Remote Photoplethysmography: Rarely Considered Factors

Mironenko, Yuriy; Kalinin, Konstantin; Kopeliovich, Mikhail; Petrushan, Mikhail

doi:10.1109/cvprw50498.2020.00156

Cited by 28 publications

(14 citation statements)

References 47 publications

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“…Zhao et al [8], for example, chose a frequency band of [48,300] min −1 (instead of the proposed [30,240] min −1 ), included the energy around the third harmonic in the energy spectrum of the pulse signal (in the original implementation, only the energy around the first and the second harmonic are included), and used the same spectral window length for calculating energies around the first, second, and third harmonic (in [21], 5-and 10-bins-long windows around the first and second harmonic were used, respectively). In the case of calculating RMSE, the length of the processing window plays an important role when interpreting the results (differences of up to 10% may occur for the window lengths from 0 to 60 s [41]). The described issue results from the already exposed drawback of the rPPG research, i.e., the lack of standardized methodology [42].…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step

2021

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Traditionally, the first step in physiological measurements based on remote photoplethysmography (rPPG) is localizing the region of interest (ROI) that contains a desired pulsatile information. Recently, approaches that do not require this step have been proposed. The purpose of this study was to evaluate the performance of selected approaches with and without ROI localization step in rPPG signal extraction. The Viola-Jones face detector and Kanade–Lucas–Tomasi tracker (VK) in combination with (a) a region-of-interest (ROI) cropping, (b) facial landmarks, (c) skin-color segmentation, and (d) skin detection based on maximization of mutual information and an approach without ROI localization step (Full Video Pulse (FVP)) were studied. Final rPPG signals were extracted using selected model-based and data-driven rPPG algorithms. The performance of the approaches was tested on three publicly available data sets offering compressed and uncompressed video recordings covering various scenarios. The success rates of pulse waveform signal extraction range from 88.37% (VK with skin-color segmentation) to 100% (FVP). In challenging scenarios (skin tone, lighting conditions, exercise), there were no statistically significant differences between the studied approaches in terms of SNR. The best overall performance in terms of RMSE was achieved by a combination of VK with ROI cropping and the model-based rPPG algorithm. Results indicate that the selection of the ROI localization approach does not significantly affect rPPG measurements if combined with a robust algorithm for rPPG signal extraction.

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Section: Discussionmentioning

confidence: 99%

Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step

2021

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“…In this study, we only compared four RPPG algorithms,based on traditional signal processing, deterministic methods. Future studies could incorporate more algorithms, including deep learning-based processing ones, as noted by Mironenko et al in [14]. Therefore, an important research direction is the incorporation of an adaptive sliding window size, to more efficiently allocate computing resources, specially in more complicated environmental settings, while maximizing signal quality.…”

Section: Discussionmentioning

confidence: 99%

Parametric Study of Performance of Remote Photopletysmography System

Rios

Lai

Yan

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Remote photoplethysmography (RPPG) is a technique in which we measure sub-cutaneous variations in blood flow, usually through a camera, to obtain physiological signals.Studies involving RPPG have increased in the past few years due to its numerous applications including remote healthcare, antispoofing, among others. While there have been many studies on how to increase RPPG's accuracy of bio-markers predictions in a variety of settings, most of them are usually done using workstation computers, yet some of the most promising applications of RPPG probably would be on limited resources, low-power embedded systems. Therefore, we did an extensive study on the effects of one of the most important design parameters in RPPG systems, sliding window (SW) size, for a variety of algorithms, in order to quantify the trade-off between computational cost in time and accuracy in root-mean-squared-error (RMSE), using a standardized public database. We also studied how different face detection and region-of-interest selection affected these results. Finally, based on these, we came up with a new and simple metric that takes into account both computation and accuracy, as a means to design dynamic systems which make the best out of the available resources With correct tuning, we can use this metric to reduce computational costs by up to 47%.

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“…Since the diffused component of incident light carries information on subtle blood flow volume changes under human skin, it is possible to analyze that component to find the small light variations in blood flow so as to obtain live data of vital signs such as the heart rate [ 53 ], respiration rate [ 200 ], blood pressure [ 201 , 202 ], and oxygen saturation [ 200 ]. However, remote BP prediction also has susceptibilities in handling noise and artifacts unrelated to the hemoglobin signals, such as makeup [ 203 ], skin tone [ 204 , 205 ], illumination [ 206 , 207 ], camera distance [ 208 , 209 ], camera specification [ 210 , 211 ], and subject motion [ 212 , 213 ]. Considering the pros and cons of using rPPG signals, several remote techniques have been developed to manipulate deficiencies using a sophisticated solution to estiTNLmate BP from the rPPG signal.…”

Section: Contactless Bp Measurement From Rppg Signalsmentioning

confidence: 99%

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

et al. 2022

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Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

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Remote Photoplethysmography: Rarely Considered Factors

Cited by 28 publications

References 47 publications

Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step

Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step

Parametric Study of Performance of Remote Photopletysmography System

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

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