Camera Measurement of Physiological Vital Signs

Abstract

The need for remote tools for healthcare monitoring has never been more apparent. Camera measurement of vital signs leverages imaging devices to compute physiological changes by analyzing images of the human body. Building on advances in optics, machine learning, computer vision and medicine these techniques have progressed significantly since the invention of digital cameras. This paper presents a comprehensive survey of camera measurement of physiological vital signs, describing they vital signs that can be measured and the computational techniques for doing so. I cover both clinical and non-clinical applications and the challenges that need to be overcome for these applications to advance from proofs-of-concept. Finally, I describe the current resources (datasets and code) available to the research community and provide a comprehensive webpage (https://cameravitals.github.io/) with links to these resource and a categorized list of all the papers referenced in this article. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing; • Applied computing → Bioinformatics; • Computing methodologies → Computer vision.

Estimation of blood pressure waveform from facial video using a deep U-shaped network and the wavelet representation of imaging photoplethysmographic signals

“…BP4D+ is a multimodal dataset publicly available to the research community 1 . The database initially includes the physiological, thermal, 2D video, 3D and dierent metadata and annotations of 140 participants [32].…”

“…Research on the remote measurement of physiological signals and cardiovascular parameters from facial video has made signicant progress the last past years. The eld is booming and supported by several signicant studies [1]. The principle, termed imaging (or remote) photoplethysmography (iPPG), consists in measuring the subtle uctuations of skin color.…”

pyVHR: a Python framework for remote photoplethysmography

“…The blossoming of the field and the variety of the proposed solutions raise the issue, for both researchers and practitioners, of a fair comparison among proposed techniques while engaging in the rapid prototyping and the systematic testing of novel methods. Under such circumstances, several reviews and surveys concerning rPPG ( McDuff et al, 2015 ; Rouast et al, 2018 ; Heusch, Anjos & Marcel, 2017a ; Unakafov, 2018 ; Wang et al, 2016 ; McDuff & Blackford, 2019 ; Cheng et al, 2021 ; McDuff, 2021 ; Ni, Azarang & Kehtarnavaz, 2021 ) have conducted empirical comparisons, albeit suffering under several aspects, as discussed in ‘Related Works’.…”

“…In the last decade the rPPG domain has witnessed a flourish of investigations ( McDuff et al, 2015 ; Rouast et al, 2018 ; Heusch, Anjos & Marcel, 2017a ; Unakafov, 2018 ; Wang et al, 2016 ; McDuff & Blackford, 2019 ; Cheng et al, 2021 ; McDuff, 2021 ; Ni, Azarang & Kehtarnavaz, 2021 ). Yet, the problem of a fair and reproducible evaluation has been in general overlooked.…”

Federated Remote Physiological Measurement with Imperfect Data

“…These systems use ubiquitously available webcams and smartphone cameras to measure important physiological vital signs such as the cardiac pulse [27], breathing rate [26] and blood oxygen saturation [32] of a patient without the data leaving the device. The methods rely on capturing subtle variations in light reflected from the body that capture volumetric changes in blood (the photoplethysmogram/PPG) and mechanical motions resulting from cardiac and respiratory function (e.g., the ballistocardiogram/BCG) [21]. Democratizing (or scaling) camera-based physiological sensing in this way has much potential.…”