Cardiovascular assessment by imaging photoplethysmography – a review

AbstractOver the last few years, the contactless acquisition of cardiovascular parameters using cameras has gained immense attention. The technique provides an optical means to acquire cardiovascular information in a very convenient way. This review provides an overview on the technique’s background and current realizations. Besides giving detailed information on the most widespread application of the technique, namely the contactless acquisition of heart rate, we outline further concepts and we critically discuss the current state.</jats:p

Holographic laser Doppler imaging of pulsatile blood flow

We report on wide-field imaging of pulsatile motion induced by blood flow using heterodyne holographic interferometry on the thumb of a healthy volunteer, in real-time. Optical Doppler images were measured with green laser light by a frequency-shifted Mach-Zehnder interferometer in off-axis configuration. The recorded optical signal was linked to local instantaneous out-of-plane motion of the skin at velocities of a few hundreds of microns per second, and compared to blood pulse monitored by plethysmoraphy during an occlusion-reperfusion experiment.Comment: 5 pages, 5 figure

Exploring Low Cost Non-Contact Detection of Biosignals for HCI

In an effort to make biosignal integration more accessible to explore for more HCI researchers, this paper presents our investigation of how well a standard, near ubiquitous webcam can support remote sensing of heart rate and respiration rate across skin tone ranges. The work contributes: how the webcam can be used for this purpose, its limitations, and how to mitigate these limitations affordably, including how the skin tone range affect the estimation results.Comment: 10 pages, 5 figure

Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG

Remote Photoplethysmography (rPPG) is a technology that utilizes the light absorption properties of hemoglobin, captured via camera, to analyze and measure blood volume pulse (BVP). By analyzing the measured BVP, various physiological signals such as heart rate, stress levels, and blood pressure can be derived, enabling applications such as the early prediction of cardiovascular diseases. rPPG is a rapidly evolving field as it allows the measurement of vital signals using camera-equipped devices without the need for additional devices such as blood pressure monitors or pulse oximeters, and without the assistance of medical experts. Despite extensive efforts and advances in this field, serious challenges remain, including issues related to skin color, camera characteristics, ambient lighting, and other sources of noise, which degrade performance accuracy. We argue that fair and evaluable benchmarking is urgently required to overcome these challenges and make any meaningful progress from both academic and commercial perspectives. In most existing work, models are trained, tested, and validated only on limited datasets. Worse still, some studies lack available code or reproducibility, making it difficult to fairly evaluate and compare performance. Therefore, the purpose of this study is to provide a benchmarking framework to evaluate various rPPG techniques across a wide range of datasets for fair evaluation and comparison, including both conventional non-deep neural network (non-DNN) and deep neural network (DNN) methods. GitHub URL: https://github.com/remotebiosensing/rppg.Comment: 19 pages, 10 figure

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Exploring remote photoplethysmography signals for deepfake detection in facial videos

Abstract. With the advent of deep learning-based facial forgeries, also called "deepfakes", the feld of accurately detecting forged videos has become a quickly growing area of research. For this endeavor, remote photoplethysmography, the process of extracting biological signals such as the blood volume pulse and heart rate from facial videos, offers an interesting avenue for detecting fake videos that appear utterly authentic to the human eye. This thesis presents an end-to-end system for deepfake video classifcation using remote photoplethysmography. The minuscule facial pixel colour changes are used to extract the rPPG signal, from which various features are extracted and used to train an XGBoost classifer. The classifer is then tested using various colour-to-blood volume pulse methods (OMIT, POS, LGI and CHROM) and three feature extraction window lengths of two, four and eight seconds. The classifer was found effective at detecting deepfake videos with an accuracy of 85 %, with minimal performance difference found between the window lengths. The GREEN channel signal was found to be important for this classifcationEtäfotoplethysmografian hyödyntäminen syväväärennösten tunnistamiseen. Tiivistelmä. Syväväärennösten eli syväoppimiseen perustuvien kasvoväärennöksien yleistyessä väärennösten tarkasta tunnistamisesta koneellisesti on tullut nopeasti kasvava tutkimusalue. Etäfotoplethysmografa (rPPG) eli biologisten signaalien kuten veritilavuuspulssin tai sykkeen mittaaminen videokuvasta tarjoaa kiinnostavan keinon tunnistaa väärennöksiä, jotka vaikuttavat täysin aidoilta ihmissilmälle. Tässä diplomityössä esitellään etäfotoplethysmografaan perustuva syväväärennösten tunnistusmetodi. Kasvojen minimaalisia värimuutoksia hyväksikäyttämällä mitataan fotoplethysmografasignaali, josta lasketuilla ominaisuuksilla koulutetaan XGBoost-luokittelija. Luokittelijaa testataan usealla eri värisignaalista veritilavuussignaaliksi muuntavalla metodilla sekä kolmella eri ominaisuuksien ikkunapituudella. Luokittelija pystyy tunnistamaan väärennetyn videon aidosta 85 % tarkkuudella. Eri ikkunapituuksien välillä oli minimaalisia eroja, ja vihreän värin signaalin havaittiin olevan luokittelun suorituskyvyn kannalta merkittävä