Key result
Contactless health monitoring using imaging photoplethysmography and machine learning enables vital sign assessment, though challenges remain regarding lighting, head movements, and skin color.
Why the study?
There is growing interest in using imaging photoplethysmography with consumer-level cameras for contactless health monitoring, but challenges remain regarding lighting variations, head movements, and skin colour types.
Video-based contactless health monitoring using machine and deep learning shows promise for vital sign assessment but requires more robust algorithms and diverse datasets to overcome current limitations.
iPPG review supports contactless vital signs exploration; leaves open rigorous validation before clinical adoption.
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.
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Hajr et al. (2025) conducted a review in Vital signs assessment. Contactless health monitoring using imaging photoplethysmography (iPPG) with machine/deep learning was evaluated on Accuracy and reliability of vital sign assessment. Contactless health monitoring using imaging photoplethysmography and machine learning enables vital sign assessment, though challenges remain regarding lighting, head movements, and skin color.
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