Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

Abstract

The measurement of physiological parameters is fundamental to assess the health status of an individual. The contactless monitoring of vital signs may provide benefits in various fields of application, from healthcare and clinical setting to occupational and sports scenarios. Recent research has been focused on the potentiality of camera-based systems working in the visible range (380-750 nm) for estimating vital signs by capturing subtle color changes or motions caused by physiological activities but invisible to human eyes. These quantities are typically extracted from videos framing some exposed body areas (e.g., face, torso, and hands) with adequate post-processing algorithms. In this review, we provided an overview of the physiological and technical aspects behind the estimation of vital signs like respiratory rate, heart rate, blood oxygen saturation, and blood pressure from digital images as well as the potential fields of application of these technologies. Per each vital sign, we provided the rationale for the measurement, a classification of the different techniques implemented for post-processing the original videos, and the main results obtained during various applications or in validation studies. The available evidence supports the premise of digital cameras as an unobtrusive and easy-to-use technology for physiological signs monitoring. Further research is needed to promote the advancements of the technology, allowing its application in a wide range of population and everyday life, fostering a biometrical holistic of the human body (BHOHB) approach.

Keywords: SpO2; blood pressure; contactless monitoring; digital camera; pulse rate; remote photoplethysmography; remote vital signs monitoring; respiratory rate.

Figure 1

Anatomy of the respiratory system (A), face (B; adapted from von Arx et al., 2018), and hand (C) vascularization (from Habib et al., 2012). In (B), numbers indicate the main arteries that carry the blood to the forehead and cheeks, specifically: common carotid artery (1), external carotid artery (2), internal carotid artery (3), facial artery (4), transverse facial artery (16), superficial temporal artery (17) and frontal branch (17.1), ophthalmic artery (18).

Figure 2

Measurement sites of the body and principal steps for extracting f_R, HR, SpO₂, and BP.

Figure 3

(A) Exemplary flowchart with the main steps to extract the breathing pattern from images by analyzing chest wall movements through color-based or motion-based methods (adapted from Romano et al., 2021). (B) Framework for respiration detection from rPPG signal (adapted from van Gastel et al., 2016).

Figure 4

An example of the pipeline used to extract HR and heart rate variability from rPPG signal extracted through a color-based method based on POS post-processing algorithm (from Gudi et al., 2020).

Figure 5

Overview of a typical framework to retrieve an estimation of SpO₂ from the analysis of video images (adapted from Fatima Galvao Rosa and Betini, 2020).

Figure 6

(A) Example of different PPG parameters which can be extracted based on the morphology of the signal (e.g., PPG width, PPG height, dicrotic notch, PPG peak, PPG foot). (B) Flowchart to retrieve SBP and DPB from a rPPG signal (adapted from McDuff et al., 2014).