A Reproducible Study on Remote Heart Rate Measurement

This paper studies the problem of reproducible research in remote photoplethysmography (rPPG). Most of the work published in this domain is assessed on privately-owned databases, making it difficult to evaluate proposed algorithms in a standard and principled manner. As a consequence, we present a new, publicly available database containing a relatively large number of subjects recorded under two different lighting conditions. Also, three state-of-the-art rPPG algorithms from the literature were selected, implemented and released as open source free software. After a thorough, unbiased experimental evaluation in various settings, it is shown that none of the selected algorithms is precise enough to be used in a real-world scenario

Comparison of scientific CMOS camera and webcam for monitoring cardiac pulse after exercise

In light of its capacity for remote physiological assessment over a wide range of anatomical locations, imaging photoplethysmography has become an attractive research area in biomedical and clinical community. Amongst recent iPPG studies, two separate research directions have been revealed, i.e., scientific camera based imaging PPG (iPPG) and webcam based imaging PPG (wPPG). Little is known about the difference between these two techniques. To address this issue, a dual-channel imaging PPG system (iPPG and wPPG) using ambient light as the illumination source has been introduced in this study. The performance of the two imaging PPG techniques was evaluated through the measurement of cardiac pulse acquired from the face of 10 male subjects before and after 10 min of cycling exercise. A time-frequency representation method was used to visualize the time-dependent behaviour of the heart rate. In comparison to the gold standard contact PPG, both imaging PPG techniques exhibit comparable functional characteristics in the context of cardiac pulse assessment. Moreover, the synchronized ambient light intensity recordings in the present study can provide additional information for appraising the performance of the imaging PPG systems. This feasibility study thereby leads to a new route for non-contact monitoring of vital signs, with clear applications in triage and homecare

DistancePPG: Robust non-contact vital signs monitoring using a camera

Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.Comment: 24 pages, 11 figure

Cardiac Inter Beat Interval and Atrial Fibrillation Detection using Video Plethysmography

Facial videoplethysmography provides non-contact measurement of heart activity based on blood volume pulsations detected in facial tissue. Typically, the signal is extracted using a simple webcam followed by elaborated signal processing methods, and provides limited accuracy of time-domain characteristics. In this study, we explore the possibility of providing accurate time-domain pulse and inter-beat interval measurements using a high- quality image sensor camera and various signal processing approaches, and use these measurements to diagnose atrial fibrillation. We capture synchronized signals using a high- quality camera, a simple webcam, an earlobe photoplethysmography sensor, and a body- surface electrocardiogram from a large group of subjects, including subjects diagnosed with cardiac arrhythmias. All signals are processed using both blind source separation and color conversion. We then assess accuracy of IBI detection, heart rate variability estimation, and atrial fibrillation diagnose by comparing to a body-surface electrocardiogram. We present a new heart variability indicator for blood volume pulsating signals. Our results demonstrate that the accuracy of a facial VPG system is greatly improved when using a high-quality camera. Coupling the high-quality camera with color conversion from RGB to Hue provides a level of accuracy equivalent to that of commercially available photoplethysmography sensors, and offers a non-contact alternative to current technology for heart rate variability assessment and atrial fibrillation screening

Use of ambient light in remote photoplethysmographic systems: comparison between a high-performance camera and a low-cost webcam

Imaging photoplethysmography (PPG) is able to capture useful physiological data remotely from a wide range of anatomical locations. Recent imaging PPG studies have concentrated on two broad research directions involving either high-performance cameras and or webcam-based systems. However, little has been reported about the difference between these two techniques, particularly in terms of their performance under illumination with ambient light. We explore these two imaging PPG approaches through the simultaneous measurement of the cardiac pulse acquired from the face of 10 male subjects and the spectral characteristics of ambient light. Measurements are made before and after a period of cycling exercise. The physiological pulse waves extracted from both imaging PPG systems using the smoothed pseudo-Wigner-Ville distribution yield functional characteristics comparable to those acquired using gold standard contact PPG sensors. The influence of ambient light intensity on the physiological information is considered, where results reveal an independent relationship between the ambient light intensity and the normalized plethysmographic signals. This provides further support for imaging PPG as a means for practical noncontact physiological assessment with clear applications in several domains, including telemedicine and homecare

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

A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos

This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment. Four alternatives from the literature are tested, three based in hand crafted approaches and one based on deep learning. The methods are compared using RGB videos from the COHFACE database. Experiments show that the learning-based method achieves much better accuracy than the hand crafted ones. The low error rate achieved by the learning based model makes possible its application in real scenarios, e.g. in medical or sports environments.Comment: Accepted in "IEEE International Workshop on Medical Computing (MediComp) 2020