Contactless heart rate estimation from face videos

Lamba, Puneet Singh; Virmani, Deepali

doi:10.1080/09720510.2020.1799584

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…The ROI used to extract the HR and other related cardiac parameters is typical the facial region. In more detail, different areas can be used as ROI, like forehead, left cheek, right cheek, or the whole face (Favilla et al, 2019;Lamba and Virmani, 2020;Shoushan et al, 2021). Moreover, other studies investigated alternative regions of the skin exposed to light like the palm hand (Cennini et al, 2010;Sun et al, 2012;Sereevoravitgul and Kondo, 2014;Lu et al, 2018;van der Kooij and Naber, 2019).…”

Section: Heart Rate and Other Cardiac Parametersmentioning

confidence: 99%

“…Most studies investigate the capabilities of camera-based technologies for monitoring cardiac activity and estimating HR in indoor environments to simulate different scenarios in which these technologies could be applied (Balakrishnan et al, 2013;Tasli et al, 2014;Lamba and Virmani, 2020;Maji et al, 2020;Song et al, 2020;Shoushan et al, 2021). In few studies, experiments to simulate training sessions (e.g., running and cycling) have been performed (Sun, 2011;Tasli et al, 2014), and other studies investigate the potentiality of applying non-contact technologies in the automotive field (Zhang et al, 2014;Huan Qi, 2015;Kuo et al, 2015).…”

Section: Heart Rate and Other Cardiac Parametersmentioning

confidence: 99%

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Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

et al. 2022

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The measurement of physiological parameters is fundamental to assess the health status of an individual. The contactless monitoring of vital signs may provide benefits in various fields of application, from healthcare and clinical setting to occupational and sports scenarios. Recent research has been focused on the potentiality of camera-based systems working in the visible range (380–750 nm) for estimating vital signs by capturing subtle color changes or motions caused by physiological activities but invisible to human eyes. These quantities are typically extracted from videos framing some exposed body areas (e.g., face, torso, and hands) with adequate post-processing algorithms. In this review, we provided an overview of the physiological and technical aspects behind the estimation of vital signs like respiratory rate, heart rate, blood oxygen saturation, and blood pressure from digital images as well as the potential fields of application of these technologies. Per each vital sign, we provided the rationale for the measurement, a classification of the different techniques implemented for post-processing the original videos, and the main results obtained during various applications or in validation studies. The available evidence supports the premise of digital cameras as an unobtrusive and easy-to-use technology for physiological signs monitoring. Further research is needed to promote the advancements of the technology, allowing its application in a wide range of population and everyday life, fostering a biometrical holistic of the human body (BHOHB) approach.

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Section: Heart Rate and Other Cardiac Parametersmentioning

confidence: 99%

Section: Heart Rate and Other Cardiac Parametersmentioning

confidence: 99%

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

et al. 2022

View full text Add to dashboard Cite

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“…In this way, the raw signals for each ROI are obtained:

with i = 1,..., N, where N is the number of frames. We then proceed with the conditioning of the raw signals, which involves two steps: (i) raw r-PPG signals detrending [ 18 , 44 , 45 ] and (ii) smoothness prior approach (SPA) [ 10 , 15 , 46 , 47 , 48 , 49 ]. At this point, for each ROI, we obtain a three-dimensional signal in time, where the three RGB channels represent the three dimensions.…”

Section: Measuring System: Description and Working Principlementioning

confidence: 99%

“…The green channel analysis is the most used method to derive plethysmographic signals from the analysis of RGB camera recorded videos [ 14 , 18 , 50 , 51 , 52 ]. As demonstrated in the study proposed by Verkruysse and colleagues [ 53 ] in 2008, the green channel offers the best signal quality in terms of signal-to-noise ratio (SNR) and accuracy in HR estimation, whereas red and blue are close to the noise value.…”

Section: Measuring System: Description and Working Principlementioning

confidence: 99%

“…Among these factors, user–camera distance, the lighting source, and the selected ROI can affect the quality of the r-PPG waveform and thus the accuracy in the estimation of HR [ 9 , 11 , 16 , 17 ]. The ROI selection for HR estimation is a debated issue, single or multiple ROIs in the facial region [ 18 , 19 ] or the palm hand [ 20 ] can be employed, but performances are typically dependent on the specific application and recording environment [ 18 , 19 ]. Conversely, motion-based methods rely on the detection of small amplitude movements recorded by a video camera, such as chest wall movements due to the breathing activity or mechanical movement of the head caused by the flow of the blood from the heart to the head (called remote ballistocardiography—r-BCG) [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras

Molinaro

Schena

Silvestri

et al. 2022

Sensors

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Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone’s camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths · min−1 for fR in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.

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Remotephotoplethysmography‐basedhuman vital sign prediction using cyclical algorithm

Gupta,

Sinhal,

Badhiye

2023

Journal of Biophotonics

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This article aims to predict vital signs like heart rate, respiration rate, and arterial oxygen saturation using ambient light video, eliminating chronic distortions through improved frame quality with BER estimation. The study employs the Cascade Residual CNN‐FPNR technique for pre‐processing and SNR enhancement using Energy Variance Maximization. The Image Cascade Network (ICNet) facilitates segmentation, achieving strong segmentation in low‐light ambient videos. Remote Photoplethysmography (iPPG) enables non‐contact vital sign monitoring, predicting Heart Rate (HR) and Respiratory Rate (RR). An innovative non‐invasive temperature and cyclical algorithm, incorporating principal component analysis and fast Fourier transform, evaluate patient HR and RR. To address challenges related to involuntary movements, a dynamic time‐warping‐based optimization method is used for precise region selection. The study introduces an intensity variance‐based threshold analysis for arterial oxygen saturation level determination. Ultimately, the Support Vector Machine (SVM) classification technique evaluates the ground truth, showcasing the system's promising potential for remote and accurate vital sign assessment.This article is protected by copyright. All rights reserved.

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Contactless heart rate estimation from face videos

Cited by 7 publications

References 20 publications

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras

Remotephotoplethysmography‐basedhuman vital sign prediction using cyclical algorithm

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