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Статті в журналах з теми "Remote photoplethysmography"

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Автор: Grafiati

Опубліковано: 13 квітня 2024

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1

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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2

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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3

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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Анотація:

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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4

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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5

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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6

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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7

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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Анотація:

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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8

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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Анотація:

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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9

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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Анотація:

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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10

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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Анотація:

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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11

Seepers, Robert Mark, Wenjin Wang, Gerard de Haan, Ioannis Sourdis, and Christos Strydis. "Attacks on Heartbeat-Based Security Using Remote Photoplethysmography." IEEE Journal of Biomedical and Health Informatics 22, no. 3 (May 2018): 714–21. http://dx.doi.org/10.1109/jbhi.2017.2691282.

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12

Zhao, Changchen, Weihai Chen, Chun-Liang Lin, and Xingming Wu. "Physiological Signal Preserving Video Compression for Remote Photoplethysmography." IEEE Sensors Journal 19, no. 12 (June 15, 2019): 4537–48. http://dx.doi.org/10.1109/jsen.2019.2899102.

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13

Yang, Yuting, Chenbin Liu, Hui Yu, Dangdang Shao, Francis Tsow, and Nongjian Tao. "Motion robust remote photoplethysmography in CIELab color space." Journal of Biomedical Optics 21, no. 11 (November 4, 2016): 117001. http://dx.doi.org/10.1117/1.jbo.21.11.117001.

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14

Artemyev, Mikhail, Marina Churikova, Mikhail Grinenko, and Olga Perepelkina. "Robust algorithm for remote photoplethysmography in realistic conditions." Digital Signal Processing 104 (September 2020): 102737. http://dx.doi.org/10.1016/j.dsp.2020.102737.

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15

Xiao, Hanguang, Tianqi Liu, Yisha Sun, Yulin Li, Shiyi Zhao, and Alberto Avolio. "Remote photoplethysmography for heart rate measurement: A review." Biomedical Signal Processing and Control 88 (February 2024): 105608. http://dx.doi.org/10.1016/j.bspc.2023.105608.

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16

Premkumar, Smera, and Duraisamy Jude Hemanth. "Intelligent Remote Photoplethysmography-Based Methods for Heart Rate Estimation from Face Videos: A Survey." Informatics 9, no. 3 (August 7, 2022): 57. http://dx.doi.org/10.3390/informatics9030057.

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Анотація:

Over the last few years, a rich amount of research has been conducted on remote vital sign monitoring of the human body. Remote photoplethysmography (rPPG) is a camera-based, unobtrusive technology that allows continuous monitoring of changes in vital signs and thereby helps to diagnose and treat diseases earlier in an effective manner. Recent advances in computer vision and its extensive applications have led to rPPG being in high demand. This paper specifically presents a survey on different remote photoplethysmography methods and investigates all facets of heart rate analysis. We explore the investigation of the challenges of the video-based rPPG method and extend it to the recent advancements in the literature. We discuss the gap within the literature and suggestions for future directions.

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17

Marcinkevics, Zbignevs, Alise Aglinska, Uldis Rubins, and Andris Grabovskis. "Remote Photoplethysmography for Evaluation of Cutaneous Sensory Nerve Fiber Function." Sensors 21, no. 4 (February 11, 2021): 1272. http://dx.doi.org/10.3390/s21041272.

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Анотація:

About 2% of the world’s population suffers from small nerve fiber dysfunction, neuropathy, which can result in severe pain. This condition is caused by damage to the small nerve fibers and its assessment is challenging, due to the lack of simple and objective diagnostic techniques. The present study aimed to develop a contactless photoplethysmography system using simple instrumentation, for objective and non-invasive assessment of small cutaneous sensory nerve fiber function. The approach is based on the use of contactless photoplethysmography for the characterization of skin flowmotions and topical heating evoked vasomotor responses. The feasibility of the technique was evaluated on volunteers (n = 14) using skin topical anesthesia, which is able to produce temporary alterations of cutaneous nerve fibers function. In the treated skin region in comparison to intact skin: neurogenic and endothelial component of flowmotions decreased by ~61% and 41%, the local heating evoked flare area decreased by ~44%, vasomotor response trend peak and nadir were substantially reduced. The results indicate for the potential of the remote photoplethysmography in the assessment of the cutaneous nerve fiber function. It is believed that in the future this technique could be used in the clinics as an affordable alternative to laser Doppler imaging technique.

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18

Garanin, А. А., V. S. Rogova, P. S. Ivanchina, and E. O. Tolkacheva. "Web photoplethysmography: opportunities and prospects." Regional blood circulation and microcirculation 22, no. 4 (December 27, 2023): 11–16. http://dx.doi.org/10.24884/1682-6655-2023-22-4-11-16.

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Анотація:

This literature review is devoted to the possibilities of using in clinical practice a new modification of photoplethysmography – its web version. The use of modern innovative techniques in the form of photo/video fixation of the human skin allows for contactless and remote assessment of the main physiological indicators of human health. This approach is of particular importance in conditions of shortage of medical workers, territorial separation of doctors and patients, restrictions in visiting medical institutions in the event of epidemics/pandemics of infectious diseases and it contributes to the development and implementation of telemedicine technologies in the daily work of medical specialists. The article discusses the possibilities of using web photoplethysmography to assess microcirculation (web capillaroscopy), heart rate and other indicators (respiratory rate, pulse rate, saturation, body temperature, etc.). Web photoplethysmography is a sensitive, simple and effective method of registering vital body functions. Its effectiveness as a possible screening method for detecting rhythm disturbances and its prospects in using blood pressure measurement has already been proven. The possibility of using not only video, but also photographic materials, for example, for the detection of cardiovascular diseases, is also described. No less promising is the possibility of using the method in conjunction with thermometry. The importance of studying aspects of web photoplethysmography is seen in the need to activate the processes of import substitution and technological sovereignty, as one of the main paradigms of our country’s development. It is obvious that the development of contactless research methods is one of the priorities in modern healthcare. Given the widespread availability of various devices, it is necessary to further explore the possibilities of using webcams and mobile devices in medical practice. The development and introduction into routine practice of medical devices that allow remote non-invasive early diagnosis of chronic non-communicable diseases is of special interest.

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19

Sheng, Yi, Wu Zeng, Qiuyu Hu, Weihua Ou, Yuxuan Xie, and Jie Li. "An Improved Approach to the Performance of Remote Photoplethysmography." Computers, Materials & Continua 73, no. 2 (2022): 2773–83. http://dx.doi.org/10.32604/cmc.2022.027985.

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20

Lee, Heejin, Junghwan Lee, Yujin Kwon, Jiyoon Kwon, Sungmin Park, Ryanghee Sohn, and Cheolsoo Park. "Multitask Siamese Network for Remote Photoplethysmography and Respiration Estimation." Sensors 22, no. 14 (July 7, 2022): 5101. http://dx.doi.org/10.3390/s22145101.

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Анотація:

Heart and respiration rates represent important vital signs for the assessment of a person’s health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese network and the multitask learning architecture. The MTS model was trained by the images of the cheek including nose and mouth and forehead areas while sharing the same parameters between the Siamese networks, in order to extract the features about the heart and respiratory information. The proposed model was constructed with a small number of parameters and was able to yield a high vital-sign-prediction accuracy, comparable to that obtained from the single-task learning model; furthermore, the proposed model outperformed the conventional multitask learning model. As a result, we can simultaneously predict the heart and respiratory signals with the MTS model, while the number of parameters was reduced by 16 times with the mean average errors of heart and respiration rates being 2.84 and 4.21. Owing to its light weight, it would be advantageous to implement the vital-sign-monitoring model in an edge device such as a mobile phone or small-sized portable devices.

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21

Tohma, Akito, Maho Nishikawa, Takuya Hashimoto, Yoichi Yamazaki, and Guanghao Sun. "Evaluation of Remote Photoplethysmography Measurement Conditions toward Telemedicine Applications." Sensors 21, no. 24 (December 14, 2021): 8357. http://dx.doi.org/10.3390/s21248357.

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Анотація:

Camera-based remote photoplethysmography (rPPG) is a low-cost and casual non-contact heart rate measurement method suitable for telemedicine. Several factors affect the accuracy of measuring the heart rate and heart rate variability (HRV) using rPPG despite HRV being an important indicator for healthcare monitoring. This study aimed to investigate the appropriate setup for precise HRV measurements using rPPG while considering the effects of possible factors including illumination, direction of the light, frame rate of the camera, and body motion. In the lighting conditions experiment, the smallest mean absolute R–R interval (RRI) error was obtained when light greater than 500 lux was cast from the front (among the following conditions—illuminance: 100, 300, 500, and 700 lux; directions: front, top, and front and top). In addition, the RRI and HRV were measured with sufficient accuracy at frame rates above 30 fps. The accuracy of the HRV measurement was greatly reduced when the body motion was not constrained; thus, it is necessary to limit the body motion, especially the head motion, in an actual telemedicine situation. The results of this study can act as guidelines for setting up the shooting environment and camera settings for rPPG use in telemedicine.

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22

Wang, Wenjin, Sander Stuijk, and Gerard de Haan. "A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation." IEEE Transactions on Biomedical Engineering 63, no. 9 (September 2016): 1974–84. http://dx.doi.org/10.1109/tbme.2015.2508602.

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23

Lee, Kunyoung, Seunghyun Kim, Byeongseon An, Hyunsoo Seo, Shinwi Park, and Eui Chul Lee. "Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation." Applied Sciences 13, no. 17 (August 30, 2023): 9818. http://dx.doi.org/10.3390/app13179818.

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Анотація:

Remote vital signal estimation has been researched for several years. There are numerous studies on rPPG, which utilizes cameras to detect cardiovascular activity. Most of the research has concentrated on obtaining rPPG from a complete video. However, excessive movement or changes in lighting can cause noise, and it will inevitably lead to a reduction in the quality of the obtained signal. Moreover, since rPPG measures minor changes that occur in the blood flow of an image due to variations in heart rate, it becomes challenging to capture in a noisy image, as the impact of noise is larger than the change caused by the heart rate. Using such segments in a video can cause a decrease in overall performance, but it can only be remedied through data pre-processing. In this study, we propose a screening technique that removes excessively noisy video segments as input and only uses signals obtained from reliable segments. Using this method, we were able to boost the performance of the current rPPG algorithm from 50.43% to 62.27% based on PTE6. Our screening technique can be easily applied to any existing rPPG prediction model and it can improve the reliability of the output in all cases.

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24

Li, Jianwei, Zitong Yu, and Jingang Shi. "Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 1334–42. http://dx.doi.org/10.1609/aaai.v37i1.25217.

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Анотація:

Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) estimation from facial videos which gives significant convenience compared with traditional contact-based measurements. In the real-world long-term health monitoring scenario, the distance of the participants and their head movements usually vary by time, resulting in the inaccurate rPPG measurement due to the varying face resolution and complex motion artifacts. Different from the previous rPPG models designed for a constant distance between camera and participants, in this paper, we propose two plug-and-play blocks (i.e., physiological signal feature extraction block (PFE) and temporal face alignment block (TFA)) to alleviate the degradation of changing distance and head motion. On one side, guided with representative-area information, PFE adaptively encodes the arbitrary resolution facial frames to the fixed-resolution facial structure features. On the other side, leveraging the estimated optical flow, TFA is able to counteract the rPPG signal confusion caused by the head movement thus benefit the motion-robust rPPG signal recovery. Besides, we also train the model with a cross-resolution constraint using a two-stream dual-resolution framework, which further helps PFE learn resolution-robust facial rPPG features. Extensive experiments on three benchmark datasets (UBFC-rPPG, COHFACE and PURE) demonstrate the superior performance of the proposed method. One highlight is that with PFE and TFA, the off-the-shelf spatio-temporal rPPG models can predict more robust rPPG signals under both varying face resolution and severe head movement scenarios. The codes are available at https://github.com/LJWGIT/Arbitrary_Resolution_rPPG.

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25

Szabała, Tomasz. "Exploratory Study on Remote Photoplethysmography using Visible Light Cameras." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 1 (January 12, 2023): 284–87. http://dx.doi.org/10.15199/48.2023.01.57.

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26

Kopeliovich, M. V., and I. V. Shcherban. "Method of Selecting the Most Discriminatory Areas Based on Spectral Entropy in Remote Photoplethysmography." Informacionnye Tehnologii 28, no. 2 (February 11, 2022): 102–12. http://dx.doi.org/10.17587/it.28.102-112.

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Анотація:

A person's heart rate can be determined from a video image of the facial skin. This is explained by the fact that variations in the optical characteristics of biological tissues are caused by changes in their blood volume due to the pulse waves. Remote photoplethysmography problem is stated which is directed to estimation of a person's heart rate by registering of changes in volume pulse remotely, using a camera. Common drawback of the existing methods of the remote photoplethysmography is caused by facial image noise due to human mimic activity, head turns during video recording, non-stationarity of illumination and other similar factors. The main ways to solve such problems are to detect face in each frame of the video sequence, and to select the most informative regions of interest. A new method for selecting facial areas in a video is proposed, which leads to improve the accuracy of a solution of the remote photoplethysmography. The method consists in the application of criterion based on the Shannon spectral entropy to the color signals obtained from different facial areas on the video sequence in order to select the least noisy area. The correctness of the proposed method was confirmed in the course of experiments on an open database collected from 42 volunteers. The developed method reduced the relative error of heart rate estimation to 7 %.

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27

Haugg, Fridolin, Mohamed Elgendi, and Carlo Menon. "Effectiveness of Remote PPG Construction Methods: A Preliminary Analysis." Bioengineering 9, no. 10 (September 20, 2022): 485. http://dx.doi.org/10.3390/bioengineering9100485.

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Анотація:

The contactless recording of a photoplethysmography (PPG) signal with a Red-Green-Blue (RGB) camera is known as remote photoplethysmography (rPPG). Studies have reported on the positive impact of using this technique, particularly in heart rate estimation, which has led to increased research on this topic among scientists. Therefore, converting from RGB signals to constructing an rPPG signal is an important step. Eight rPPG methods (plant-orthogonal-to-skin (POS), local group invariance (LGI), the chrominance-based method (CHROM), orthogonal matrix image transformation (OMIT), GREEN, independent component analysis (ICA), principal component analysis (PCA), and blood volume pulse (PBV) methods) were assessed using dynamic time warping, power spectrum analysis, and Pearson’s correlation coefficient, with different activities (at rest, during exercising in the gym, during talking, and while head rotating) and four regions of interest (ROI): the forehead, the left cheek, the right cheek, and a combination of all three ROIs. The best performing rPPG methods in all categories were the POS, LGI, and OMI methods; each performed well in all activities. Recommendations for future work are provided.

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28

Song, Rencheng, Huan Chen, Juan Cheng, Chang Li, Yu Liu, and Xun Chen. "PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography." IEEE Journal of Biomedical and Health Informatics 25, no. 5 (May 2021): 1373–84. http://dx.doi.org/10.1109/jbhi.2021.3051176.

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Nikolaiev, Sergii, Sergii Telenyk, and Yury Tymoshenko. "Non-Contact Video-Based Remote Photoplethysmography for Human Stress Detection." Journal of Automation, Mobile Robotics and Intelligent Systems 14, no. 2 (July 6, 2020): 63–73. http://dx.doi.org/10.14313/jamris/2-2020/21.

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30

Cennini, Giovanni, Jeremie Arguel, Kaan Akşit, and Arno van Leest. "Heart rate monitoring via remote photoplethysmography with motion artifacts reduction." Optics Express 18, no. 5 (February 24, 2010): 4867. http://dx.doi.org/10.1364/oe.18.004867.

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31

Macwan, Richard, Yannick Benezeth, and Alamin Mansouri. "Heart rate estimation using remote photoplethysmography with multi-objective optimization." Biomedical Signal Processing and Control 49 (March 2019): 24–33. http://dx.doi.org/10.1016/j.bspc.2018.10.012.

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32

Mösch, Lucas, Isabelle Barz, Anna Müller, Carina B. Pereira, Dieter Moormann, Michael Czaplik, and Andreas Follmann. "For Heart Rate Assessments from Drone Footage in Disaster Scenarios." Bioengineering 10, no. 3 (March 7, 2023): 336. http://dx.doi.org/10.3390/bioengineering10030336.

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The ability to use drones to obtain important vital signs could be very valuable for emergency personnel during mass-casualty incidents. The rapid and robust remote assessment of heart rates could serve as a life-saving decision aid for first-responders. With the flight sensor data of a specialized drone, a pipeline was developed to achieve a robust, non-contact assessment of heart rates through remote photoplethysmography (rPPG). This robust assessment was achieved through adaptive face-aware exposure and comprehensive de-noising of a large number of predicted noise sources. In addition, we performed a proof-of-concept study that involved 18 stationary subjects with clean skin and 36 recordings of their vital signs, using the developed pipeline in outdoor conditions. In this study, we could achieve a single-value heart-rate assessment with an overall root-mean-squared error of 14.3 beats-per-minute, demonstrating the basic feasibility of our approach. However, further research is needed to verify the applicability of our approach in actual disaster situations, where remote photoplethysmography readings could be impacted by other factors, such as blood, dirt, and body positioning.

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Kopeliovich, M. V., M. V. Petrushan, and A. I. Samarin. "Evolutionary algorithm for structural-parametric optimization of the remote photoplethysmography method." Optical Memory and Neural Networks 26, no. 1 (January 2017): 55–61. http://dx.doi.org/10.3103/s1060992x17010052.

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34

Yu, Zitong, Xiaobai Li, Pichao Wang, and Guoying Zhao. "TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection." IEEE Signal Processing Letters 28 (2021): 1290–94. http://dx.doi.org/10.1109/lsp.2021.3089908.

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35

Firmansyah, Riza Agung, Yuliyanto Agung Prabowo, Titiek Suheta, and Syahri Muharom. "Implementation of 1D convolutional neural network for improvement remote photoplethysmography measurement." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (March 1, 2023): 1326. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1326-1335.

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<span lang="EN-US">Remote photoplethysmography (rPPG) for non-contact heart rate measurement has been widely developed and shows good development. However, motion artifact due to changes in illumination and subject movement is still the main problem. Especially when measurements are taken in real conditions. In these conditions, it will be vulnerable to rPPG signal readings with poor signal quality. So, in this paper, it is proposed to classify the signal quality using one dimensional convolutional neural network (1D CNN). The classification is carried out based on the extraction of the temporal features of the rPPG signal that has been obtained from the plane orthogonal to skin algorithm and the magnitude of the subject's movement when measured. The classification results are entered into a compensated network if the signal obtained shows moderate quality. The compensated network will provide a more accurate estimate of hr value. The test was carried out using a dataset of 10 subjects, each measured with 3 different types of illumination. In the experiments conducted, the system's performance showed an improvement compared to the POS algorithm alone. The experiment found that the mean absolute error measurement was 2.78, and the mean error was relative at 3.67%.</span>

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Chae, JongEui, DaeYeol Kim, KwangKee Lee, and ChanHyeong Park. "Assessment of Heart Rate Derivation Methods and Applicability in Remote Photoplethysmography." Journal of the Institute of Electronics and Information Engineers 60, no. 10 (October 31, 2023): 35–42. http://dx.doi.org/10.5573/ieie.2023.60.10.35.

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37

Boccignone, Giuseppe, Alessandro D’Amelio, Omar Ghezzi, Giuliano Grossi, and Raffaella Lanzarotti. "An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation." Sensors 23, no. 7 (March 23, 2023): 3387. http://dx.doi.org/10.3390/s23073387.

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The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation.

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38

Song, Rencheng, Jiji Li, Minda Wang, Juan Cheng, Chang Li, and Xun Chen. "Remote Photoplethysmography With an EEMD-MCCA Method Robust Against Spatially Uneven Illuminations." IEEE Sensors Journal 21, no. 12 (June 15, 2021): 13484–94. http://dx.doi.org/10.1109/jsen.2021.3067770.

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39

Strokanev, K. S. "Review and Classification of Current Methods for Remote Photoplethysmography of the Face." Intellekt. Sist. Proizv. 19, no. 2 (July 10, 2021): 129. http://dx.doi.org/10.22213/2410-9304-2021-2-129-138.

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Дистанционная фотоплетизмография позволяет измерять частоту сердечных сокращений бесконтактным способом. Подобный способ особенно полезен, когда невозможно измерить пульс методами, требующими контакта с кожей. Методом дистанционной фотоплетизмографии можно измерить и насыщение крови кислородом. Рассмотрены исследования с двумя различными подходами: изменение цвета кожи лица и движение головы. Целью исследования является классификация современных методов лицевой фотоплетизмографии. В данной статье приведен обзор и классификация исследований в области дистанционной фотоплетизмографии c 2015 по 2020 год. В ходе исследования статей выявлено, что термин «дистанционная фотоплетизмография», или «remote photopletysmography» (rPPG), встречался чаще всего и поэтому был выбран основным для описания данного вида фотоплетизмографии. Источниками информации послужили самые крупные научные социальные сети и сайты. В работе приведена краткая история и основы дистанционной фотоплетизмографии. Разработан алгоритм для классификации исследований. Изученные работы представлены в удобном виде в таблице. По представленным материалам были составлены выводы. Исследователи могут использовать рассмотренные и классифицированные алгоритмы в качестве отправной точки для улучшения разработок в области дистанционной фотоплетизмографии.

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40

Caica, Anastasija. "Use of remote photoplethysmography in assessment of topical corticosteroid-induced skin blanching." Intrinsic Activity 5, Suppl. 2 (October 16, 2017): A2.40. http://dx.doi.org/10.25006/ia.5.s2-a2.40.

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41

Litong Feng, Lai-Man Po, Xuyuan Xu, Yuming Li, and Ruiyi Ma. "Motion-Resistant Remote Imaging Photoplethysmography Based on the Optical Properties of Skin." IEEE Transactions on Circuits and Systems for Video Technology 25, no. 5 (May 2015): 879–91. http://dx.doi.org/10.1109/tcsvt.2014.2364415.

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42

Aprini, Istighfariza, and Martin Clinton Tosima Manullang. "Adapting remote photoplethysmography for Indonesian subjects: an examination of diverse rPPG techniques." JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) 3, no. 3 (September 30, 2023): 165–80. http://dx.doi.org/10.35313/jitel.v3.i3.2023.165-180.

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Vital sign measurements are essential in intensive care patients, such as in the ICU or emergency department, and also for newborns or prenatal babies. The duty nurse usually monitors these vital signs by manually writing down the patient's condition on a large piece of paper in front of the patient's room. The lack of nurses can hinder the process of monitoring patient vital signs. However, since the COVID-19 pandemic, people have limited contact with their surroundings, making measuring vital signs with contact uncomfortable and unhygienic. The typical non-contact method for measuring heart rate is the remote photoplethysmography (rPPG) technique. In this study, we proposed to assess the performance of various rPPG algorithms on the Indonesian subjects dataset. The algorithms used are CHROM, GREEN, ICA, LGI, PBV, PCA, and POS on 70 pieces of data. Based on the test results with three types of evaluation metrics, namely MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and Bland Altman, it is found that the measurement results with the best performance POS algorithm with a low prediction error rate with the resulting MAE value of 2.59 and RMSE of 4.65.

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43

Lee, Seongbeen, Minseon Lee, and Joo Yong Sim. "DSE-NN: Deeply Supervised Efficient Neural Network for Real-Time Remote Photoplethysmography." Bioengineering 10, no. 12 (December 15, 2023): 1428. http://dx.doi.org/10.3390/bioengineering10121428.

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Non-contact remote photoplethysmography can be used in a variety of medical and healthcare fields by measuring vital signs continuously and unobtrusively. Recently, end-to-end deep learning methods have been proposed to replace the existing handcrafted features. However, since the existing deep learning methods are known as black box models, the problem of interpretability has been raised, and the same problem exists in the remote photoplethysmography (rPPG) network. In this study, we propose a method to visualize temporal and spectral representations for hidden layers, deeply supervise the spectral representation of intermediate layers through the depth of networks and optimize it for a lightweight model. The optimized network improves performance and enables fast training and inference times. The proposed spectral deep supervision helps to achieve not only high performance but also fast convergence speed through the regularization of the intermediate layers. The effect of the proposed methods was confirmed through a thorough ablation study on public datasets. As a result, similar or outperforming results were obtained in comparison to state-of-the-art models. In particular, our model achieved an RMSE of 1 bpm on the PURE dataset, demonstrating its high accuracy. Moreover, it excelled on the V4V dataset with an impressive RMSE of 6.65 bpm, outperforming other methods. We observe that our model began converging from the very first epoch, a significant improvement over other models in terms of learning efficiency. Our approach is expected to be generally applicable to models that learn spectral domain information as well as to the applications of regression that require the representations of periodicity.

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44

Lanata, Antonio. "Wearable Systems for Home Monitoring Healthcare: The Photoplethysmography Success Pros and Cons." Biosensors 12, no. 10 (October 12, 2022): 861. http://dx.doi.org/10.3390/bios12100861.

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The widespread use of remote technology has moved medical care services into individuals’ homes. In this perspective, the ubiquitous computing research proposes self-management and remote monitoring to help patients with healthcare in low-cost everyday home usage systems based on the latest technological advances in sensors, communication, and portability. This work analyzes recent publications on the paradigm of continuous monitoring through wearable and portable systems, focusing on photoplethysmography (PPG) advances and referencing the current systematic study proposed by Fine et al. The study revised the literature highlighting the pros and cons of using the PPG system for fitness, wellbeing, and medical devices. However, future works should focus on the standardization of the practical use and assessment of the quality of the PPGs’ output. For clinical parameter extraction methodology in terms of biological sites of application and signal processing methods, PPG is the most convenient and widely used system potentially suitable for the decentralized paradigm of continuous monitoring healthcare concepts.

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45

Babgei, Atar Fuady, Muhammad Wikan Sasongko, and Tri Arief Sardjono. "Analisis Photoplethysmography Jarak Jauh dalam berbagai Kondisi Pencahayaan." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 12, no. 2 (October 31, 2022): 169. http://dx.doi.org/10.22146/ijeis.78715.

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One of the limitations of photoplethysmography (PPG) using a contact sensor to estimate the heart rate is that the sensor must be attached directly to the patient's body. rPPG (remote-Photoplethysmography) can remotely monitor a patient's heart ratebased on an image. However, rPPG has limitations in instances where this technology is directly affected by the lighting conditions and direction of the observed subject. This study used rPPG based on the Green Channel and HSV (Hue, Saturation, and Value) color model to estimate heart rate under different lighting conditions. Analysis, computational methods,and image transformation functions are used for data selection, denoising, colormodel conversion, spectral analysis, and visualization to extract biomedical signals from inputs. The estimatedheart rate was then derivedusing spectral analysis on videostaken from an area of interest on the forehead. Compared to the ground truth, theaverage percentage error from the facial lighting tests conducted at 260 lux, 19 lux, and 11 lux for the Green Channel color modelis 0.038, 0.118, and 0.229, which is less than the HSV's error of 0.095, 0.212, and 0.247.

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46

Pagano, Tiago Palma, Lucas Lisboa dos Santos, Victor Rocha Santos, Paulo H. Miranda Sá, Yasmin da Silva Bonfim, José Vinicius Dantas Paranhos, Lucas Lemos Ortega, et al. "Remote Heart Rate Prediction in Virtual Reality Head-Mounted Displays Using Machine Learning Techniques." Sensors 22, no. 23 (December 5, 2022): 9486. http://dx.doi.org/10.3390/s22239486.

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Head-mounted displays are virtual reality devices that may be equipped with sensors and cameras to measure a patient’s heart rate through facial regions. Heart rate is an essential body signal that can be used to remotely monitor users in a variety of situations. There is currently no study that predicts heart rate using only highlighted facial regions; thus, an adaptation is required for beats per minute predictions. Likewise, there are no datasets containing only the eye and lower face regions, necessitating the development of a simulation mechanism. This work aims to remotely estimate heart rate from facial regions that can be captured by the cameras of a head-mounted display using state-of-the-art EVM-CNN and Meta-rPPG techniques. We developed a region of interest extractor to simulate a dataset from a head-mounted display device using stabilizer and video magnification techniques. Then, we combined support vector machine and FaceMash to determine the regions of interest and adapted photoplethysmography and beats per minute signal predictions to work with the other techniques. We observed an improvement of 188.88% for the EVM and 55.93% for the Meta-rPPG. In addition, both models were able to predict heart rate using only facial regions as input. Moreover, the adapted technique Meta-rPPG outperformed the original work, whereas the EVM adaptation produced comparable results for the photoplethysmography signal.

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47

Kossack, Benjamin, Eric L. Wisotzky, Anna Hilsmann, Peter Eisert, and Ronny Hänsch. "Local blood flow analysis and visualization from RGB-video sequences." Current Directions in Biomedical Engineering 5, no. 1 (September 1, 2019): 373–75. http://dx.doi.org/10.1515/cdbme-2019-0094.

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AbstractThe extraction of heart rate and other vital parameters from video recordings of a person has attracted much attention over the last years. In this paper, we examine time differences between distinct spatial regions using remote photoplethysmography (rPPG) in order to extract the blood flow path through human skin tissue in the neck and face. We can show that the visualization of the blood flow path corresponds to the physiologically defined path.

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48

Liu, Si-Qi, Xiangyuan Lan, and Pong C. Yuen. "Multi-Channel Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection." IEEE Transactions on Information Forensics and Security 16 (2021): 2683–96. http://dx.doi.org/10.1109/tifs.2021.3050060.

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49

Luguern, Duncan, Richard Macwan, Yannick Benezeth, Virginie Moser, L. Andrea Dunbar, Fabian Braun, Alia Lemkaddem, and Julien Dubois. "Wavelet Variance Maximization: A contactless respiration rate estimation method based on remote photoplethysmography." Biomedical Signal Processing and Control 63 (January 2021): 102263. http://dx.doi.org/10.1016/j.bspc.2020.102263.

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50

Wu, Bing-Fei, Yun-Wei Chu, Po-Wei Huang, and Meng-Liang Chung. "Neural Network Based Luminance Variation Resistant Remote-Photoplethysmography for Driver’s Heart Rate Monitoring." IEEE Access 7 (2019): 57210–25. http://dx.doi.org/10.1109/access.2019.2913664.

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