This paper presents an approach for pulse rate extraction from videos. The core of the presented approach is a novel method to segment and track a suitable region of interest (ROI). The proposed method combines level sets with subject-individual Gaussian Mixture Models to yield a time varying ROI. The ROI builds up from multiple homogeneous skin areas under constraints regarding the area and contour length of the ROI. Together with state of the art signal processing methods our approach yields an Mean Average Error (MAE) of 2.3 bpm, 1.4 bpm and 2.7 bpm on own data, the PURE database and the UBFC-rPPG database, respectively. Therewith, our method performs equal or better compared to widely used approaches (e.g. the KLT tracker instead of the proposed image processing yields an MAE of 2.6 bpm, 2.6 bpm and 4.4 bpm). Such results and the 2nd place with a MAE of 7.92 bpm in the 1st Challenge on Remote Physiological Signal Sensing prove the applicability of the proposed method. The taken approach, however, bears further potential for optimization in the context of photoplethysmography imaging and should be transferable to other segmentation tasks as well.

Adaptive Gaussian Mixture Model Driven Level Set Segmentation for Remote Pulse Rate Detection

Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still not straightforward. The best algorithm selection improves the signal-to-noise ratio (SNR) and therefore improves the accuracy of the recognition and classification of vital signs. We recorded more than 300 temporal rPPG signals where the noise was not motion-induced. Then, we investigated the best digital filter in pre-processing temporal rPPG data and compared the performances of 10 filters with 10 orders each (i.e., a total of 100 filters). The performances are assessed using a signal quality metric on three levels. The quality of the raw signals was classified under three categories; Q1 being the best and Q3 being the worst. The results are presented in SNR scores, which show that the Chebyshev II orders of 2nd, 4th, and 6th perform the best for denoising rPPG signals.

Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

In recent years, several approaches for Photoplethysmography Imaging have been proposed. However, the so far proposed approaches have been evaluated either on non-public datasets or on such proposed for different research scope. Therefore, we present VitalCamSet, a new dataset for PPGI allowing comparative evaluation of different algorithmic approaches. The VitalCamSet contains videos of 26 subjects with 10 different scenarios and a length of two minutes each, recorded simultaneously by a color camera and a monochrome near-infrared camera. The video data was synchronized using the cross correlation of LED signals, captured in the videos, as well as in the light sensor of the vital sign reference system. In addition, signal quality of the videos was analyzed using a SNR (Signal-to-Noise-Ratio) metric. The dataset is made publicly available and we encourage PPGI researchers to use it for testing their developed PPGI approaches.

VitalCamSet - a dataset for Photoplethysmography Imaging

Camera-based measurement of vital signs utilizes imaging equipment to measure physiological changes by analyzing human body images. The contactless physiological measurement provides benefits across various fields, from healthcare and clinical trial to telemedicine and exercise scenarios. On the basis of advances in medicine, optics, and computer vision, these technologies have made significant progress. Research in this area has grown exponentially in recent years. In this review, we provide a comprehensive and meticulous overview of camera-based measurements of physiological vital signs, including four aspects: data acquisition, methodology, vital signs and applications. It covers the measurement of heart rate, heart rate variability, respiratory rate, blood pressure, and oxygen saturation, using various technologies including motion detection, signal processing, and deep learning. We also present newly released datasets and applications in new areas. At last, future promising prospects and potential research directions are also discussed. 

Camera-based physiological measurement: Recent advances and future prospects

Fetal ECG extraction from abdominal ECG is critical task for telemonitoring of fetus which require lot of understanding to the subject. Conventional source separation methods are not efficient enough to separate FECG from huge multichannel ECG. Thus use of compression technique is needed to compress and reconstruct ECG signal without any significant losses in quality of signal. Compressed sensing shows promising results for such tasks. However, current compressed sensing theory is not so far that successful due to the non-sparsity and strong noise contamination present in ECG signal. The proposed work explores the concept of block compressed sensing to reconstruct non-sparse FECG signal using GFOCUSS algorithm. The main objective of this paper is not only to successfully reconstruct the ECG signal but to efficiently separate FECG from abdominal ECG. The proposed algorithm is explained in very extensive manner for all experiments. The key feature of proposed method is, that it doesn’t affect the interdependence relation between multichannel ECG. The useof walsh sensing matrix made it possible to achieve high compression ratio. Experimental results shows that even at very high compression ratio, successful FECG reconstruction from raw ECG is possible. These results are validated using PSNR, SINR, and MSE. This shows the framework, compared to other algorithms such as current blocking CS algorithms, rackness CS algorithm and wavelet algorithms, can greatly reduce code execution time during data compression stage and achieve better reconstruction in terms of MSE, PSNR and SINR.

/pdf/modified-block-compressed-sensing-for-extraction-of-fetal-868ninu1le.pdf

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Photoplethysmography Imaging (PPGI) in the sense of remote vital sign measurement via camera has attracted high interest in recent years. The non-contact measurement principle allows the use in many health monitoring applications, like monitoring of newborns. Beyond that, there are interesting areas of application in the multimedia sector, such as measuring the reaction to multimedia content or heart rate based liveness detection for multimedia security. The derived signal of a PPGI algorithm is often referred as blood volume pulse signal (BVP). The signal corresponds to the optical signal of blood volume changes in the upper skin layers. Most current approaches use peak detection in frequency spectrum to estimate heart rate from BVP signals. However, we focus on heart rate computation based on beat-to-beat peak detection in time domain. In this paper, we present a method for adaptive bandpass filtering for PPGI based on temporal spectrogram analysis of the BVP signal with a sliding time window. The main goal of this new method is to further improve accuracy of beat-to-beat peak detection in time domain. The approach exploits the analysis of main frequency components of the BVP signal over time, to build a bandpass filter with adaptive cutoff frequencies in order to filter noise and interference. So far, state-of-the-art approaches have usually used fixed cut-off frequencies in the physiologically possible range of heart rate. The novelty of the proposed method lies in its simple but effective solution to reduce the influence of noise and interference in the PPGI signal to improve peak detection for heart rate estimation. We show the improvements applying the adaptive bandpass filter technique to four basic algorithmic approaches of PPGI, namely ICA, Chrominance, POS and 2SR and comparing against current state-of-the-art peak detection approaches. For the evaluation we used a database with videos of 26 subjects in 4 different scenarios, each lasting two minutes.

An Adaptive Bandpass Filter Based on Temporal Spectrogram Analysis for Photoplethysmography Imaging

Recently, deep neural networks have been applied for rPPG measurement and achieved promising measurement performance compared to conventional rPPG algorithms. However, the designing of the deep network approaches has been independent of the conventional methods. No one has yet investigated if combining the processing schema of conventional rPPG algorithms could benefit the performance of deep network methods. Thus, we proposed a deep network integrated into the short-window overlap-adding pipeline of the classical model-based rPPG algorithms. The proposed method is evaluated on the large-scale datasets VitcalCamSet and UBFC, and the evaluation results demonstrate that the proposed method has superior performance compared with state-of-the-art approaches.

Short Window Network for Remote Heart Rate Measurement

In recent years, several approaches for remote Photoplethysmography (rPPG) have been proposed, and the recently proposed methods have achieved substantial improvement in measurement accuracy. However, none of the methods has investigated the possibility of using the spectral characteristics for the design of rPPG signal extraction algorithms. In this paper, we propose a new rPPG measurement method which exploits the spectral characteristics of rPPG signals. We validated the freshly proposed method on a benchmark dataset including seven scenarios and 26 participants. The results of the validation experiment demonstrates the feasibility to use spectral characteristics to extract rPPG signal. By combining with the constraint plane, the new proposed method provides better overall performance.

/pdf/enhancing-remote-ppg-pulse-extraction-in-disturbance-4jx8bnja2g.pdf

Enhancing Remote-PPG Pulse Extraction in Disturbance Scenarios Utilizing Spectral Characteristics.

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Papers

VitalCamSet - a dataset for Photoplethysmography Imaging

An Adaptive Bandpass Filter Based on Temporal Spectrogram Analysis for Photoplethysmography Imaging

Short Window Network for Remote Heart Rate Measurement

Enhancing Remote-PPG Pulse Extraction in Disturbance Scenarios Utilizing Spectral Characteristics.