Noncontact measurement of heart rate using facial video illuminated under natural light and signal weighted analysis

Yan, Yunhui; Ma, Xiang; Yao, Lifeng; Ouyang, Jun

doi:10.3233/bme-151383

Cited by 27 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the motion-based techniques such as active sonar [ 32 ] are sensitive to the significant body movement. Some investigators applied the RGB camera to estimate the BR and HR under the natural lighting conditions [ 14 , 33 – 35 ], but these system would not work when the subject slept during night. Hence, the state-of-art multi-camera system was developed for simultaneously measuring the BR and HR [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

“…Under a certain illumination, subtle color changes caused by blood circulation allow us to detect HR through the camera [ 13 ]. Yan et al, took advantage of this point to measure HR from facial RGB video illuminated by natural light [ 14 ]. Although some investigators developed the filters for eliminating the effects of illumination variations, this is still a highly illumination-dependent solution for HR measurement.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation

Zhai

et al. 2018

PLoS ONE

View full text Add to dashboard Cite

To achieve the simultaneous and unobtrusive breathing rate (BR) and heart rate (HR) measurements during nighttime, we leverage a far-infrared imager and an infrared camera equipped with IR-Cut lens and an infrared lighting array to develop a dual-camera imaging system. A custom-built cascade face classifier, containing the conventional Adaboost model and fully convolutional network trained by 32K images, was used to detect the face region in registered infrared images. The region of interest (ROI) inclusive of mouth and nose regions was afterwards confirmed by the discriminative regression and coordinate conversions of three selected landmarks. Subsequently, a tracking algorithm based on spatio-temporal context learning was applied for following the ROI in thermal video, and the raw signal was synchronously extracted. Finally, a custom-made time-domain signal analysis approach was developed for the determinations of BR and HR. A dual-mode sleep video database, including the videos obtained under environment where illumination intensity ranged from 0 to 3 Lux, was constructed to evaluate the effectiveness of the proposed system and algorithms. In linear regression analysis, the determination coefficient (R2) of 0.831 had been observed for the measured BR and reference BR, and this value was 0.933 for HR measurement. In addition, the Bland-Altman plots of BR and HR demonstrated that almost all the data points located within their own 95% limits of agreement. Consequently, the overall performance of the proposed technique is acceptable for BR and HR estimations during nighttime.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation

Zhai

et al. 2018

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In other words, it can be considered as a combination of gradient descent and the gauss-newton method depending on the proximity of the current solution to the global minima. The present study customized the conventional LMA by introducing the entropy of cumulative density function (CDF) approximated by a hyperbolic tangent tanh defined in [5] as an optimization function, followed by its maximization for statistical independence. The advantage of approximation using tanh lies in the fact that it introduces processing with higher-order statistics to deal with the non-linearity associated with the optimization problem [47].…”

Section: Customised Levenberg Marquardt Algorithm (Lma)mentioning

confidence: 99%

Motion and Illumination Resistant Facial Video based Heart Rate Estimation Method using Levenberg-Marquardt Algorithm Optimized Undercomplete Independent Component Analysis

Gupta¹,

Ravelo-García²,

Morgado‐Dias³

2021

Preprint

View full text Add to dashboard Cite

<div>Heart Rate (HR) estimation is of utmost need due to its applicability in diverse fields. Conventional methods for HR estimation require skin contact and are not suitable for scenarios such as sensitive skin or prolonged unobtrusive HR monitoring. Therefore remote photoplethysmography (rPPG) methods have been an active area of research. These methods utilize the facial videos acquired using a camera followed by extracting the Blood Volume Pulse (BVP) signal for heart rate calculation. The existing rPPG methods either used a single color channel or weighted color differences, which has limitations dealing with motion and illumination artifacts. This study considers BVP extraction as an undercomplete problem and proposed a method U-LMA. First, a non-linear Cumulative Density Function (CDF) approximated by a hyperbolic tangent (tanh) was used to deal with the non-linearity associated with rigid and non-rigid motions and illumination variations. Then, the entropy of the proposed non-linear CDF was optimized using a customized LMA for BVP signal extraction, followed by maximum peak estimation for HR calculation. The performance of the proposed method was tested under three scenarios: constrained, motion, and illumination variations scenarios. High Pearson correlation coefficient values and smaller lower-upper statistical limits of bland-altman plots , justified the good performance of the U-LMA. Comparative analysis of U-LMA with undercomplete ICA with negentropy (U-neg) and other state-of-the-art methods demonstrated its best performance of U-LMA by achieving the lowest error and highest correlation values (0.01 significance level) . Additionally, higher accuracy satisfying the clinically accepted error differences also justified its clinical relevance.</div>

show abstract

“…De Hann et al developed more robust methods to the motion artifacts using chrominance features from the RGB spectrum [4] and using the Hulsbusch noise-free spectrum model [5]. Yan et al [6] proposed an enhanced method to maximize the SNR by taking the weighted average of each color component of the RGB spectrum. Although the remote PPG has improved the issues of the SNR and motion artifacts, they have still presented limited ability to be applied in daily life due to the sensitivity for illumination variance.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Measurement of Heart Rate from Ballistocardiographic Head Movements Using Unsupervised Clustering

Lee

Cho

Lee

et al. 2019

Sensors

View full text Add to dashboard Cite

Heart rate has been measured comfortably using a camera without the skin-contact by the development of vision-based measurement. Despite the potential of the vision-based measurement, it has still presented limited ability due to the noise of illumination variance and motion artifacts. Remote ballistocardiography (BCG) was used to estimate heart rate from the ballistocardiographic head movements generated by the flow of blood through the carotid arteries. It was robust to illumination variance but still limited in the motion artifacts such as facial expressions and voluntary head motions. Recent studies on remote BCG focus on the improvement of signal extraction by minimizing the motion artifacts. They simply estimated the heart rate from the cardiac signal using peak detection and fast fourier transform (FFT). However, the heart rate estimation based on peak detection and FFT depend on the robust signal estimation. Thus, if the cardiac signal is contaminated with some noise, the heart rate cannot be estimated accurately. This study aimed to develop a novel method to improve heart rate estimation from ballistocardiographic head movements using the unsupervised clustering. First, the ballistocardiographic head movements were measured from facial video by detecting facial points using the good-feature-to-track (GFTT) algorithm and by tracking using the Kanade–Lucas–Tomasi (KLT) tracker. Second, the cardiac signal was extracted from the ballistocardiographic head movements by bandpass filter and principal component analysis (PCA). The relative power density (RPD) was extracted from its power spectrum between 0.75 Hz and 2.5 Hz. Third, the unsupervised clustering was performed to construct a model to estimate the heart rate from the RPD using the dataset consisting of the RPD and the heart rate measured from electrocardiogram (ECG). Finally, the heart rate was estimated from the RPD using the model. The proposed method was verified by comparing it with previous methods using the peak detection and the FFT. As a result, the proposed method estimated a more accurate heart rate than previous methods in three experiments by levels of the motion artifacts consisting of facial expressions and voluntary head motions. The four main contributions are as follows: (1) the unsupervised clustering improved the heart rate estimation by overcoming the motion artifacts (i.e., facial expressions and voluntary head motions); (2) the proposed method was verified by comparing with the previous methods using the peak detection and the FFT; (3) the proposed method can be combined with existing vision-based measurement and can improve their performance; (4) the proposed method was tested by three experiments considering the realistic environment including the motion artifacts, thus, it increases the possibility of the non-contact measurement in daily life.

show abstract

Noncontact measurement of heart rate using facial video illuminated under natural light and signal weighted analysis

Cited by 27 publications

References 15 publications

Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation

Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation

Motion and Illumination Resistant Facial Video based Heart Rate Estimation Method using Levenberg-Marquardt Algorithm Optimized Undercomplete Independent Component Analysis

Vision-Based Measurement of Heart Rate from Ballistocardiographic Head Movements Using Unsupervised Clustering

Contact Info

Product

Resources

About