Contactless Health Monitoring: An Overview of Video-Based Techniques Utilising Machine/Deep Learning

Abstract

Vital signs are crucial indicators of an individual's physiological well-being and represent one of the primary evaluations conducted in clinical and hospital environments. A comprehensive evaluation of a patient's health state depends on these signs which include heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), blood pressure (BP) and body temperature (BT). In recent years, there has been significant interest in using imaging photoplethysmography (iPPG) with consumer-level cameras for contactless health monitoring (CHM) to accurately assess vital signs. The introduction of iPPG in CHM signifies the beginning of a remarkable era in the history of healthcare, whereby diagnostic processes are enhanced via the integration of technology and patient well-being. This review article presents a comprehensive examination of CHM techniques utilising machine learning (ML) and deep learning (DL) algorithms for the assessment of critical vital signs. The article addresses the challenges and research gaps identified in recent studies, particularly those related to variations in lighting conditions, head movements and the impact of different colour types on the accuracy and reliability of CHM techniques. Finally, we propose several recommendations aimed to enhance the efficiency of CHM systems. These include the development of more robust learning algorithms and the creation of diverse datasets that encompass a wide range of demographics including variations in gender, skin colour and lighting conditions.