Blood Volume Pulse Extraction for Non-Contact Heart Rate Measurement by Digital Camera Using Singular Value Decomposition and Burg Algorithm

Tayibnapis, Iman Rahmansyah; Yang, Yeon-Mo; Lim, Ki Moo

doi:10.3390/en11051076

Cited by 6 publications

(4 citation statements)

References 23 publications

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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%

“…Both HR and f R can be estimated from the r-PPG signal, as the respiratory activity modulates the cardiac activity [ 8 ]. When the r-PPG technique is used to retrieve information about the pulsatile activity through a video recording of a subject’s face, different factors should be considered, including the user–camera distance [ 9 , 10 ], the lighting source [ 11 , 12 ], the resolution of the video acquisition system [ 13 ], and the region of interest (ROI) [ 14 , 15 ]. 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 ].…”

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

Section: Measuring System: Description and Working Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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show abstract

“…This as-sumption requires analyzing each IC as a potential candidate for the BVP signal while also requiring apriori knowledge. Moreover, there is no defined criterion for selecting the BVP signal from the independent components from different color channels [27]. Conventionally, BVP signal extraction includes selecting the component with the highest periodicity, which may result in selecting the incorrect IC as a BVP signal in the case of periodic motions by the subjects [11].…”

Section: Introductionmentioning

confidence: 99%

A Motion and Illumination Resistant Non-Contact Method Using Undercomplete Independent Component Analysis and Levenberg-Marquardt Algorithm

Gupta

Ravelo-García

Morgado‐Dias

2022

IEEE J. Biomed. Health Inform.

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Heart Rate (HR) estimation is of utmost importance due to its applicability in diverse fields. Conventional methods for HR estimation require skin contact and are not suitable in certain scenarios such as sensitive skin or prolonged unobtrusive HR monitoring. Therefore remote photoplethysmography (rPPG) methods have become 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 utilized a single color channel or weighted color differences, which has certain limitations dealing with motion and illumination artifacts. This study considered BVP extraction as an undercomplete problem and proposed a method resistant to motion and illumination variation artifacts. This method is based on an undercomplete independent component analysis, aiming to estimate the unmixing matrix using a non-linear Cumulative Density Function (CDF) that has been optimized using the customized Levenberg-Marquardt algorithm. Therefore, the method is named U-LMA. 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 outstanding performance of the proposed U-LMA. Furthermore, its comparative analysis with the state-of-theart methods demonstrated its efficacy and reliability, which was proven by 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.

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“…This assumption requires analyzing each IC as a potential candidate for the BVP signal and requires apriori knowledge. Moreover, there is no de-fined criterion for selecting the BVP signal from the independent components from different color channels [27]. Conventionally, BVP signal extraction includes selecting the component with the highest periodicity, which may result in selecting the wrong IC as BVP signal in case of periodic motions by the subjects [11].…”

Section: Introductionmentioning

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

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<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>

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Blood Volume Pulse Extraction for Non-Contact Heart Rate Measurement by Digital Camera Using Singular Value Decomposition and Burg Algorithm

Cited by 6 publications

References 23 publications

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

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

A Motion and Illumination Resistant Non-Contact Method Using Undercomplete Independent Component Analysis and Levenberg-Marquardt Algorithm

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

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