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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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Wang, W., Wei, Z., Yuan, J., Fang, Y. C., & Zheng, Y. (2022) · PLoS ONE · doi:10.1371/journal.pone.0275544 · Cited by 13
“solved the problem of low signal-tonoise ratio (SNR) caused by deep skin color and weak illumination conditions by using a weighted average to combine the skin color change signals of different tracking regions of the face [20].”
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MethodInfluentialSmall-Scale Perception in Medical Body Area Networks
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“Other methods like utilizing pressure sensor arrays [11] or cameras [12] are also used for monitoring respiration.”
MethodArchitectural Tricks for Deep Learning in Remote Photoplethysmography
Kopeliovich, M. V., Mironenko, Y., & Petrushan, M. V. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00209 · Cited by 13
“• skin reflection model, which is expected to help with noise caused by observational skin color changes caused by different view angles [3]; • attention model – ROI building procedure which pays special attention to moving pixels of image, also expected to distinguish smaller movements from global rigid motion [12, 3…”
BackgroundMethodA VCSEL-Based NIR Transillumination System for Morpho-Functional Imaging
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“Transillumination is also functional to transmissive photoplethysmography (PPG), a non-invasive optical technique that provides a signal related to the changes in blood volume in the tissue under observation, typically finger or ear lobe [19,20].”
BackgroundAn iPPG-Based Device for Pervasive Monitoring of Multi-Dimensional Cardiovascular Hemodynamics
Luo, J., Zhen, J., Zhou, P., Chen, W., & Guo, Y. (2021) · Sensors · doi:10.3390/s21030872 · Cited by 13
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“INTRODUCTION Vital signs assessment using cameras, known as remote photoplethysmography (rPPG), has shown significant promise in recent years [6], [8], [11].”
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Bella, A., Latif, R., Saddik, A., & Guerrouj, F. Z. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202122901030 · Cited by 12
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“Warm Bodies”: A Post-Processing Technique for Animating Dynamic Blood Flow on Photos and Avatars
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“Previous HR estimation research also showed that the strength of PPG signals differs between different regions of the face, with the cheek and forehead regions tending to produce the strongest PPG signals [19].”
BackgroundRobust Imaging Photoplethysmography in Long-Distance Motion
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“In 2015, Kumar [11] combined skin-colour-change signals from different tracked regions of the face to reduce motion artefacts by using different weighted averages.”
BackgroundAssessment of physiological states from contactless face video: a sparse representation approach
Qayyum, A., Mazher, M., Shuaibu, A. N., Benzinou, A., Malik, A. S., & Razzak, I. (2022) · Computing · doi:10.1007/s00607-021-01028-3 · Cited by 11
“The leading remote photoplethysmography (rPPG) approaches can be used in many systems for the video health assessment of factors such as mental stress [1,44], respiration [2], HR [2,3], blood pressure [4], and SpO2 [2] and detection of atrial fibrillation [5].”
MethodNon-Contact HR Extraction from Different Color Spaces Using RGB Camera
Panigrahi, A., & Sharma, H. K. (2022) · National Conference on Communications · doi:10.1109/ncc55593.2022.9806722 · Cited by 11
“Kumar et al. divided the facial region into sub-ROIs, and the iPPG signal is calculated as a weighted sum of the candidate pulse signals [10].”
Huber Kalman Filter for Wi-Fi Based Vehicle Driver's Respiration Detection
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Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems
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On the Feasibility of Pulse Wave Velocity Imaging for Remote Assessment of Physiological Functions
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Quasi Real-time Contactless Physiological Sensing Using Consumer-Grade Cameras
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“Signal-to-noise ratio (SNR) can also be used as an evaluation metric to select regions with the strongest pulse component [12, 13].”
MethodA Web Application for Experimenting and Validating Remote Measurement of Vital Signs
Ayesha, A. H., Qiao, D., & Zulkernine, F. H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21047-1_21 · Cited by 10
The Medical Internet of Things: applications in respiratory medicine
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Classification of physical exercise intensity by using facial expression analysis
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“Haque [19] presented a DistancePPG model which is a new method of combining skin-color signals from different tracked regions of the face using a weighted average.”
MethodHelical long period fiber grating sensor for non-invasive measurement of vital signs
Chen, W., Zhang, Y., Chen, Z., Zhang, H., Chen, X., Zhang, Y. X., Huang, S., Hu, X., & Zhang, X. (2023) · Optics & Laser Technology · doi:10.1016/j.optlastec.2023.109293 · Cited by 9
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“Kumar et al. [34] divided seven facial regions into 20 × 20 pixel blocks and analyzed the color change signal intensity obtained for each block.”
BackgroundResultMotion-tolerant heart rate estimation from face videos using derivative filter
Zhao, Y. S., Yang, X., & Wu, X. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07849-x · Cited by 9
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Performance Analysis for Human Crowd Monitoring to Control COVID-19 disease by Drone Surveillance
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A camera-based multispectral setup for remote vital signs assessment
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Camera-Based Respiration Monitoring of Unconstrained Rodents
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Sensitivity of Video-Based Pulse Arrival Time to Dynamic Blood Pressure Changes
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“contains a higher strength PPG signal [16].”
Ariffin, F. F. S., Kamarudin, L. M., Ghazali, N., Nishizaki, H., Zakaria, A., & Zakaria, S. M. M. S. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1755/1/012038 · Cited by 8
“Currently, the use of non-contact method for measuring vital signs has gained popularity due to the flexibility and non-invasive procedure [1].”
MethodDiverse R-PPG: Camera-Based Heart Rate Estimation for Diverse Subject Skin-Tones and ScenesPreprint
Chari, P., Kabra, K., Karinca, D., Lahiri, S., Srivastava, D., Kulkarni, K., Chen, T., Cannesson, M. P., Jalilian, L., & Kadambi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12769 · Cited by 12
In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos
Condrea, F., Ivan, V.-A., & Leordeanu, M. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00157 · Cited by 8
“Moreover, advancements in cameras and other devices, in combination with powerful vision and machine learning algorithms, prove that emerging smart medical technologies are able to provide measurements that meet or even surpass the traditional medical gold standards [1, 2].”
BackgroundTAMURA, T., & Maeda, Y. (2017) · Springer eBooks · doi:10.1007/978-3-319-69362-0_6 · Cited by 11
Anti-motion imaging photoplethysmography via self-adaptive multi-ROI tracking and selection
Duan, Y., He, C., & Zhou, M. (2023) · Physiological Measurement · doi:10.1088/1361-6579/ad071f · Cited by 7
Jalaja, A. A., & Kavitha, M. (2024) · Biotechnology and Bioengineering · doi:10.1002/bit.28644 · Cited by 7
“In contrast to previous state ‐ of ‐ the ‐ art methods that primarily measure a single sign, (Iuchi et al., 2020; Kumar et al., 2015; Massaroni et al., 2018) et al., 2020).”
BackgroundResultA Fast FPGA Hardware Accelerator for Remote Heart Rate Detection Based on RGB Vision
Hsu, J.-Y., Jiang, T.-Y., & Chao, P. C.-P. (2024) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2024.3354505 · Cited by 7
“Unfortunately, slow acquisition of HR is indeed a serious problem in a number of PPG applications, like heart failure pre-warning of an in-action athlete [13], [14], [15] and drowsiness detection of a driver [16].”
BackgroundIllumination Variation-Resistant Network for Heart Rate Measurement by Exploring RGB and MSR Spaces
Liu, L., Xia, Z., Zhang, X., Feng, X., & Zhao, G. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3432140 · Cited by 7
“Assuming that the regions of interest (ROIs) of the skin can be treated as a Lambertian radiator, some rPPG signal measurement methods that synchronously analyze multiple facial ROIs were also presented to eliminate the illumination artifacts [13], [32], [33], [34].”
BackgroundMethodAn Aircraft Pilot Workload Sensing System
Alaimo, A., Esposito, A., Milazzo, A., & Orlando, C. (2021) · Lecture notes in civil engineering · doi:10.1007/978-3-030-64594-6_85 · Cited by 9
Human physiology and contactless vital signs monitoring using camera and wireless signals
Wang, X., & Shao, D. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822281-2.00008-1 · Cited by 7
Video-based HR measurement using adaptive facial regions with multiple color spaces
Panigrahi, A., Sharma, H. K., & Mukherjee, A. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.12.001 · Cited by 7
Direct-Global Separation for Improved Imaging Photoplethysmography
Park, J., Sabharwal, A., & Veeraraghavan, A. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00186 · Cited by 7
“Over the last few years, significant improvements have been demonstrated in algorithms for camera-based remote photoplethysmography (rPPG) estimation [1, 2, 3, 4, 5, 6, 7, 8].”
BackgroundMethodPerformance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
Pirnar, Ž., Finžgar, M., & Podržaj, P. (2021) · Applied Sciences · doi:10.3390/app11083467 · Cited by 7
“divided into multiple ROIs (multiple ROIs would also enable measurements of additional physiological parameters [44]); and (3) in all approaches, lengths of the processing window lengths could be optimized.”
BackgroundA low-cost, camera-based continuous PPG monitoring system using Laplacian pyramid
Patil, O. R., Wang, W., Gao, Y., Xu, W., & Jin, Z. (2018) · Smart Health · doi:10.1016/j.smhl.2018.07.024 · Cited by 7
The Roberta IRONSIDE project : a cognitive and physical robot coach for dependent persons
Sansen, H., Chollet, G., Glackin, C., Jokinen, K., Badii, A., Torres, M. I., Petrovska‐Delacrétaz, D., Boudy, J., & Schlögl, S. (2016) · HAL · Cited by 7
Lee, K., Seo, H., Kim, S., An, B. S., Park, S., Jeon, Y., & Lee, E. C. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3504605 · Cited by 6
“…rPPG Compensation Compensated rPPG i = rPPG i 4: else 5: count = 0 ← the number of BPM ( top 10% ) within ± 10 seconds 6: for j = i − 10 , i − 9 ,... , i + 10 do 7: if if count > 0 then return 10: else 13: BPM snr = BPM with best SNR in BPM j 14: proposed by Nowara et al [18], [21], [22].”
Lai, M., Dicorato, C. S., de Wild, M., Verbakel, F., Shulepov, S. Y., Groen, J., Notten, M., Lucassen, G. W., van Sambeek, M. R. H. M., Hendriks, B. H. W., et al. (2021) · SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging · doi:10.1117/12.2580640 · Cited by 6
“Various algorithms have been proposed for contactless PPG measurements, where most of these aim to substitute contact PPG, measuring vital sign parameters.(13,14) To the best of our knowledge, this is the first work that evaluates iPPG for assessing PAD.”
BackgroundBenchmarking of Contactless Heart Rate Measurement Systems in ARM-Based Embedded Platforms
Manni, A., Caroppo, A., Rescio, G., Siciliano, P. A., & Leone, A. (2023) · Sensors · doi:10.3390/s23073507 · Cited by 6
Real-Time Visual Respiration Rate Estimation with Dynamic Scene Adaptation
Chatterjee, A., Prathosh, A. P., Praveena, P., & Upadhya, V. (2016) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2016.31 · Cited by 6
“Several attempts are made to overcome such limitations using signal processing and statistical modeling techniques such as blind source separation, alternative reflectance models, spatial pruning and temporal filtering [12], [13], [14], [15].”
MethodPerfusion Monitoring by Contactless Photoplethy Smography Imaging
Lai, M., Shan, C., Ciuhu, C., & Izamis, M. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759547 · Cited by 6
“Various algorithms have been proposed for contactless PPG measurements [9,10], but most of these work focus on, as substitute of contact PPG, measuring vital sign parameters.”
BackgroundCombining Magnification and Measurement for Non-Contact Cardiac Monitoring
Nowara, E. M., McDuff, D., & Veeraraghavan, A. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00422 · Cited by 6
“As the iPPG technology has matured, many approaches have achieved robustness to challenging motion [21, 22, 23, 24, 25, 26, 27], low light settings and varying illumination [28, 29, 30], and video compression [31, 32, 4, 3, 6].”
BackgroundLiu, X., Yang, X., Meng, Z., Wang, Y., Zhang, J., & Wong, A. K. C. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00270 · Cited by 6
“(3) Multi-region analysis [13] extracts color signals from different facial areas, followed by combining the color signals with high signal-noise ratios into the pulse signal.”
BackgroundWoyczyk, A., Fleischhauer, V., & Zaunseder, S. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00164 · Cited by 6
“Another approach de-fines the ROI using facial landmarks to increase the number of skin pixels, e.g. [ 11].”
MethodNon-contact Vital Signals Measurement on a Mobile Rescue Robot
Huang, R., Su, W., Jian, R., Li, S., Xie, P., Zhang, S., & Wei, Q. (2019) · International Conference on Data Science and Business Analytics (ICDSBA) · doi:10.1109/icdsba48748.2019.00096 · Cited by 6
“Face to be ROI for three reasons: 1) facial skin is usually exposed to camera, 2) the perfusion of blood in face is higher than other body parts, 3) RIIV clearly presents in PPG signals from the face [13].”
BackgroundQiu, Z., Lin, L., Sun, H., Liu, J., & Chen, Y. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-79161-2_11 · Cited by 6
High Sampling Rate Smartphone-PPG via Built-in Rolling Shutter Image Sensor
Rachim, V. P., Baek, J., Kim, Y. S., Kim, Y., & Park, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3201910 · Cited by 6
“Different from the LED-PD configuration, which is mostly utilized for wearable healthcare technologies, the LED-CIS configuration allows both contact and noncontact (measuring in distance) PPG signal acquisition that ultimately opens a whole new application of PPG-based healthcare system [11], [12].”
MethodSeeing beneath the skin with computational photography
Nowara, E. M., McDuff, D., Sabharwal, A., & Veeraraghavan, A. (2022) · Communications of the ACM · doi:10.1145/3536421 · Cited by 6
Phys-EdiGAN: A privacy-preserving method for editing physiological signals in facial videos
Tu, X., Niu, Z., Yin, J., Zhang, Y., Yang, M., Wei, L., Wang, Y., Fan, Z., & Zhao, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111966 · Cited by 5
A noncontact vital sign monitoring algorithm using a camera
Anusree, R., Ranjith, R. S., & Ramachandran, K. I. (2016) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2016.7732336 · Cited by 5
“[8] developed distance PPG algorithm, a motion tolerant and intensity compensated algorithm to determine pulse rate by using recorded video of a human face.”
MethodDynamic Heart Rate Measurements from Video Sequences using Canonical Component Analysis
Ling, S., Paramesran, R., & Yu, Y.-P. (2019) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2019.03005 · Cited by 5
Enhanced heart rate estimation based on face features
Ayed, M. B., Alshaya, S. A., & Alshammari, A. (2021) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd52085.2021.9429508 · Cited by 5
“Kumar et al. [17] proposed a method to reduce the signal-noise caused by the low lighting.”
MethodSystem for contactless monitoring of tissue perfusion
Procka, P., & Borik, Š. (2022) · ELEKTRO · doi:10.1109/elektro53996.2022.9803323 · Cited by 5
“Previous works [11] and [14] noted a more pronounced PPG wave formation in the green wavelength range.”
BackgroundLingqin, 孔. K., Yuejin, 赵. Z., Dong, L., Ming, 刘. L., Ge, 徐. X., Mei, 惠. H., & Xuhong, 褚. C. (2023) · Acta Optica Sinica · doi:10.3788/aos230755 · Cited by 5
Kviesis-Kipge, E., & Rubīns, U. (2016) · Biennial Baltic Electronics Conference (BEC) · doi:10.1109/bec.2016.7743727 · Cited by 5
“Remote photoplethysmography imaging (rPPG) is a noncontact optical diagnostic technique for detection of blood volume changes in skin using backscattered radiation [1-9].”
BackgroundImproving ROI detection in photoplethysmographic imaging with thermal cameras
Scebba, G., Dragas, J., Hu, S., & Karlen, W. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037803 · Cited by 5
“Furthermore, non-uniform spatial illumination from ambient light sources introduces noise to the PPG signal extraction [8].”
BackgroundColor Signal Processing Methods for Webcam-Based Heart Rate Evaluation
Kopeliovich, M. V., & Petrushan, M. V. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29513-4_53 · Cited by 5
Measuring Accuracy Towards Facial Video Heart-Rate Estimation Using Haar-Cascade and CNN Method
Akbar, N. A., Muneer, A., Taib, S. M., & Fati, S. M. (2022) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa54658.2022.9764992 · Cited by 5
A Multiresolution Method for Non-Contact Heart Rate Estimation Using Facial Video Frames
Das, M., Choudhary, T., Bhuyan, M. K., Sharma, L. N., & Dutta, H. (2022) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet54241.2022.9767186 · Cited by 5
Remote Bio Vision: Perfusion Imaging Based Non-Contact Autonomic Biosignal Measurement Algorithm
Kim, C.-I., Park, E.-B., Won, C., & Lee, J.-H. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3279328 · Cited by 5
“This study used the Shi–Tomasi method [19], which was”
MethodRobust Heart Rate Measurement by Adaptive ROI Selection for Head-Rotation Conditions
Fan, X., Liu, F., Huang, Z., Xue, W., Gao, T., Fu, J., & Zhang, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3323992 · Cited by 5
“A notable improvement is the recent works [22], [23], [24] used facial landmarks to segment the face into several preselected ROIs, and selected the ROI with the highest SNR at each time window length of 5–10 s.”
BackgroundMethodInfluentialHeart Rate Based Face Synthesis for Pulse Estimation
Çiftçi, U. A., & Yin, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33720-9_42 · Cited by 5
Ayala, L. A., Mîndroc-Filimon, D., Rees, M., Hübner, M., Sellner, J., Seidlitz, S., Tizabi, M. D., Wirkert, S., Seitel, A., & Maier‐Hein, L. (2024) · Scientific Data · doi:10.1038/s41597-024-03307-y · Cited by 5
Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals
Filho, I. J. S., Rahman, M. M. U., Laleg‐Kirati, T., & Al-Naffouri, T. Y. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.11.101 · Cited by 4
Doctor Robots: Design and Implementation of a Heart Rate Estimation Algorithm
Göngör, F., & Tutsoy, Ö. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00888-9 · Cited by 4
“have employed Kanade–Lucas–Tomasi (KLT) based tracking algorithm to extract related motion vectors [8].”
MethodJin, J., Lu, J. Q., Chen, C., Zhou, R., & Hu, X. (2022) · Journal of Biomedical Optics · doi:10.1117/1.jbo.27.11.116004 · Cited by 4
“SNR can be further enhanced by averaging over multiple ROIs of different weighs after frequency filtering.(16) Pixel averaging used in this type of algorithms, however, comes at the expense of reduced spatial resolution that diminishes the benefit of imaging.”
BackgroundAdaptive-Weight Network for Imaging Photoplethysmography Signal Extraction and Heart Rate Estimation
Liu, H., Ding, Y., Zhou, M., & Li, Q. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3212524 · Cited by 4
“Although Kumar et al. [16] and Tulyakov et al [17] flexibly employed the information in different face regions and their results showed a good performance, these methods depend on prior knowledge of the IPPG signals and noises, which cannot adaptively adjust for scenarios with different noises.”
BackgroundMethodNon - Contact Heart Rate Monitoring System using Deep Learning Techniques
Malini, A., G, S., Satesh Kumar, G., & G., S. (2023) · 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot56793.2023.10053467 · Cited by 3
“Kumar et al., [8] deals with the addition to obvious medical applications, monitoring a person's heart rate has a wide range of applications in domains such as sports, psychology, and more.”
BackgroundHeart rate estimation by leveraging static and dynamic region weights
Liu, L., Xia, Z., Zhang, X., Peng, J., Feng, X., & Zhao, G. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.4.043037 · Cited by 4
Remote photoplethysmography based on reflected light angle estimation
Fan, X., Liu, F., Zhang, J., Gao, T., Fan, Z., Huang, Z., Xue, W., & Zhang, J. (2024) · Physiological Measurement · doi:10.1088/1361-6579/ad2f5d · Cited by 4
Eaton, A., Vincely, V. D., Lloyd, P., Hugenberg, K., & Vishwanath, K. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2252629 · Cited by 4
“For video streams with faces, the ROI was always selected on the exposed forehead region, since several previous reports have indicated that the forehead was the most robust region on the human face for VPPG [1, 15, 16].”
BackgroundMethodInfluentialRemote Heart Rate Estimation Based on 3D Facial Landmarks
Maki, Y., Monno, Y., Tanaka, M., & Okutomi, M. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176563 · Cited by 4
“Furthermore, some methods divide the face ROI into face patches using the 2D landmarks and locally select suitable patches based on the reliability for each patch [7]–[10].”
MethodRespiratory Frequency Estimation Method Based on Periodic Features Using UWB Radar
Guo, B., Yang, Z., Cheng, Y., & Zhou, J. (2020) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict51264.2020.9334351 · Cited by 4
Touchscreens Can Reveal User Identity: Capacitive Plethysmogram-Based Biometrics
Wu, J., Ji, X., Lyu, Y., Luo, X., Meng, Y., Morales, E., Wang, D., & Luo, X. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3214846 · Cited by 4
“Although some remote sensing methods (e.g., rPPG [55], [56]) can measure the BVP from a distance, it is still difficult to reconstruct CPG dynamics.”
BackgroundMethodA Distributed Framework for Remote Multimodal Biosignal Acquisition and Analysis
Casado, C. Á., Räsänen, P., Nguyen, L. N., Lämsä, A., Peltola, J., & López, M. B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-59091-7_9 · Cited by 4
A Supervised Learning Approach for Robust Health Monitoring using Face Videos
Gupta, M., Chen, L., Yu, D., & Aggarwal, V. (2020) · doi:10.1145/3427772.3429392 · Cited by 4
“We were careful in retaining the forehead since it contained the maximum information about blood perfusion inside the arteries [5].”
BackgroundMethodInfluentialElectronic recording of vital signs for mental health inpatients
Gale‐Grant, O., & Quist, H. (2018) · British Journal of Mental Health Nursing · doi:10.12968/bjmh.2018.7.2.64 · Cited by 4
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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