Real-Time Temporal Superpixels for Unsupervised Remote Photoplethysmography

Bobbia, Serge; Luguern, Duncan; Benezeth, Yannick; Nakamura, Keisuke; Gómez, Randy; Dubois, Julien

doi:10.1109/cvprw.2018.00182

Cited by 16 publications

(19 citation statements)

References 27 publications

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“…The ROI of reliable sites is the key to extracting physiological parameters based on the rPPG method and directly affects the accuracy of the measured values [ 23 ]. Marnix et al [ 24 ] found that the use of video cameras to collect facial skin tissues is very accurate in calculating heart rate through rPPG, but the measurement of heart rate in the wrist and calf region is not reliable.…”

Section: Related Workmentioning

confidence: 99%

Non-Contact Heart Rate Detection When Face Information Is Missing during Online Learning

Zheng

Cui

et al. 2020

Sensors

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Research shows that physiological signals can provide objective data support for the analysis of human emotions. At present, non-contact heart rate data have been employed in the research of medicine, intelligent transportation, smart education, etc. However, it is hard to detect heart rate data using non-contact traditional methods during head rotation, especially when face information is missing in scenarios such as online teaching/learning. Traditional remote photoplethysmography (rPPG) methods require a static, full frontal face within a fixed distance for heart rate detection. These strict requirements make it impractical to measure heart rate data in real-world scenarios, as a lot of videos only partially record the subjects’ face information, such as profile, too small distance, and wearing a mask. The current algorithm aims to solve the problem of head deflections between 30 degrees and 45 degrees by employing a symmetry substitution method, which can replace the undetected region of interest (ROI) with the detectable one. When face information is partially missing, our algorithm uses face–eye location to determine ROI. The results show that the method in this paper can solve certain practical problems related to heart rate detection, with a root mean square error (RMSE) under 7.64 bpm.

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Section: Related Workmentioning

confidence: 99%

Non-Contact Heart Rate Detection When Face Information Is Missing during Online Learning

Zheng

Cui

et al. 2020

Sensors

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“…ROI selection and tracking 27 28 29 . Using convolutional neural networks, Chaichulee et al were able to detect patients and select skin regions from NICU recordings 50 .…”

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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“…The algorithm can be decomposed into four main steps. First, the input video frames are decomposed into several temporal superpixels using the IBIS method [1]. The segmentation step is performed by implicitly identifying the superpixel boundaries.…”

Section: Unsupervised Rppg Frameworkmentioning

confidence: 99%

“…5. The UBFC-RPPG database is made publicly available along with the ground truth data from the pulse oximeter for rPPG measurement analysis 1 .…”

Section: Snr K=500mentioning

confidence: 99%

“…Usually, ROI segmentation is based on the result of classical face detection [5] and tracking algorithms and possibly refined with skin pixel classification [9], [20]. As an alternative to this pipeline approach, data-driven methods exploit the pulse-signal as a feature to segment the ROI in an unsupervised manner using voxels [9] or temporal superpixels [1], [3] video segmentation. They are called unsupervised rPPG methods to emphasize the difference with methods that require a trained classifier to determine the ROI.…”

Section: Introductionmentioning

confidence: 99%

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