Photoplethysmography Imaging (PPGI) is a camera-based and non-contact technology for measurement of physiological signals. It has been shown that important physiological parameters such as heart rate, heart rate variability and respiratory rate can be derived from PPGI. However, as is the case with most non-contact measurement techniques, motion artefacts present a major challenge. Various algorithms for application to both the 2D PPGI video frames as well as the resulting 1D PPGI waveforms have been developed in order to enhance robustness against motion. In this paper, we focus on the aspect of feature point tracking in the 2D PPGI video sequences. We present an experimental setup, where we used a motion capture system in order to obtain a reference for motion during the recording of PPGI video sequences. In a laboratory experiment, PPGI video sequences were recorded from ten healthy volunteers, who were asked to perform various movements during the recording. The KLT tracking algorithm was applied to the recorded sequences and results compared with the reference values from the motion capture system. The results indicate, that tracking of measurement regions in PPGI video sequences is only one element towards motion robust PPGI. In most scenarios, tracking is not sufficiently precise, requiring further processing of the PPGI waveforms in order to reduce motion artefacts in PPGI signals. These indications were confirmed by further analysis when we looked into the effects of tracking on PPGI heart rate extraction.
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http://dx.doi.org/10.1109/EMBC.2019.8856810 | DOI Listing |
Sensors (Basel)
January 2024
St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia.
Heart rate is an essential vital sign to evaluate human health. Remote heart monitoring using cheaply available devices has become a necessity in the twenty-first century to prevent any unfortunate situation caused by the hectic pace of life. In this paper, we propose a new method based on the transformer architecture with a multi-skip connection biLSTM decoder to estimate heart rate remotely from videos.
View Article and Find Full Text PDFPhysiol Meas
November 2022
Critical Care Research Group, Kadoorie Centre for Critical Care Research and Education, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom.
Clinical assessment of skin perfusion informs prognosis in critically ill patients. Video camera monitoring could provide an objective, continuous method to monitor skin perfusion. In this prospective, interventional study of healthy volunteers, we tested whether video camera-derived photoplethysmography imaging and colour measurements could detect drug-induced skin perfusion changes.
View Article and Find Full Text PDFBiomed Opt Express
July 2022
AI Systems in Medicine (KISMED), Technische Universität Darmstadt, Darmstadt, Germany.
Spatial mapping of skin perfusion provides essential information about physiological processes that are often hidden from the eyes of the examining physician. The perfusion map quality depends on several key factors, such as the camera system type, frame rate, sensitivity, or signal-to-noise ratio. When investigating physiological parameters, the reference signal allows for increasing the spatial resolution of the photoplethysmography imaging (PPGI) system.
View Article and Find Full Text PDFSensors (Basel)
November 2021
Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea.
In general, facial image-based remote photoplethysmography (rPPG) methods use color-based and patch-based region-of-interest (ROI) selection methods to estimate the blood volume pulse (BVP) and beats per minute (BPM). Anatomically, the thickness of the skin is not uniform in all areas of the face, so the same diffuse reflection information cannot be obtained in each area. In recent years, various studies have presented experimental results for their ROIs but did not provide a valid rationale for the proposed regions.
View Article and Find Full Text PDFBiomed Eng Online
January 2021
Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074, Aachen, Germany.
Background: Only a small fraction of the information available is generally used in the majority of camera-based sensing approaches for vital sign monitoring. Dedicated skin pixels, for example, fall into this category while other regions are often disregarded early in the processing chain.
Methods: We look at a simple processing chain for imaging where a video stream is converted to several other streams to investigate whether other image regions should also be considered.
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