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DistancePPG: Robust non-contact vital signs monitoring using a camera
Open accessAbstract
Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.
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“and other subjective scales were employed in 13 studies (35.14%) [30,42,43,44,45,50,52,56,72,78,80,87,88].”
From Skin pigmentation influence on pulse oximetry accuracy: a systematic review and bibliometric analysis (2022) · cites this paper as [78]
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“And last but not the least, smart ROI based methods where the spatial characteristics of images are utilized to construct the rPPG signal [1], [10], [5].”
MethodEnhanced Contactless Vital Sign Estimation from Real-Time Multimodal 3D Image Data
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BackgroundRemote Photoplethysmography Using Nonlinear Mode Decomposition
Demirezen, H., & Erdem, Ç. E. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462538 · Cited by 31
“In order to improve the estimation accuracy, different ROI selection [9], signal decompostion methods [3], [11], illimunation models [10], [7], multispectral aproaches [14], and multicamera setups [15] have been proposed and compared.”
BackgroundMethodInter-Beat Interval Estimation from Facial Video Based on Reliability of BVP Signals
Maki, Y., Monno, Y., 吉崎和徳, Tanaka, M., & Okutomi, M. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857081 · Cited by 31
“, [7]–[10]), our framework first extracts candidate BVP signals from sampled multiple face patches.”
BackgroundMethodPulseNet: A multitask learning network for remote heart rate estimation
Yin, R., Jia, R., Cui, Z., & Sun, H.-M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.108048 · Cited by 31
Evaluation of Remote Photoplethysmography Measurement Conditions toward Telemedicine Applications
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MW-PPG Sensor: An on-Chip Spectrometer Approach
Chang, C.-C., Wu, C.-T., Choi, B. I., & Fang, T.-J. (2019) · Sensors · doi:10.3390/s19173698 · Cited by 30
Information-Enhanced Network for Noncontact Heart Rate Estimation From Facial Videos
Liu, L., Xia, Z., Zhang, X., Peng, J., Feng, X., & Zhao, G. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3301962 · Cited by 30
“In recent years, remote Photoplethysmography (rPPG) methods based on facial videos have attracted much attention, and many effective methods have continuously emerged [2], [3], [4].”
BackgroundMethodInfluentialUsing imaging photoplethysmography for heart rate estimation in non-human primates
Михайлович, У. А., Moeller, S., Kagan, I., Gail, A., Treue, S., & Wolf, F. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202581 · Cited by 29
Designing a Moral Compass for the Future of Computer Vision Using Speculative Analysis
Skirpan, M. W., & YEH, T. P. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.179 · Cited by 28
“Without a doubt, combining advanced machine learning with troves of image data is likely to aid human causes such as health monitoring [37] and accelerate efficiency in areas such as archiving [17] and traffic analysis [66].”
Physiological Signal Preserving Video Compression for Remote Photoplethysmography
Zhao, C., Chen, W., Lin, C., & Wu, X. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2899102 · Cited by 28
“V ITAL signs camera is a physiological signal monitoring sensor that enables accurate reading of a variety of subjects’ physiological signals such as heart/breathing rates [1], SpO2 [2], blood pressure [3], etc, by means of computer vision techniques.”
BackgroundMethodRobust heart-rate estimation from facial videos using Project_ICA
Qi, L., Yu, H., Xu, L., Mpanda, R. S., & Greenwald, S. E. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab2c9f · Cited by 28
“In previous studies (Kumar et al 2015, Wang et al 2016, 2017a) various skin types resulted in different strengths of rPPG signal and it was confirmed that both dark-skinned and light-skinned subjects were representative for studying the accuracy of HR measurement using rPPG.”
BackgroundMethodA computer vision approach for classifying isometric grip force exertion levels
Asadi, H., Zhou, G., Lee, J. J., Aggarwal, V., & Yu, D. (2020) · Ergonomics · doi:10.1080/00140139.2020.1745898 · Cited by 28
“Collecting PPG was motivated by previous work suggesting computer vision can measure sub-surface physiology (Kumar, Veeraraghavan, and Sabharwal 2015; Sun and Thakor 2016; Wang, Stuijk, and De Haan 2016, Wang et al. 2017); however, pilot work by our team showed computer vision-based PPG detection…”
MethodAdvancements in Methods and Camera-Based Sensors for the Quantification of Respiration
Rehouma, H., Noumeir, R., Essouri, S., & Jouvet, P. A. (2020) · Sensors · doi:10.3390/s20247252 · Cited by 28
Non-Contact Photoplethysmogram and Instantaneous Heart Rate Estimation from Infrared Face Video
Martinez, N., Bertrán, M., Sapiro, G., & Wu, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803109 · Cited by 27
“Several methodologies for estimating heart rate from face videos have been developed over the years [7–12].”
MethodReal-time respiration rate measurement from thoracoabdominal movement with a consumer grade camera
Chatterjee, A., Prathosh, A. P., & Praveena, P. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591289 · Cited by 27
“Several attempts are made to overcome such limitations in [9], [10], [11], [12].”
BackgroundQuality metric for camera-based pulse rate monitoring in fitness exercise
Wang, W., Balmaekers, B., & de Haan, G. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532795 · Cited by 27
“Index Terms— biomedical monitoring, remote photoplethysmography, pulse rate, quality metric, fitness exercise.”
BackgroundVideo-based Contactless Blood Pressure Estimation: A Review
Lu, Y., Wang, C., & Meng, M. Q. ‐. (2020) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar49640.2020.9303040 · Cited by 26
Demirezen, H., & Erdem, Ç. E. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-01873-x · Cited by 26
Motion-Robust Atrial Fibrillation Detection Based on Remote-Photoplethysmography
Wu, B., Wu, B.-J., Cheng, S.-E., Sun, Y., & Chung, M.-L. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172705 · Cited by 27
“This method outperforms CNN-based algorithms such as DistPPG [37], RhythmNet [28], DeepPhys [29], and rPPGNet [30] in their experiments.”
MethodNoncontact Cardiac Parameters Estimation Using Radar Acoustics for Healthcare IoT
Rong, Y., Lenz, I., & Bliss, D. W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3317670 · Cited by 26
“Camera-based approaches have successfully measured HR, respiration [7], and oxygen saturation [8].”
MethodA Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using an RGB Camera
Ghanadian, H., Ghodratigohar, M., & Osman, H. A. (2018) · IEEE Access · doi:10.1109/access.2018.2872756 · Cited by 26
“[20] extract the HRV using the Maximum Ratio Combining (MRC) method.”
MethodInfluence of a skin status on the light interaction with dermis
Kamshilin, A. A., Mamontov, O. V., Koval, V. T., Zayats, G. A., & Владимирович, Р. Р. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.004326 · Cited by 25
Imaging PPG for In Vivo Human Tissue Perfusion Assessment during Surgery
Lai, M., van der Stel, S. D., Groen, H. C., van Gastel, M., Kuhlmann, K. F. D., Ruers, T. J. M., & Hendriks, B. H. W. (2022) · Journal of Imaging · doi:10.3390/jimaging8040094 · Cited by 25
Liu, S., Lan, X., & Yuen, P. C. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3197335 · Cited by 25
“Considering the difficulties of rPPG estimation on dark skin [54], the stable performance under cross dataset evaluations of 3DMAD ⇔ HKBU-MARsV2 + show the robustness of our method.”
MethodA Review on Computer Vision Technology for Physical Exercise Monitoring
Khanal, S. R., Paulino, D., Sampaio, J. E., Barroso, J., Reis, A., & Filipe, V. M. D. J. (2022) · Algorithms · doi:10.3390/a15120444 · Cited by 25
Robust algorithm for remote photoplethysmography in realistic conditions
Artemyev, M., Churikova, M., Grinenko, M., & Perepelkina, O. S. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102737 · Cited by 24
A Two-Stage Low-Complexity Human Sleep Motion Classification Method Using IR-UWB
Lai, J., Yang, Z., & Guo, B. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3100635 · Cited by 25
Guo, K., Zhai, T., Purushothama, M. H., Dobre, A., Meah, S. Z., Pashollari, E., Vaish, A., DeWilde, C., & Islam, M. N. (2022) · Applied Sciences · doi:10.3390/app12094416 · Cited by 24
iSense: Enabling Radar Sensing Under Mutual Device Interference
Yang, S., Zhang, D., Li, Y., Hu, Y., Sun, Q., & Chen, Y. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3379570 · Cited by 24
“3) Other Sensing Modalities: Computer vision-based meth-ods [60] use cameras to identify blood flow signals on human faces to achieve vital signs monitoring.”
BackgroundLee, H.-T., Shim, M., Liu, X., Cheon, H.-R., Kim, S., Han, C. H., & Hwang, H.-J. (2025) · Biomedical Engineering Letters · doi:10.1007/s13534-025-00469-5 · Cited by 29
“However, PPG signals are highly sensitive to motion artifacts, skin tone variability, and environmental light interference, which can affect measurement accuracy and reliability [46, 47 ].”
A Non-Contact PPG Biometric System Based on Deep Neural Network
Patil, O. R., Wang, W., Gao, Y., Xu, W., & Jin, Z. (2018) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2018.8698552 · Cited by 22
“The camera-based PPG acquisition approaches have recently been proposed and investigated by many researchers, providing a user-friendly way for continuous monitoring of PPG and other vital signs [15, 29].”
MethodAmelard, R., Clausi, D. A., & Wong, A. K. C. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.004874 · Cited by 22
Adaptive Gaussian Mixture Model Driven Level Set Segmentation for Remote Pulse Rate Detection
Woyczyk, A., Fleischhauer, V., & Zaunseder, S. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3054779 · Cited by 22
“A set of facial landmarks, e.g. eyes,nose,mouthandjawline,aredetected,andthoselandmarks areeitherusedasanchorpointsforanellipsoidROI[17]orused todefinetheoutercornersofapolygonwhichservesasROI[18].”
BackgroundNon-Contact Reflectance Photoplethysmography: Progress, Limitations, and Myths
Špetlík, R., Čech, J., & Matas, J. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00111 · Cited by 22
“[1], [16], [21], [29], [39], [40], [43], [52], [53], [58], [60], [63]) that a signal obtained from a transmittance-mode pulse oximeter may serve as a gold standard in the evaluation of a NrPPG approach.”
BackgroundResultThe Effect of Optical Crosstalk on Accuracy of Reflectance-Type Pulse Oximeter for Mobile Healthcare
Baek, H. J., Shin, J., & Cho, J. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/3521738 · Cited by 22
RobustPPG: camera-based robust heart rate estimation using motion cancellation
Maity, A. K., Wang, J., Sabharwal, A., & Nayar, S. K. (2022) · Biomedical Optics Express · doi:10.1364/boe.465143 · Cited by 21
Deep Learning-Based Non-Contact IPPG Signal Blood Pressure Measurement Research
Cheng, H., Xiong, J., Chen, Z., & Chen, J. (2023) · Sensors · doi:10.3390/s23125528 · Cited by 21
Fundus Imaging-Based Healthcare: Present and Future
Kumar, V., & Paul, K. (2023) · ACM Transactions on Computing for Healthcare · doi:10.1145/3586580 · Cited by 21
“We know that all blood components show peculiar characteristics at the different frequency ranges of light source [83, 85, 126].”
BackgroundDynamic heart rate estimation using principal component analysis
Yu, Y.-P., Paramesran, R., Lim, C.-L., & Kwan, B.-H. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.004610 · Cited by 21
Spatial-Spectral-Temporal Fusion for Remote Heart Rate Estimation
Kado, S., Monno, Y., 吉崎和徳, Tanaka, M., & Okutomi, M. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2997785 · Cited by 21
“Some studies have presented a method using multiple local face ROIs to make HR estimation more robust to illumination variations at different face regions [37]–[39].”
MethodLiu, X., Yang, X., Wang, D., & Wong, A. K. C. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3021222 · Cited by 22
“and eyes), these methods [23], [24] divide the facial region into multiple subregions and then select ones rich”
MethodHeart rate estimation based on face video under unstable illumination
Yin, R., Jia, R., Cui, Z., Yu, J.-T., Du, Y.-B., Gao, L., & Sun, H.-M. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02167-4 · Cited by 20
“proposed a method of combining skin color change signals based on the weighted average [24].”
MethodLapitan, D. G., & Рогаткин, Д. А. (2021) · Journal of Biophotonics · doi:10.1002/jbio.202000459 · Cited by 20
Recent Advances in Atherosclerotic Disease Screening Using Pervasive Healthcare
Luo, J., Yan, Z., Guo, S., & Chen, W. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3081180 · Cited by 20
Gupta, A. R., Ravelo-García, A. G., & Morgado‐Dias, F. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3144677 · Cited by 20
“[7] used a monochrome camera for extracting the green spectra followed by its weighted average using varied ROI combinations.”
MethodCPulse: Heart Rate Estimation From RGB Videos Under Realistic Conditions
Mehta, A. D., & Sharma, H. K. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3303504 · Cited by 20
“2) Effect of Skin Tone: Several studies considered skin tone as a major challenge in the estimation of HR accurately from facial videos, where more melanin (pigment) in the skin generally produces a darker skin tone [22].”
BackgroundMethodUncertainty Quantification for Deep Learning-Based Remote Photoplethysmography
Song, R., Wang, H., Xia, H., Cheng, J., Li, C., & Chen, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3317379 · Cited by 19
“Kumar et al. [22] defined a goodness metric using the power spectrum density for the rPPG signal extracted on each region of interest (ROI).”
MethodA Review of Patient Bed Sensors for Monitoring of Vital Signs
Recmanik, M., Martínek, R., Nedoma, J., Jaroš, R., Pelc, M., Hájovský, R., Velička, J., Pieš, M., Sevcakova, M., & Kawala‐Sterniuk, A. (2024) · Sensors · doi:10.3390/s24154767 · Cited by 20
“In [98], Kumar and his colleagues proposed a distance PPG, which is a new algorithm for estimating vital functions based on a camera.”
BackgroundContactless Cardiac Activity Detection Based on Head Motion Magnification
Al‐Naji, A., & Chahl, J. (2017) · International Journal of Image and Graphics · doi:10.1142/s0219467817500012 · Cited by 20
Remote Photoplethysmography (rPPG): A State-of-the-Art ReviewPreprint
Pirzada, P., Wilde, A., Doherty, G. H., & Harris‐Birtill, D. (2023) · medRxiv · doi:10.1101/2023.10.12.23296882 · Cited by 21
“One study [137], as shown in Table III, demonstrated a higher error rate in such cases.”
VidAF: A Motion-Robust Model for Atrial Fibrillation Screening From Facial Videos
Liu, X., Yang, X., Wang, D., Wong, A. K. C., Ma, L., & Li, L. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3124967 · Cited by 19
Systematic analysis of video-based pulse measurement from compressed videos
Nowara, E. M., McDuff, D., & Veeraraghavan, A. (2020) · Biomedical Optics Express · doi:10.1364/boe.408471 · Cited by 19
Cho, D., Kim, J., Lee, K. J., & Kim, S. (2021) · IEEE Access · doi:10.1109/access.2021.3106046 · Cited by 19
“Digital filters, configured by a linear time invariant (LTI) system and Fast Fourier Transform (FFT) for searching motion artifact frequency ranges, have been used for removing motion [30], [33].”
MethodNoncontact Blood Pressure Monitoring Technology using Facial Photoplethysmograms
Adachi, Y., Edo, Y., Ogawa, R., Tomizawa, R., Iwai, Y., & Okumura, T. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856439 · Cited by 19
“In addition to reduction of face detection errors and face tracking errors, it will be necessary to construct the blood pressure estimation model which adapts to the body movements [12] [13].”
BackgroundRaheel, M. S., Tubbal, F., Raad, R., Ogunbona, P., Coyte, J., Patterson, C., Perlman, D., Iranmanesh, S., Odeh, N., & Foroughi, J. (2024) · Sensors & Diagnostics · doi:10.1039/d4sd00073k · Cited by 20
CoSTHR: A Heart Rate Estimating Network With Adaptive Color Space Transformation
Qiu, Z., Liu, J., Sun, H., Lin, L., & Chen, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3170976 · Cited by 18
Measuring Heart Rate Variability Using Facial Video
Martinez-Delgado, G. H., Correa-Balan, A. J., May-Chan, J. A., Parra-Elizondo, C. E., Guzman-Rangel, L. A., & Martínez‐Torteya, A. (2022) · Sensors · doi:10.3390/s22134690 · Cited by 18
Style Transfer with Bio-realistic Appearance Manipulation for Skin-tone Inclusive rPPG
Ba, Y., Wang, Z., Karinca, D., Bozkurt, O. D., & Kadambi, A. (2022) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccp54855.2022.9887649 · Cited by 19
“Algorithms of detecting non-contact PPG signal can roughly be divided into three categories: Signal decomposition [8], [24], [25], [26], [27], model-based methods [28], [29], [30], [31], and deep learning methods [9], [10], [11], [12].”
MethodUbiquitous WiFi and Acoustic Sensing: Principles, Technologies, and Applications
Huang, J., Wang, Y.-S., Zou, Y., Wu, K., & Ni, L. M.-S. (2023) · Journal of Computer Science and Technology · doi:10.1007/s11390-023-3073-5 · Cited by 18
Remote vitals monitoring in rodents using video recordings
Kunczik, J., Pereira, C. B., Zieglowski, L., Tolba, R. H., Wassermann, L., Häger, C., Bleich, A., Janssen‐Peters, H., Thum, T., & Czaplik, M. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.004422 · Cited by 18
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Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation
Wong, K. L., Chin, J. W., Chan, T. T., Odinaev, I., Suhartono, K., Kang, T., & So, R. H. Y. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00235 · Cited by 18
“hk) subject motion [5,24] and distance to the camera [1,8], skin tone [10,18], and make-up [28] can significantly impact the quality of the extracted signal.”
BackgroundChiu, L.-W., Chou, Y.-R., Wu, Y.-C., & Wu, B. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3328703 · Cited by 16
“In addition, DistancePPG [30], SparsePPG [3], AutoSparsePPG [6], TURNIP [24], and Wu et al. [7] were applied to both multichannel (RGB) images and single-channel (NIR) images.”
BackgroundPeng, J., Su, W., Chen, H., Sun, J., & Tian, Z. (2024) · Bioengineering · doi:10.3390/bioengineering11020113 · Cited by 16
“This spatial similarity in rPPG signals has been leveraged in the methodology design of several studies [10,11,32–36].”
MethodNguyen, N. D., Nguyen, L. N., Li, H., López, M. B., & Casado, C. Á. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108873 · Cited by 16
Programmable Non-Epipolar Indirect Light Transport: Capture and Analysis
Kubo, H., Jayasuriya, S., Iwaguchi, T., Funatomi, T., Mukaigawa, Y., & Narasimhan, S. G. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2946812 · Cited by 16
“In addition, there has been several works on extracting blood pulse using photoplethysmography (PPG) [48], [49] as well as motion magnification [50].”
BackgroundRyu, J., Hong, S., Liang, S., Pak, S., Chen, Q., & Yan, S. (2021) · Journal of Biomedical Optics · doi:10.1117/1.jbo.26.2.025003 · Cited by 16
On the spatial phase distribution of cutaneous low-frequency perfusion oscillations
Borik, Š., Lyra, S., Perlitz, V., Keller, M., Leonhardt, S. K., & Blažek, V. (2022) · Scientific Reports · doi:10.1038/s41598-022-09762-0 · Cited by 16
Fathy, R. A., Wang, H., & Ren, L. (2016) · IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS) · doi:10.1109/biowireless.2016.7445552 · Cited by 16
“To overcome such drawbacks [1, 2], Doppler radar and camera based PPG systems have been proposed for triage monitoring, and very promising performance have been demonstrated.”
BackgroundMethodInfluentialCerina, L., Iozzia, L., & Mainardi, L. T. (2017) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2016-0234 · Cited by 16
Motion-resistant heart rate measurement from face videos using patch-based fusion
Yang, Z., Yang, X., Jin, J., & Wu, X. (2019) · Signal Image and Video Processing · doi:10.1007/s11760-018-01409-w · Cited by 16
Machine-based Multimodal Pain Assessment Tool for Infants: A ReviewPreprint
Zamzmi, G., Goldgof, D. B., Kasturi, R., Sun, Y., & Ashmeade, T. L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.00331 · Cited by 23
“ore, it has been suggested to use contactless and non-invasive methods for monitoring infants’ vital signs. Examples of video-based vital signs detection methods are presented in [123], [124], [125], [126], [127], [128]. In summary, we presented in this section the current efforts for assessing infants’ pain using vit…”
BackgroundZhao, X., Tanaka, R., Mandour, A. S., Shimada, K., & Hamabe, L. (2025) · Animals · doi:10.3390/ani15071033 · Cited by 18
“(3) Engineering of specialized illumination systems optimized for veterinary clinical environments to improve signal acquisition in varying lighting conditions [40].”
Kurihara, K., Maeda, Y., Sugimura, D., & Hamamoto, T. (2023) · IEEE Access · doi:10.1109/access.2023.3284465 · Cited by 16
MSDN: A Multistage Deep Network for Heart-Rate Estimation From Facial Videos
Zhang, X., Xia, Z., Dai, J., Liu, L., Peng, J., & Feng, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3329095 · Cited by 15
“Inspired by [45] and [46], different facial regions contain various contribution weights for the rPPG generation.”
BackgroundWearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation
Cheung, M. Y., Sabharwal, A., Coté, G. L., & Veeraraghavan, A. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3434344 · Cited by 23
“Templatematchingusingdynamictime warping[106],spectralquality[107],andgoodnessmetric[108] havealsobeenusedtodeterminesignalquality.”
Lee, K., Han, D. K., & Ko, H. (2018) · Journal of Advanced Transportation · doi:10.1155/2018/8513487 · Cited by 15
Perspective review of optical imaging in welfare assessment in animal-based research
Pereira, C. B., Kunczik, J., Bleich, A., Häger, C., Kiessling, F. M., Thum, T., Tolba, R. H., Lindauer, U., Treue, S., & Czaplik, M. (2019) · Journal of Biomedical Optics · doi:10.1117/1.jbo.24.7.070601 · Cited by 15
“The application of NIR imaging for SpO2 measurement was primarily tested on human subjects.(29,30) In the last years, this technique has also been being used in animal research to measure cortical blood oxygenation.”
MethodReal-Time Robust Noncontact Heart Rate Monitoring With a Camera
Bai, G., Huang, J., & Liu, H. (2018) · IEEE Access · doi:10.1109/access.2018.2837086 · Cited by 15
“[21], in a method based”
Kurihara, K., Sugimura, D., & Hamamoto, T. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803673 · Cited by 15
“Face Patch Selection According to past studies [7, 8], it is reported that analyzing time-series signals in local image patch basis was effective for HR estimation.”
BackgroundSpatio-temporal and -spectral feature maps in photoplethysmography imaging and infrared thermography
Paul, M., Behr, S. C., Weiß, C., Heimann, K., Orlikowsky, T. W., & Leonhardt, S. K. (2021) · BioMedical Engineering OnLine · doi:10.1186/s12938-020-00841-9 · Cited by 14
Turnip: Time-Series U-Net With Recurrence For NIR Imaging PPG
Comas, A., Marks, T. K., Mansour, H., Lohit, S., Ma, Y., & Liu, X. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506663 · Cited by 14
“The smoothed locations are used to extract 48 regions of interest (ROIs) located around the forehead, cheeks and chin (the face areas containing the strongest PPG signals [17]).”
BackgroundMethodInfluentialContactless Monitoring of PPG Using Radar
Khan, U. M., Rigazio, L., & Shahzad, M. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3550330 · Cited by 14
“The basic principle behind the camera-based sensing is to infer the blood volume changes by tracking the skin color over time [7, 8].”
BackgroundMethodInfluentialTong, Y., Huang, Z., Zhang, Z., Yin, M., Shan, G., Wu, J., & Qin, F. (2023) · Measurement · doi:10.1016/j.measurement.2023.113605 · Cited by 14
PhysMLE: Generalizable and Priors-Inclusive Multi-Task Remote Physiological Measurement
Wang, J., Lü, H., Wang, A., Yang, X. R., Chen, Y.-C., He, D., & Wu, K. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3545598 · Cited by 14
“Previous studies have highlighted the presence of analogous waveforms in short-term rPPG signals captured from different facial regions [28], [72], [73].”
Rubīns, U., Miščuks, A., & Lange, M. (2017) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-5122-7_95 · Cited by 14
“RPPG is mainly used for remote monitoring of cardiac pulse [4-7], but it also can be used for monitoring the blood circulation parameters, such as pulsatile amplitude changes [1-3,8-13].”
BackgroundTopological Eulerian Synthesis of Slow Motion Periodic Videos
Tralie, C. J., & Berger, M. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451014 · Cited by 14
“This fine detailed analysis can be applied, for example, to characterize subtle progressions of blood flow in the face during a heartbeat [1].”
Robust contactless pulse transit time estimation based on signal quality metric
Fan, X., & Tjahjadi, T. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.06.016 · Cited by 14
Automated Remote Pulse Oximetry System (ARPOS)
Pirzada, P., MORRISON, D. L., Doherty, G. H., Dhasmana, D., & Harris‐Birtill, D. (2022) · Sensors · doi:10.3390/s22134974 · Cited by 14
Wang, H., Yang, X., Liu, X., & Wang, D. (2022) · Biomedical Optics Express · doi:10.1364/boe.457774 · Cited by 14
Photoplethysmography imaging algorithm for continuous monitoring of regional anesthesia
Rubīns, U., Spīgulis, J., & Miščuks, A. (2016) · doi:10.1145/2993452.2994308 · Cited by 14
“INTRODUCTION Remote photoplethysmography imaging (rPPG) is a non-contact optical diagnostic technique to detect blood volume changes in the microvascular bed of tissue [1-13].”
BackgroundPulseCam: High-resolution blood perfusion imaging using a camera and a pulse oximeter
Kumar, M., Suliburk, J. W., Veeraraghavan, A., & Sabharwal, A. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591581 · Cited by 14
“Finally, w(~x, t) is the noise added during the camera acquisition process [8].”
BackgroundSimple measurement of pulse oximetry using a standard color camera
Cobos-Torres, J.-C., & Abderrahim, M. (2017) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp.2017.8076026 · Cited by 14
“In our opinion, they use a zero-phase noncausal forward-backward bandpass filter that is explained in another paper [12].”
Method
Metadata
- Authors
- Kumar, M., Veeraraghavan, A., & Sabharwal, A.
- Affiliations
- Rice University (US)
- Venue
- Biomedical Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2015-04-06
- Volume
- 6
- Issue
- 5
- Pages
- 1565
- ISSN
- 2156-7085 (ISSN-L)
- PMC
- PMC4467696
- OpenAlex
- W2029745771
- Cited by
- 417
- References
- 17
- Citations by year
- 2026: 9 · 2025: 21 · 2024: 36 · 2023: 46 · 2022: 49 · 2021: 57 · 2020: 35 · 2019: 47 · 2018: 46 · 2017: 33 · 2016: 31 · 2015: 7
- FWCI
- 19.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Biomedical Optics Express (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Rice Research Repository (Rice University) (submittedVersion)
- Funders
- National Science Foundation (NSF) (1126478, 1429047)
- Texas Higher Education Coordinating Board (THECB) (THECB-NHARP 13308)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
- Keywords
- respiratory rate estimation, photoplethysmography, skin color change, blood perfusion, incident light intensity, signal-to-noise ratio, darker skin tones, motion robustness
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