Relevant bibliographies by topics / Remote photoplethysmography

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Remote photoplethysmography'

Author: Grafiati
Published: 13 April 2024

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Journal articles on the topic "Remote photoplethysmography"

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van Gastel, Mark, Sander Stuijk, and Gerard de Haan. "Robust respiration detection from remote photoplethysmography." Biomedical Optics Express 7, no. 12 (November 3, 2016): 4941. http://dx.doi.org/10.1364/boe.7.004941.

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Laurie, Jordan, Niall Higgins, Thierry Peynot, and Jonathan Roberts. "Dedicated Exposure Control for Remote Photoplethysmography." IEEE Access 8 (2020): 116642–52. http://dx.doi.org/10.1109/access.2020.3003548.

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Kim, Seung-Hyun, Su-Min Jeon, and Eui Chul Lee. "Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal." Sensors 22, no. 8 (April 16, 2022): 3070. http://dx.doi.org/10.3390/s22083070.

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Spoofing attacks in face recognition systems are easy because faces are always exposed. Various remote photoplethysmography-based methods to detect face spoofing have been developed. However, they are vulnerable to replay attacks. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that minimizes the susceptibility to certain database dependencies and high-quality replay attacks without additional devices. The proposed method has the following advantages. First, because only an RGB camera is used to detect spoofing attacks, the proposed method is highly usable in various mobile environments. Second, solutions are incorporated in the method to obviate new attack scenarios that have not been previously dealt with. In this study, we propose a remote photoplethysmography-based face recognition spoofing detection method that improves susceptibility to certain database dependencies and high-quality replay attack, which are the limitations of previous methods without additional devices. In the experiment, we also verified the cut-off attack scenario in the jaw and cheek area where the proposed method can be counter-attacked. By using the time series feature and the frequency feature of the remote photoplethysmography signal, it was confirmed that the accuracy of spoof detection was 99.7424%.
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Boccignone, Giuseppe, Donatello Conte, Vittorio Cuculo, Alessandro D’Amelio, Giuliano Grossi, Raffaella Lanzarotti, and Edoardo Mortara. "pyVHR: a Python framework for remote photoplethysmography." PeerJ Computer Science 8 (April 15, 2022): e929. http://dx.doi.org/10.7717/peerj-cs.929.

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Remote photoplethysmography (rPPG) aspires to automatically estimate heart rate (HR) variability from videos in realistic environments. A number of effective methods relying on data-driven, model-based and statistical approaches have emerged in the past two decades. They exhibit increasing ability to estimate the blood volume pulse (BVP) signal upon which BPMs (Beats per Minute) can be estimated. Furthermore, learning-based rPPG methods have been recently proposed. The present pyVHR framework represents a multi-stage pipeline covering the whole process for extracting and analyzing HR fluctuations. It is designed for both theoretical studies and practical applications in contexts where wearable sensors are inconvenient to use. Namely, pyVHR supports either the development, assessment and statistical analysis of novel rPPG methods, either traditional or learning-based, or simply the sound comparison of well-established methods on multiple datasets. It is built up on accelerated Python libraries for video and signal processing as well as equipped with parallel/accelerated ad-hoc procedures paving the way to online processing on a GPU. The whole accelerated process can be safely run in real-time for 30 fps HD videos with an average speedup of around 5. This paper is shaped in the form of a gentle tutorial presentation of the framework.
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Bobbia, Serge, Richard Macwan, Yannick Benezeth, Alamin Mansouri, and Julien Dubois. "Unsupervised skin tissue segmentation for remote photoplethysmography." Pattern Recognition Letters 124 (June 2019): 82–90. http://dx.doi.org/10.1016/j.patrec.2017.10.017.

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Po, Lai-Man, Litong Feng, Yuming Li, Xuyuan Xu, Terence Chun-Ho Cheung, and Kwok-Wai Cheung. "Block-based adaptive ROI for remote photoplethysmography." Multimedia Tools and Applications 77, no. 6 (March 13, 2017): 6503–29. http://dx.doi.org/10.1007/s11042-017-4563-7.

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Peng, Rong-Chao, Wen-Rong Yan, Ning-Ling Zhang, Wan-Hua Lin, Xiao-Lin Zhou, and Yuan-Ting Zhang. "Investigation of Five Algorithms for Selection of the Optimal Region of Interest in Smartphone Photoplethysmography." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/6830152.

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Smartphone photoplethysmography is a newly developed technique that can detect several physiological parameters from the photoplethysmographic signal obtained by the built-in camera of a smartphone. It is simple, low-cost, and easy-to-use, with a great potential to be used in remote medicine and home healthcare service. However, the determination of the optimal region of interest (ROI), which is an important issue for extracting photoplethysmographic signals from the camera video, has not been well studied. We herein proposed five algorithms for ROI selection: variance (VAR), spectral energy ratio (SER), template matching (TM), temporal difference (TD), and gradient (GRAD). Their performances were evaluated by a 50-subject experiment comparing the heart rates measured from the electrocardiogram and those from the smartphone using the five algorithms. The results revealed that the TM and the TD algorithms outperformed the other three as they had less standard error of estimate (<1.5 bpm) and smaller limits of agreement (<3 bpm). The TD algorithm was slightly better than the TM algorithm and more suitable for smartphone applications. These results may be helpful to improve the accuracy of the physiological parameters measurement and to make the smartphone photoplethysmography technique more practical.
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Lee, Kunyoung, Jaemu Oh, Hojoon You, and Eui Chul Lee. "Improving Remote Photoplethysmography Performance through Deep-Learning-Based Real-Time Skin Segmentation Network." Electronics 12, no. 17 (September 4, 2023): 3729. http://dx.doi.org/10.3390/electronics12173729.

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In recent years, health-monitoring systems have become increasingly important in the medical and safety fields, including patient and driver monitoring. Remote photoplethysmography is an approach that captures blood flow changes due to cardiac activity by utilizing a camera to measure transmitted or reflected light through the skin, but it has limitations in its sensitivity to changes in illumination and motion. Moreover, remote photoplethysmography signals measured from nonskin regions are unreliable, leading to inaccurate remote photoplethysmography estimation. In this study, we propose Skin-SegNet, a network that minimizes noise factors and improves pulse signal quality through precise skin segmentation. Skin-SegNet separates skin pixels and nonskin pixels, as well as accessories such as glasses and hair, through training on facial structural elements and skin textures. Additionally, Skin-SegNet reduces model parameters using an information blocking decoder and spatial squeeze module, achieving a fast inference time of 15 ms on an Intel i9 CPU. For verification, we evaluated Skin-SegNet using the PURE dataset, which consists of heart rate measurements from various environments. When compared to other skin segmentation methods with similar inference speeds, Skin-SegNet demonstrated a mean absolute percentage error of 1.18%, showing an improvement of approximately 60% compared to the 4.48% error rate of the other methods. The result even exhibits better performance, with only 0.019 million parameters, in comparison to DeepLabV3+, which has 5.22 million model parameters. Consequently, Skin-SegNet is expected to be employed as an effective preprocessing technique for facilitating efficient remote photoplethysmography on low-spec computing devices.
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Bok, Jin Yeong, Kun Ha Suh, and Eui Chul Lee. "Detecting Fake Finger-Vein Data Using Remote Photoplethysmography." Electronics 8, no. 9 (September 11, 2019): 1016. http://dx.doi.org/10.3390/electronics8091016.

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Today, biometrics is being widely used in various fields. Finger-vein is a type of biometric information and is based on finger-vein patterns unique to each individual. Various spoofing attacks have recently become a threat to biometric systems. A spoofing attack is defined as an unauthorized user attempting to deceive a system by presenting fake samples of registered biometric information. Generally, finger-vein recognition, using blood vessel characteristics inside the skin, is known to be more difficult when producing counterfeit samples than other biometrics, but several spoofing attacks have still been reported. To prevent spoofing attacks, conventional finger-vein recognition systems mainly use the difference in texture information between real and fake images, but such information may appear different depending on the camera. Therefore, we propose a method that can detect forged finger-vein independently of a camera by using remote photoplethysmography. Our main idea is to get the vital sign of arterial blood flow, a biometric measure indicating life. In this paper, we selected the frequency spectrum of time domain signal obtained from a video, as the feature, and then classified data as real or fake using the support vector machine classifier. Consequently, the accuracy of the experimental result was about 96.46%.
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Yu, Su-Gyeong, So-Eui Kim, Na Hye Kim, Kun Ha Suh, and Eui Chul Lee. "Pulse Rate Variability Analysis Using Remote Photoplethysmography Signals." Sensors 21, no. 18 (September 17, 2021): 6241. http://dx.doi.org/10.3390/s21186241.

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Pulse rate variability (PRV) refers to the change in the interval between pulses in the blood volume pulse (BVP) signal acquired using photoplethysmography (PPG). PRV is an indicator of the health status of an individual’s autonomic nervous system. A representative method for measuring BVP is contact PPG (CPPG). CPPG may cause discomfort to a user, because the sensor is attached to the finger for measurements. In contrast, noncontact remote PPG (RPPG) extracts BVP signals from face data using a camera without the need for a sensor. However, because the existing RPPG is a technology that extracts a single pulse rate rather than a continuous BVP signal, it is difficult to extract additional health status indicators. Therefore, in this study, PRV analysis is performed using lab-based RPPG technology that can yield continuous BVP signals. In addition, we intended to confirm that the analysis of PRV via RPPG can be performed with the same quality as analysis via CPPG. The experimental results confirmed that the temporal and frequency parameters of PRV extracted from RPPG and CPPG were similar. In terms of correlation, the PRVs of RPPG and CPPG yielded correlation coefficients between 0.98 and 1.0.
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Dissertations / Theses on the topic "Remote photoplethysmography"

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Soleimani, Vahid. "Remote depth-based photoplethysmography in pulmonary function testing." Thesis, University of Bristol, 2018. http://hdl.handle.net/1983/f6a6f7b6-943f-43f7-b684-1612161aee1a.

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This thesis introduces several novel, noninvasive lung function assessment approaches in which we incorporate computer vision techniques to remotely compute standard clinical Pulmonary Function Testing (PFT) measures. Using a single depth sensor, a dynamic 3-D model of a subject's chest is reconstructed and used to generate chest volume-time data by estimating the chest volume variation throughout a sequence. Following computation of multiple keypoints and calibration of volume-time data to present real volume of exchanged air, 7 Forced Vital Capacity (FVC) measures and 4 Slow Vital Capacity (SVC) measures are computed. Evaluation on a dataset of 85 patients (529 sequences), attending a respiratory outpatient service for spirometry, shows a high correlation between the proposed depth-based PFT measures and the measures from a spirometer. Trunk motion during PFT affects the accuracy of these results, so the natural reaction of the subject's body to maximal inhalation and exhalation, must be decoupled from the chest-surface breathing motion. We present an automatic, open source data acquisition and calibration pipeline in which two opposing depth sensors are calibrated and used to reconstruct a well-defined dynamic 3-D model of the trunk during PFT performance. Our proposed method is able to reconstruct dynamic 3-D models with accurate temporal frame synchronisation and spatial registration. Then, we propose a whole body depth-based photoplethysmography (dPPG) approach which allows subjects to perform PFT, as in routine spirometry, without restraining their natural trunk reactions. By decoupling the trunk movement and the chest-surface respiratory motion, dPPG obtains more accurate respiratory volume-time data which improves the accuracy of the estimated PFT measures. A dataset spanning 35 subjects (298 sequences) was collected and used to illustrate the superiority of the proposed dPPG method by comparing its measures to those provided by a spirometer and the single Kinect approach. Although dPPG is able to improve the PFT measures accuracy to a significant extent, it is not able to filter complex trunk motions, particularly at the deep forced inhalation-exhalation stage. To effectively correct trunk motion artifacts further, we propose an active trunk shape modelling approach by which the respiratory volume-time data is computed by performing principal component analysis on temporal 3-D geometrical features, extracted from the chest and posterior shape models in R3 space. We validate the method's accuracy at the signal level by computing several comparative metrics between the depth-based and spirometer volume-time data. Evaluating on the dPPG PFT dataset (300 PFT sequences), our trunk shape modelling approach outperforms the single Kinect and dPPG methods.
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Botina, Monsalve Deivid. "Remote photoplethysmography measurement and filtering using deep learning based methods." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2022. http://www.theses.fr/2022UBFCK061.

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RPPG est une technique développée pour mesurer le signal du pouls et ensuite estimer les données physiologiques telles que la fréquence cardiaque, la fréquence respiratoire et la variabilité du pouls.En raison des multiples sources de bruit qui détériorent la qualité du signal RPPG, les filtres conventionnels sont couramment utilisés. Cependant, certaines altérations demeurent, alors qu'un œil expérimenté peut facilement les identifier. Dans la première partie de cette thèse, nous proposons le réseau LSTMDF (Long Short-Term Memory Deep-Filter) pour réaliser le filtrage du signal RPPG. Nous utilisons différents protocoles pour analyser les performances de la méthode. Nous démontrons comment le réseau peut être entraîné efficacement avec un nombre limité de signaux. Notre étude démontre expérimentalement la supériorité du filtre basé sur le LSTM par rapport aux filtres conventionnels. Le réseau est ainsi peu sensible rapport signal/bruit moyen des signaux RPPG.Les approches basées sur les réseaux convolutifs tels que les 3DCNN ont récemment surpassé les méthodes manuelles traditionnelles dans la tâche de mesure du RPPG. Cependant, il est connu que les grands modèles 3DCNN ont des coûts de calcul élevés et peuvent être inadaptés aux applications en temps réel. Comme deuxième contribution de cette thèse, nous proposons une étude d'une architecture 3DCNN, trouvant le meilleur compromis entre la précision de la mesure du pouls et le temps d'inférence. Nous utilisons une étude d'ablation où nous diminuons la taille de l'entrée, proposons une fonction de perte personnalisée, et évaluons l'impact de différents espaces de couleur d'entrée. Le résultat est le RPPG en temps réel (RTRPPG), un outil de mesure du RPPG de bout en bout qui peut être utilisé sur GPU et CPU. Nous avons également proposé une méthode d'augmentation des données qui vise à améliorer les performances des réseaux d'apprentissage profond lorsque la base de données présente des caractéristiques spécifiques (par exemple, les mouvements de type fitness) et lorsque les données disponibles sont peu nombreuses
RPPG is a technique developed to measure the blood volume pulse signal and then estimate physiological data such as pulse rate, breathing rate, and pulse rate variability.Due to the multiple sources of noise that deteriorate the quality of the RPPG signal, conventional filters are commonly used. However, some alterations remain, but interestingly, an experienced eye can easily identify them. In the first part of this thesis, we propose the Long Short-Term Memory Deep-Filter (LSTMDF) network in the RPPG filtering task. We use different protocols to analyze the performance of the method. We demonstrate how the network can be efficiently trained with a few signals. Our study demonstrates experimentally the superiority of the LSTM-based filter compared with conventional filters. We found a network sensitivity related to the average signal-to-noise ratio on the RPPG signals.Approaches based on convolutional networks such as 3DCNNs have recently outperformed traditional hand-crafted methods in the RPPG measurement task. However, it is well known that large 3DCNN models have high computational costs and may be unsuitable for real-time applications. As the second contribution of this thesis, we propose a study of a 3DCNN architecture, finding the best compromise between pulse rate measurement precision and inference time. We use an ablation study where we decrease the input size, propose a custom loss function, and evaluate the impact of different input color spaces. The result is the Real-Time RPPG (RTRPPG), an end-to-end RPPG measurement framework that can be used in GPU and CPU. We also proposed a data augmentation method that aims to improve the performance of deep learning networks when the database has specific characteristics (e.g., fitness movement) and when there is not enough data available
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Zaunseder, Sebastian, Alexander Trumpp, Hannes Ernst, Michael Förster, and Hagen Malberg. "Spatio-temporal analysis of blood perfusion by imaging photoplethysmography." SPIE, 2018. https://tud.qucosa.de/id/qucosa%3A35157.

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Imaging photoplethysmography (iPPG) has attracted much attention over the last years. The vast majority of works focuses on methods to reliably extract the heart rate from videos. Only a few works addressed iPPGs ability to exploit spatio-temporal perfusion pattern to derive further diagnostic statements. This work directs at the spatio-temporal analysis of blood perfusion from videos. We present a novel algorithm that bases on the two-dimensional representation of the blood pulsation (perfusion map). The basic idea behind the proposed algorithm consists of a pairwise estimation of time delays between photoplethysmographic signals of spatially separated regions. The probabilistic approach yields a parameter denoted as perfusion speed. We compare the perfusion speed versus two parameters, which assess the strength of blood pulsation (perfusion strength and signal to noise ratio). Preliminary results using video data with different physiological stimuli (cold pressure test, cold face test) show that all measures are in fluenced by those stimuli (some of them with statistical certainty). The perfusion speed turned out to be more sensitive than the other measures in some cases. However, our results also show that the intraindividual stability and interindividual comparability of all used measures remain critical points. This work proves the general feasibility of employing the perfusion speed as novel iPPG quantity. Future studies will address open points like the handling of ballistocardiographic effects and will try to deepen the understanding of the predominant physiological mechanisms and their relation to the algorithmic performance.
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Trumpp, Alexander, Johannes Lohr, Daniel Wedekind, Martin Schmidt, Matthias Burghardt, Axel R. Heller, Hagen Malberg, and Sebastian Zaunseder. "Camera-based photoplethysmography in an intraoperative setting." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234950.

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Background Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment. Methods We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity. Results The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations. Conclusions The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.
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Uggla, Lingvall Kristoffer. "Remote heart rate estimation by evaluating measurements from multiple signals." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210303.

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Heart rate can say a lot about a person's health. While most conventional methods for heart rate measurement require contact with the subject, these are not always applicable. In this thesis, a non-invasive method for pulse detection is implemented and analyzed. Different signals from the color of the forehead—including the green channel, the hue channel and different ICA and PCA components—are inspected, and their resulted heart rates are weighted together according to the significance of their FFT peaks. The system is tested on videos with different difficulties regarding the amount of movement and setting of the scene. The results show that the approach of weighting measurements from different signals together has great potential. The system in this thesis, however, does not perform very well on videos with a lot of movement because of motion noise. Though, with better, less noisy signals, good results can be expected.
En människas puls säger en hel del om dennes hälsa. För att mäta pulsenanvänds vanligtvis metoder som vidrör människan, vilket iblandär en nackdel. I det här examensarbetet tas en metod för pulsmätningpå avstånd fram, som endast använder klipp från en vanlig videokamera. Färgen i pannan mäts och utifrån den genereras flera signalersom analyseras, vilket resulterar i olika mätvärden för pulsen. Genomatt värdera dessa mätvärden med avseende på hur tydliga signalernaär, beräknas ett viktat medelvärde som ett slutgiltigt estimat på medelpulsen. Metoden testas på videoklipp med varierande svårighetsgrad,beroende på hur mycket rörelser som förekommer och på vilketavstånd från kameran försökspersonen står. Resultaten visar att metodenhar mycket god potential och att man kan man förvänta sig finaresultat med bättre, mindre brusiga signaler.
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Ghanadian, Hamideh. "A Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using RGB Camera." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38563.

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Recording and monitoring vital signs is an essential aspect of home-based healthcare. Using contact sensors to record physiological signals can cause discomfort to patients, especially after prolonged use. Hence, remote physiological measurement approaches have attracted considerable attention as they do not require physical contact with the patient’s skin. Several studies proposed techniques to measure Heart Rate (HR) and Heart Rate Variability (HRV) by detecting the Blood Volume Pulse (BVP) from human facial video recordings while the subject is in a resting condition. In this thesis, we focus on the measurement of HR. We adopt an algorithm that uses the Independent Component Analysis (ICA) to separate the source (physiological) signal from noise in the RGB channels of a facial video. We generalize existing methods to support subject movement during video recording. When a subject is moving, the face may be turned away from the camera. We utilize multiple cameras to enable the algorithm to monitor the vital sign continuously, even if the subject leaves the frame or turns away from a subset of the system’s cameras. Furthermore, we improve the accuracy of existing methods by implementing a light equalization scheme to reduce the effect of shadows and unequal facial light on the HR estimation, a machine learning method to select the most accurate channel outputted by the ICA module, and a regression technique to adjust the initial HR estimate. We systematically test our method on eleven subjects using four cameras. The proposed method decreases the RMSE by 27% compared to the state of the art in the rest condition. When the subject is in motion, the proposed method achieves a RMSE of 1.12 bpm using a single camera and RMSE of 0.96 bpm using multiple camera.
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Alghoul, Karim. "Heart Rate Variability Extraction from Video Signals." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33003.

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Heart Rate Variability (HRV) analysis has been garnering attention from researchers due to its wide range of applications. Medical researchers have always been interested in Heart Rate (HR) and HRV analysis, but nowadays, investigators from variety of other fields are also probing the subject. For instance, variation in HR and HRV is connected to emotional arousal. Therefore, knowledge from the fields of affective computing and psychology, can be employed to devise machines that understand the emotional states of humans. Recent advancements in non-contact HR and HRV measurement techniques will likely further boost interest in emotional estimation through . Such measurement methods involve the extraction of the photoplethysmography (PPG) signal from the human's face through a camera. The latest approaches apply Independent Component Analysis (ICA) on the color channels of video recordings to extract a PPG signal. Other investigated methods rely on Eulerian Video Magnification (EVM) to detect subtle changes in skin color associated with PPG. The effectiveness of the EVM in HR estimation has well been established. However, to the best of our knowledge, EVM has not been successfully employed to extract HRV feature from a video of a human face. In contrast, ICA based methods have been successfully used for HRV analysis. As we demonstrate in this thesis, these two approaches for HRV feature extraction are highly sensitive to noise. Hence, when we evaluated them in indoor settings, we obtained mean absolute error in the range of 0.012 and 28.4. Therefore, in this thesis, we present two approaches to minimize the error rate when estimating physiological measurements from recorded facial videos using a standard camera. In our first approach which is based on the EVM method, we succeeded in extracting HRV measurements but we could not get rid of high frequency noise, which resulted in a high error percentage for the result of the High frequency (HF) component. Our second proposed approach solved this issue by applying ICA on the red, green and blue (RGB) colors channels and we were able to achieve lower error rates and less noisy signal as compared to previous related works. This was done by using a Buterworth filter with the subject's specific HR range as its Cut-Off. The methods were tested with 12 subjects from the DISCOVER lab at the University of Ottawa, using artificial lights as the only source of illumination. This made it a challenge for us because artificial light produces HF signals which can interfere with the PPG signal. The final results show that our proposed ICA based method has a mean absolute error (MAE) of 0.006, 0.005, 0.34, 0.57 and 0.419 for the mean HR, mean RR, LF, HF and LF/HF respectively. This approach also shows that these physiological parameters are highly correlated with the results taken from the electrocardiography (ECG).
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Trumpp, Alexander. "Remote Assessment of the Cardiovascular Function Using Camera-Based Photoplethysmography." Doctoral thesis, 2019. https://tud.qucosa.de/id/qucosa%3A36758.

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Camera-based photoplethysmography (cbPPG) is a novel measurement technique that allows the continuous monitoring of vital signs by using common video cameras. In the last decade, the technology has attracted a lot of attention as it is easy to set up, operates remotely, and offers new diagnostic opportunities. Despite the growing interest, cbPPG is not completely established yet and is still primarily the object of research. There are a variety of reasons for this lack of development including that reliable and autonomous hardware setups are missing, that robust processing algorithms are needed, that application fields are still limited, and that it is not completely understood which physiological factors impact the captured signal. In this thesis, these issues will be addressed. A new and innovative measuring system for cbPPG was developed. In the course of three large studies conducted in clinical and non-clinical environments, the system’s great flexibility, autonomy, user-friendliness, and integrability could be successfully proven. Furthermore, it was investigated what value optical polarization filtration adds to cbPPG. The results show that a perpendicular filter setting can significantly enhance the signal quality. In addition, the performed analyses were used to draw conclusions about the origin of cbPPG signals: Blood volume changes are most likely the defining element for the signal's modulation. Besides the hardware-related topics, the software topic was addressed. A new method for the selection of regions of interest (ROIs) in cbPPG videos was developed. Choosing valid ROIs is one of the most important steps in the processing chain of cbPPG software. The new method has the advantage of being fully automated, more independent, and universally applicable. Moreover, it suppresses ballistocardiographic artifacts by utilizing a level-set-based approach. The suitability of the ROI selection method was demonstrated on a large and challenging data set. In the last part of the work, a potentially new application field for cbPPG was explored. It was investigated how cbPPG can be used to assess autonomic reactions of the nervous system at the cutaneous vasculature. The results show that changes in the vasomotor tone, i.e. vasodilation and vasoconstriction, reflect in the pulsation strength of cbPPG signals. These characteristics also shed more light on the origin problem. Similar to the polarization analyses, they support the classic blood volume theory. In conclusion, this thesis tackles relevant issues regarding the application of cbPPG. The proposed solutions pave the way for cbPPG to become an established and widely accepted technology.
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Book chapters on the topic "Remote photoplethysmography"

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Lempe, Georg, Sebastian Zaunseder, Tom Wirthgen, Stephan Zipser, and Hagen Malberg. "ROI Selection for Remote Photoplethysmography." In Bildverarbeitung für die Medizin 2013, 99–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36480-8_19.

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He, Lin, Kazi Shafiul Alam, Jiachen Ma, Richard Povinelli, and Sheikh Iqbal Ahamed. "Dynamics Reconstruction of Remote Photoplethysmography." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 96–110. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99194-4_8.

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Kalinin, Konstantin, Yuriy Mironenko, Mikhail Kopeliovich, and Mikhail Petrushan. "Towards Collecting Big Data for Remote Photoplethysmography." In Lecture Notes in Networks and Systems, 70–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80129-8_6.

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Kalinin, Konstantin, Yuriy Mironenko, Mikhail Kopeliovich, and Mikhail Petrushan. "Towards Collecting Big Data for Remote Photoplethysmography." In Lecture Notes in Networks and Systems, 70–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80129-8_6.

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Liu, Siqi, Pong C. Yuen, Shengping Zhang, and Guoying Zhao. "3D Mask Face Anti-spoofing with Remote Photoplethysmography." In Computer Vision – ECCV 2016, 85–100. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46478-7_6.

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Monika, Harish Kumar, Sakshi Kaushal, and Varinder Garg. "Remote Photoplethysmography: Digital Disruption in Health Vital Acquisition." In Explainable Machine Learning for Multimedia Based Healthcare Applications, 215–33. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-38036-5_12.

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Qiu, Zhaolin, Lanfen Lin, Hao Sun, Jiaqing Liu, and Yen-Wei Chen. "Artificial Intelligence in Remote Photoplethysmography: Remote Heart Rate Estimation from Video Images." In Handbook of Artificial Intelligence in Healthcare, 267–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79161-2_11.

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Zhang, Haoyu, Raghavendra Ramachandra, and Christoph Busch. "Face Presentation Attack Detection Using Remote Photoplethysmography Transformer Model." In Communications in Computer and Information Science, 558–71. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31417-9_42.

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Sinhal, Ruchika, Kavita Singh, and M. M. Raghuwanshi. "An Overview of Remote Photoplethysmography Methods for Vital Sign Monitoring." In Computer Vision and Machine Intelligence in Medical Image Analysis, 21–31. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8798-2_3.

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Lee, Kunyoung, Hojoon You, Jaemu Oh, and Eui Chul Lee. "Extremely Lightweight Skin Segmentation Networks to Improve Remote Photoplethysmography Measurement." In Intelligent Human Computer Interaction, 454–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27199-1_45.

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Conference papers on the topic "Remote photoplethysmography"

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Mironenko, Yuriy, Konstantin Kalinin, Mikhail Kopeliovich, and Mikhail Petrushan. "Remote Photoplethysmography: Rarely Considered Factors." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00156.

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Macwan, Richard, Yannick Benezeth, Alamin Mansouri, Keisuke Nakamura, and Randy Gomez. "Remote Photoplethysmography measurement using constrained ICA." In 2017 E-Health and Bioengineering Conference (EHB). IEEE, 2017. http://dx.doi.org/10.1109/ehb.2017.7995453.

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Wang, Wenjin, Albertus C. den Brinker, Sander Stuijk, and Gerard de Haan. "Color-Distortion Filtering for Remote Photoplethysmography." In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). IEEE, 2017. http://dx.doi.org/10.1109/fg.2017.18.

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Demirezen, Halil, and Cigdem Eroglu Erdem. "Remote Photoplethysmography Using Nonlinear Mode Decomposition." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462538.

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Harbawi, Malek A., Muhammad I. Ibrahimy, and S. M. A. Motakabber. "Photoplethysmography based remote health monitoring system." In 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA). IEEE, 2013. http://dx.doi.org/10.1109/icsima.2013.6717955.

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Rubins, Uldis, Zbignevs Marcinkevics, Robert Andrianirina Muckle, Ieva Henkuzena, Andris Roze, and Andris Grabovskis. "Remote photoplethysmography for assessment of oral mucosa." In Preclinical and Clinical Optical Diagnostics, edited by J. Quincy Brown and Ton G. van Leeuwen. SPIE, 2019. http://dx.doi.org/10.1117/12.2526979.

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Marcinkevics, Zbignevs, Kapil Ilango, Paula Balode, Uldis Rubins, and Andris Grabovskis. "The assessment of gingivitis using remote photoplethysmography." In Third International Conference Biophotonics Riga 2020, edited by Janis Spigulis. SPIE, 2020. http://dx.doi.org/10.1117/12.2581969.

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Feng, Litong, Lai-Man Po, Xuyuan Xu, and Yuming Li. "Motion artifacts suppression for remote imaging photoplethysmography." In 2014 International Conference on Digital Signal Processing (DSP). IEEE, 2014. http://dx.doi.org/10.1109/icdsp.2014.6900813.

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Wu, Bing-Fei, Po-Wei Huang, Da-Hong He, Chung-Han Lin, and Kuan-Hung Chen. "Remote Photoplethysmography Enhancement with Machine Leaning Methods." In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2019. http://dx.doi.org/10.1109/smc.2019.8914554.

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Kossack, Benjamin, Eric Wisotzky, Peter Eisert, Sebastian P. Schraven, Brigitta Globke, and Anna Hilsmann. "Perfusion assessment via local remote photoplethysmography (rPPG)." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. http://dx.doi.org/10.1109/cvprw56347.2022.00238.

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You might also be interested in the extended bibliographies on the topic 'Remote photoplethysmography' for particular source types:
Journal articles
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