Heart rate estimation using facial video: A review

Hassan, Mohamed Abul; Malik, Aamir Saeed; Fofi, David; Saad, Mohamad Naufal Mohamad; Karasfi, Babak; Ali, Yasir Salih; Mériaudeau, Fabrice

doi:10.1016/j.bspc.2017.07.004

Cited by 149 publications

(82 citation statements)

References 26 publications

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“…The source signal which corresponds closest with a PPG signal is used to measure the heart rate, which somewhat limits the utility, since the heart rate estimation must be seeded with a known heart rate in the first place. A detailed review of many other heart rate estimation methods is given in (29).…”

Section: B Cyanosis Detectionmentioning

confidence: 99%

AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

Hegde

Jiang

Suresha

et al. 2020

Preprint

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With the recent COVID-19 pandemic, healthcare systems all over the world are struggling to manage the massive increase in emergency department (ED) visits. This has put an enormous demand on medical professionals. Increased wait times in the ED increases the risk of infection transmission. In this work we present an open-source, low cost, off-body system to assist in the automatic triage of patients in the ED based on widely available hardware. The system initially focuses on two symptoms of the infection -fever and cyanosis. The use of visible and far-infrared cameras allows for rapid assessment at a 1m distance, thus reducing the load on medical staff and lowering the risk of spreading the infection within hospitals. Its utility can be extended to a general clinical setting in non-emergency times as well to reduce wait time, channel the time and effort of healthcare professionals to more critical tasks and also prioritize severe cases.Our system consists of a Raspberry Pi 4, a Google Coral USB accelerator, a Raspberry Pi Camera v2 and a FLIR Lepton 3.5 Radiometry Long-Wave Infrared Camera with an associated IO module. Algorithms running in real-time detect the presence and body parts of individual(s) in view, and segments out the forehead and lip regions using PoseNet. The temperature of the forehead-eye area is estimated from the infrared camera image and cyanosis is assessed from the image of the lips in the visible spectrum. In our preliminary experiments, an accuracy of 97% was achieved for detecting fever and 77% for the detection of cyanosis, with a sensitivity of 91% and area under the receiver operating characteristic curve of 0.91. Heart rate and respiratory effort are also estimated from the visible camera.Although preliminary results are promising, we note that the entire system needs to be optimized before use and assessed for efficacy. The use of low-cost instrumentation will not produce temperature readings and identification of cyanosis that is acceptable in many situations. For this reason, we are releasing the full code stack and system design to allow others to rapidly iterate and improve the system. This may be of particular benefit in low-resource settings, and low-to-middle income countries in particular, which are just beginning to be affected by COVID-19.

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Section: B Cyanosis Detectionmentioning

confidence: 99%

AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

Hegde

Jiang

Suresha

et al. 2020

Preprint

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“…Signal processing based rPPG methods Since the early work of Verkruysse et al [31], who showed that heart rate could be measured from recordings from a consumer grade camera in ambient light, a large body of research has been conducted on the topic. Extensive reviews of these work can be found in [9,19,26]. Most published rPPG methods work either by applying skin detection on a certain area in each frame or by selecting one or multiple regions of interest and track their averages over time to generate colour signals.…”

Section: Related Workmentioning

confidence: 99%

“…The process of rPPG essentially involves two steps: detecting and tracking the skin colour changes of the subject, and analysing this signal to compute measures like heart rate, heart rate variability and respiration rate. Recent advances in computer video, signal processing, and machine learning have improved the performances of rPPG techniques significantly [9]. Current state-of-the-art methods are able to leverage image processing by deep neural networks to robustly select skin pixels within an image and perform HR estimation [4,22].…”

Section: Introductionmentioning

confidence: 99%

Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability

Gudi

Bittner

Lochmans

et al. 2019

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

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Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rate more accurately, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This method requires no rPPG specific training and is able to operate in real-time. We validate our method on a self-recorded dataset under an idealized lab setting, and show state-of-the-art results on two public dataset with realistic conditions (VicarPPG and PURE).

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“…Remote heart rate measuring methods have grown rapidly, and many methods using different mediums have emerged. The technological maturity along with the reliability of each of the methods are discussed in detail in [9,13].…”

Section: Introductionmentioning

confidence: 99%

“…The mechanical motion contributes to a microscopic displacement of the head/face or facial skin [18,19]. Many digital camera based heart rate measuring methods have been proposed using these two principles; a comprehensive survey and review on the details of the methods can be found in [20,13].…”

Section: Introductionmentioning

confidence: 99%

Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

et al. 2020

Self Cite

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a b s t r a c tThe paper presents a feasibility study for heart rate measurement using a digital camera to perform health monitoring. The feasibility study investigates the reliability of the state of the art heart rate measuring methods in realistic situations. Therefore, an experiment was designed and carried out on 45 subjects to investigate the effects caused by illumination, motion, skin tone, and distance variance. The experiment was conducted for two main scenarios; human-computer interaction scenario and health monitoring scenario. The human-computer scenario investigated the effects caused by illumination variance, motion variance, and skin tone variance. The health monitoring scenario investigates the feasibility of health monitoring at public spaces (i.e. airports, subways, malls). Five state of the art heart rate measuring methods were re-implemented and tested with the feasibility study database. The results were compared with ground truth to estimate the heart rate measurement error. The heart rate measurement error was analyzed using mean error, standard deviation; root means square error and Pearson correlation coefficient. The findings of this experiment inferred promising results for health monitoring of subjects standing at a distance of 500 cm.

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Heart rate estimation using facial video: A review

Cited by 149 publications

References 26 publications

AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability

Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

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