Long-range non-contact imaging photoplethysmography: cardiac pulse wave sensing at a distance

Blackford, Ethan B.; Estepp, Justin R.; Piasecki, Adam; Bowers, Margaret A.; Klosterman, Samantha L.

doi:10.1117/12.2208130

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…Using an action camcorder, Al-Naji, Perera, and Chahl demonstrated the estimation of vital signs from a hovering unmanned aerial vehicle 83 . While Szankin et al used a webcam to estimate vital signs from a distance of six meters 84 , Blackford et al evaluated the possibility to extract HR with a mirrorless camera (Panasonic GH4 camera + Rokinon 650Z-B 650-1300 mm Super Telephoto Zoom Lens) from up to 100 m 85 , 86 . Jeong and Finkelstein presented a work on non-contact blood pressure estimation, in which a consumer-grade high-speed camera was used (Casio EX-FH20, 224x168 pixels at 420 frames per second) 87 .…”

Section: Survey Taxonomymentioning

confidence: 99%

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Antink

Lyra²,

Paul

et al. 2019

Yearb Med Inform

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Objectives: Camera-based vital sign estimation allows the contactless assessment of important physiological parameters. Seminal contributions were made in the 1930s, 1980s, and 2000s, and the speed of development seems ever increasing. In this suivey, we aim to overview the most recent works in this area, describe their common features as well as shortcomings, and highlight interesting “outliers”. Methods: We performed a comprehensive literature research and quantitative analysis of papers published between 2016 and 2018. Quantitative information about the number of subjects, studies with healthy volunteers vs. pathological conditions, public datasets, laboratory vs. real-world works, types of camera, usage of machine learning, and spectral properties of data was extracted. Moreover, a qualitative analysis of illumination used and recent advantages in terms of algorithmic developments was also performed. Results: Since 2016, 116 papers were published on camera-based vital sign estimation and 59% of papers presented results on 20 or fewer subjects. While the average number of participants increased from 15.7 in 2016 to 22.9 in 2018, the vast majority of papers (n=100) were on healthy subjects. Four public datasets were used in 10 publications. We found 27 papers whose application scenario could be considered a real-world use case, such as monitoring during exercise or driving. These include 16 papers that dealt with non-healthy subjects. The majority of papers (n=61) presented results based on visual, red-green-blue (RGB) information, followed by RGB combined with other parts of the electromagnetic spectrum (n=18), and thermography only (n=12), while other works (n=25) used other mono- or polychromatic non-RGB data. Surprisingly, a minority of publications (n=39) made use of consumer-grade equipment. Lighting conditions were primarily uncontrolled or ambient. While some works focused on specialized aspects such as the removal of vital sign information from video streams to protect privacy or the influence of video compression, most algorithmic developments were related to three areas: region of interest selection, tracking, or extraction of a one-dimensional signal. Seven papers used deep learning techniques, 17 papers used other machine learning approaches, and 92 made no explicit use of machine learning. Conclusion: Although some general trends and frequent shortcomings are obvious, the spectrum of publications related to camera-based vital sign estimation is broad. While many creative solutions and unique approaches exist, the lack of standardization hinders comparability of these techniques and of their performance. We believe that sharing algorithms and/ or datasets will alleviate this and would allow the application of newer techniques such as deep learning.

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Section: Survey Taxonomymentioning

confidence: 99%

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Antink

Lyra²,

Paul

et al. 2019

Yearb Med Inform

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“…In this study we used three different sensors: RGB and IR sensors of a Microsoft Kinect for Xbox One and Chameleon 3 1.3 MP (color and monochrome models), details are provided in Table 8. Although all these CMOS-sensors have moderate characteristics, they are better than sensors of most general-purpose cameras used for iPPG extraction in humans (see, for instance, [25,63]). Still, specialized cameras, like those used in [23,39], could provide more robust and precise iPPG extraction in NHP.…”

Section: Video Acquisition and Region Of Interest Selectionmentioning

confidence: 99%

“…Besides, sufficient frame rate is required to capture heart cycles. For humans the minimal frame rate for iPPG extraction is 15 fps [25], though in most cases 30 fps and higher are used [63]. Since heart rate for rhesus monkeys is almost twice as high as for humans, higher frame rate is required.…”

Section: Video Acquisition and Region Of Interest Selectionmentioning

confidence: 99%

“…For humans, a model taking this into account was suggested in [24], however its stable performance has been only demonstrated for relatively short (several meters) and nearly constant distances from the face to the camera. Nevertheless, recent progress in iPPG extraction from a long distance video [63] and in compensation of the variable illumination angle and intensity [24,69] gives hope that required models might be developed in future.…”

Section: Video Acquisition and Region Of Interest Selectionmentioning

confidence: 99%

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Using imaging photoplethysmography for heart rate estimation in non-human primates

Unakafov

Moeller

Kagan

et al. 2018

Preprint

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For humans and for non-human primates heart rate is a reliable indicator of an individual's current physiological state, with applications ranging from health checks to experimental studies of cognitive and emotional state. In humans, changes in the optical properties of the skin tissue correlated with cardiac cycles (imaging photoplethysmogram, iPPG) allow non-contact estimation of heart rate by its proxy, pulse rate. Yet, there is no established simple and non-invasive technique for pulse rate measurements in awake and behaving animals. Using iPPG, we here demonstrate that pulse rate in rhesus monkeys can be accurately estimated from facial videos. We computed iPPGs from seven color facial videos of three awake head-stabilized rhesus monkeys. Pulse rate estimated from iPPGs was in good agreement with reference data from a pulse-oximeter with error of pulse rate estimation below 5% for 82% of all epochs, and below 10% for 98% of the epochs. We conclude that iPPG allows non-invasive and non-contact estimation of pulse rate in non-human primates, which is useful for physiological studies and can be used toward welfare-assessment of non-human primates in research.

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“…More recent works demonstrated that pulse signal can be recovered from long distances [25] up to 50 m [1] and from images as small as 41 × 30 pixels [14].…”

Section: Introductionmentioning

confidence: 99%

Architectural Tricks for Deep Learning in Remote Photoplethysmography

Kopeliovich

Mironenko

Petrushan

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Architectural improvements are studied for convolutional network performing estimation of heart rate (HR) values on color signal patches. Color signals are time series of color components averaged over facial regions recorded by webcams in two scenarios: Stationary (without motion of a person) and Mixed Motion (different motion patterns of a person). HR estimation problem is addressed as a classification task, where classes correspond to different heart rate values within the admissible range of [40; 125] bpm. Both adding convolutional filtering layers after fully connected layers and involving combined loss function where first component is a cross entropy and second is a squared error between the network output and smoothed one-hot vector, lead to better performance of HR estimation model in Stationary and Mixed Motion scenarios.

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Long-range non-contact imaging photoplethysmography: cardiac pulse wave sensing at a distance

Cited by 12 publications

References 27 publications

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Using imaging photoplethysmography for heart rate estimation in non-human primates

Architectural Tricks for Deep Learning in Remote Photoplethysmography

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