Recent research works utilizes techniques like Electroencephalogram (EEG), Photoplethysmography (PPG), wireless sensors, Internet of Things (IoT), Electrocardiogram (ECG), and Remote-PPG (RPPG) However, a prominent role is played by the RPPG signal in HVS estimation. This paper aims to explore contactless HVS monitoring utilizing RPPG signals and also investigate the numerous Machine Learning (ML) and Deep Learning (DL) approaches that are employed for HVS prediction. Likewise, by evaluating certain quality metrics, the performance of the different techniques is validated. Further the survey examines the related models and  the best techniques for non-contact HVS measurement. The key objective of this study is to provide a  detailed insight into the purpose of HVS prediction.