Remote plethysmographic imaging using ambient light

By W. Verkruysse, L. O. Svaasand, J. Nelson. Optics Express, 2008

1,695 citations recorded by the source corpus.

Plethysmographic signals were measured remotely (> 1m) using ambient light and a simple consumer level digital camera in movie mode. Heart and respiration rates could be quantified up to several harmonics. Although the green channel featuring the strongest plethysmographic signal, corresponding to an absorption peak by (oxy-) hemoglobin, the red and blue channels also contained plethysmographic information. The results show that ambient light photo-plethysmography may be useful for medical purposes such as characterization of vascular skin lesions (e.g., port wine stains) and remote sensing of vital signs (e.g., heart and respiration rates) for triage or sports purposes.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/73bf382608d4c4cd559cda7dd9e3be25ebe7de61

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Remote Heart Rate Measurement from Face Videos under Realistic Situations (2014, background): “Previous work found that although red, green and blue channels all contains some level of plethysmographic signals, the green channel contains the strongest one among all three [20].”
DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks (2018, methodology): “Gold-standard physiological signals were measured with a FlexComp Infiniti that recorded Blood Volume Pulse (BVP) from a finger probe at a constant sampling frequency of 256 Hz.”
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks (2019, methodology): “In earlier studies of rPPG, most methods [1, 4, 10, 13, 14, 21, 22, 23] can be seen as a two-stage pipeline, which first detects or tracks the face to extract the rPPG signals, and then estimates the corresponding average HR from frequency analysis.”
Eulerian video magnification for revealing subtle changes in the world (2012, background): “This variation, while invisible to the naked eye, can be exploited to extract pulse rate [Verkruysse et al. 2008; Poh et al. 2010; Philips 2011].”
Visual Heart Rate Estimation with Convolutional Neural Network (2018, methodology): “[6] reimplemented two baseline HR estimation approaches [4, 21] and a method of Li et al.”
RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation (2019, methodology): “Although the color changes are invisible to human eyes, they can be captured by commodity RGB sensors and used for computing the HR [30], and a number of approaches have been reported to use such information for HR estimation.”
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models (2014, background): “It has been well known since the 1930s—see, for example, the introduction in the paper by Verkruysse et al (2008)—that the variations in tissue blood volume in a body segment with each heart beat modulate the transmission or reﬂection of visible (or infra-red) light through or from that body…”
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (2020, background): “Camera-based Physiological Measurement: Early work established that the blood volume pulse can be extracted by analysing skin pixel intensity changes over time [10, 11].”
Photoplethysmography revisited: from contact to noncontact, from point to imaging (2015, positive): “introduced a spatial ROI averaging approach [85].”
Wearable Photoplethysmographic Sensors—Past and Present (2014, background): “The green signal was found to provide the strongest plethysmographic signal among camera RGB signals [11,12].”
Physiological Parameter Monitoring from Optical Recordings with a Mobile Phone (2011, positive): “by hemoglobin in the green range, and it has been demonstrated to give a stronger cardiac pulse signal than the red or blue bands during remote PPG imaging [16].”
Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019, background): “, forehead and cheeks), which is in accordance with the priori knowledge mentioned in [32].”
Detecting Pulse from Head Motions in Video (2013, methodology): “Our method is complementary to the extraction of pulse rate from video via analysis of the subtle color changes in the skin caused by blood circulation [14, 18].”
Algorithmic Principles of Remote PPG (2017, methodology): “R EMOTE photoplethysmography (rPPG) enables contactless monitoring of human cardiac activities by detecting the pulse-induced subtle color variations on human skin surface using a multiwavelength RGB camera [1].”
Robust Pulse Rate From Chrominance-Based rPPG (2013, methodology): “Publications have even shown successful rPPG using a regular color video camera in the ambient light conditions [4]–[7].”
A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation (2016, background): “R EMOTE photoplethysmography (rPPG) enables contactless monitoring of human cardiac activities by detecting the pulse-induced subtle color changes on skin surface using a regular RGB camera [1], [2].”