4,958 research outputs found
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
Contact-Free Heart Rate Measurement From Human Face Videos and its Biometric Recognition Application
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
- …