A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Summary

Objectives: Camera-based vital sign estimation allows the contactless assessment of important physiological parameters. Seminal contributions were made in the 1930s, 1980s, and 2000s, and the speed of development seems ever increasing. In this suivey, we aim to overview the most recent works in this area, describe their common features as well as shortcomings, and highlight interesting “outliers”.

Methods: We performed a comprehensive literature research and quantitative analysis of papers published between 2016 and 2018. Quantitative information about the number of subjects, studies with healthy volunteers vs. pathological conditions, public datasets, laboratory vs. real-world works, types of camera, usage of machine learning, and spectral properties of data was extracted. Moreover, a qualitative analysis of illumination used and recent advantages in terms of algorithmic developments was also performed.

Results: Since 2016, 116 papers were published on camera-based vital sign estimation and 59% of papers presented results on 20 or fewer subjects. While the average number of participants increased from 15.7 in 2016 to 22.9 in 2018, the vast majority of papers (n=100) were on healthy subjects. Four public datasets were used in 10 publications. We found 27 papers whose application scenario could be considered a real-world use case, such as monitoring during exercise or driving. These include 16 papers that dealt with non-healthy subjects. The majority of papers (n=61) presented results based on visual, red-green-blue (RGB) information, followed by RGB combined with other parts of the electromagnetic spectrum (n=18), and thermography only (n=12), while other works (n=25) used other mono- or polychromatic non-RGB data. Surprisingly, a minority of publications (n=39) made use of consumer-grade equipment. Lighting conditions were primarily uncontrolled or ambient. While some works focused on specialized aspects such as the removal of vital sign information from video streams to protect privacy or the influence of video compression, most algorithmic developments were related to three areas: region of interest selection, tracking, or extraction of a one-dimensional signal. Seven papers used deep learning techniques, 17 papers used other machine learning approaches, and 92 made no explicit use of machine learning.

Conclusion: Although some general trends and frequent shortcomings are obvious, the spectrum of publications related to camera-based vital sign estimation is broad. While many creative solutions and unique approaches exist, the lack of standardization hinders comparability of these techniques and of their performance. We believe that sharing algorithms and/ or datasets will alleviate this and would allow the application of newer techniques such as deep learning.

Keywords

Photoplethysmography imaging - photoplethysmography - thermography - camera-based sensing - vital signs

^* Both authors contributed equally

• References

• 1 Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012; 9 (07) 671-5
• 2 Burke BE, Gregory JA, Cooper M, Loomis AH, Young DJ, Lind TA. , et al. CCD Imager Development for Astronomy. Lincoln Lab J 2007; 16 (02) 393-412
• 3 Goldfain A, Smith B, Arabandi S, Brochhausen M, Hogan WR. Vital Sign Ontology. In: Proceedings of the Workshop on Bio-Ontologies, ISMB, Vienna, June 2011; 2011. p. 71-4
• 4 Glaeser DH, Thomas LJ. Computer Monitoring in Patient Care. Annu Rev Biophys Bioeng 1975; 4 (01) 449-76
• 5 Hertzman AB. The blood supply of various skin areas as estimated by the photoelectric plethysmograph. Am J Physiol 1938; 124: 328-40
• 6 Severinghaus JW. Takuo Aoyagi: Discovery of Pulse Oximetry. Anesth Analg 2007; 105 (6 Suppl): S1-4
• 7 Matthes K. Untersuchungen über die Sauerstoffsättigung des menschlichen Arterienblutes [Studies on the oxygen saturation of human arterial blood]. Naunyn. Schmiedebergs. Arch Exp Pathol Pharmakol 1935; 179 (06) 698-711
• 8 Wu T, Blazek V, Schmitt HJ. Photoplethysmography Imaging: A New Noninvasive and Non-contact Method for Mapping of the Dermal Perfusion Changes. In: Optical Techniques and Instrumentation for the Measurement of Blood Composition, Structure, and Dynamics 2000 4163. 62-71
• 9 Murthy R, Pavlidis I, Tsiamyrtzis P. Touchless monitoring of breathing function. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2004 3. 1196-9
• 10 Garbey M, Sun N, Merla A, Pavlidis I. Contact-Free Measurement of Cardiac Pulse Based on the Analysis of Thermal Imagery. IEEE Trans Biomed Eng 2007; 54 (08) 1418-26
• 11 NeffTA. Routine Oximetry. Chest 1988; 94 (02) 227
• 12 Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 2007; 28 (03) R1-R39
• 13 Wieringa FP, Mastik F, van der Steen AFW. Contactless Multiple Wavelength Photoplethys-mographic Imaging: A First Step Toward ’spO2 Camera’ Technology. Ann Biomed Eng 2005; 33 (08) 1034-41
• 14 Balakrishnan G, Durand F, Guttag J. Detecting Pulse from Head Motions in Video. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition; 2013. p. 3430-7
• 15 Leonhardt S, Leicht L, Teichmann D. Unobtrusive Vital Sign Monitoring in Automotive Environments–A Review. Sensors 2018; 18 (09) 3080
• 16 McDuff DJ, Estepp JR, Piasecki AM, Blackford EB. A survey of remote optical photoplethysmo-graphic imaging methods. In: Proc of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2015. p. 6398-404
• 17 Sun Y, Thakor N. Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging. IEEE Trans Biomed Eng 2016; 63 (03) 463-77
• 18 Sikdar A, Behera SK, Dogra DP. Computer-Vision-Guided Human Pulse Rate Estimation: A Review. IEEE Rev Biomed Eng 2016; 9: 91-105
• 19 Rouast PV, Adam MTP, Chiong R, Cornforth D, Lux E. Remote heart rate measurement using low-cost RGB face video: a technical literature review. Front Comput Sci 2018; 12 (05) 858-72
• 20 Zaunseder S, Trumpp A, Wedekind D, Malberg H. Cardiovascular assessment by imaging photoplethysmography - a review. Biomed Tech (Berl) 2018; 63 (05) 617-34
• 21 Rasche S, Trumpp A, Waldow T, Gaetjen F, Plötze K, Wedekind D. , et al. Camera-based photoplethysmography in critical care patients. Clin Hemorheol Microcirc 2016; 64 (01) 77-90
• 22 Trumpp A, Rasche S, Wedekind D, Rudolf M, Malberg H, Matschke K. , et al. Relation between pulse pressure and the pulsation strength in camera-based photoplethysmograms. Curr Dir Biomed Eng 2017; 3 (02) 489-92
• 23 Elphick HE, Alkali AH, Kingshott RK, Burke D, Saatchi R. Exploratory Study to Evaluate Respiratory Rate Using a Thermal Imaging Camera. Respiration 2019; 97 (03) 205-12
• 24 Fan Q, Li K. Non-contact remote estimation of cardiovascular parameters. Biomed Signal Process Control 2018; 40: 192-203
• 25 Cerina L, Iozzia L, Mainardi L. Influence of acquisition frame-rate and video compression techniques on pulse-rate variability estimation from vPPG signal. Biomed Tech (Berl) 2017; 64 (01) 53-65
• 26 Moco AV, Stuijk S, de Haan G. Ballistocardiographic Artifacts in PPG Imaging. IEEE Trans Biomed Eng 2016; 63 (09) 1804-11
• 27 Bobbia S, Macwan R, Benezeth Y, Mansouri A, Dubois J. Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recognit Lett 2017; 1-9
• 28 Bobbia S, Luguern D, Benezeth Y, Nakamura K, Gomez R, Dubois J. Real-Time Temporal Superpixels for Unsupervised Remote Photoplethysmography. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2018. p. 1341-8
• 29 Bobbia S, Benezeth Y, Dubois J. Remote photoplethysmography based on implicit living skin tissue segmentation. In: Proc of the 23rd International Conference on Pattern Recognition (ICPR): 2016. p. 361-5
• 30 Macwan R, Benezeth Y, Mansouri A. Remote photoplethysmography with constrained ICA using periodicity and chrominance constraints. Biomed Eng Online 2018; 17 (01) 22
• 31 Zhang Z, Girard JM, Wu Y, Zhang X, Liu P, Ciftci U. , et al. Multimodal spontaneous emotion corpus for human behavior analysis. In: Proc of the IEEE Conference on Computer Vision and Pattern Recognition (CPVR); 2016. p. 3438-46
• 32 Tulyakov S, Alameda-Pineda X, Ricci E, Yin L, Cohn JF, Sebe N. Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions. In: Proc of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. p. 2396-404
• 33 Soleymani M, Lichtenauer J, Pun T, Pantic M. A Multimodal Database for Affect Recognition and Implicit Tagging. IEEE Trans Affect Comput 2012; 3 (01) 42-55
• 34 Hassan MA, Malik AS, Fofi D, Saad NM, Ali YS, Meriaudeau F. Video-Based Heartbeat Rate Measuring Method Using Ballistocardiography. IEEE Sens J 2017; 17 (14) 4544-57
• 35 Hassan MA, Malik AS, Fofi D, Saad N, Meriaudeau F. Novel health monitoring method using an RGB camera. Biomed Opt Express 2017; 8 (11) 4838
• 36 Koelstra S, Mühl C, Soleymani M, Lee JS, Yazdani A, Ebrahimi T, Paet AL. DEAP: A Database for Emotion Analysis ;Using Physiological Signals. IEEE Trans Affect Comput 2012; 3 (01) 18-31
• 37 Unakafov AM. Pulse rate estimation using imaging photoplethysmography: generic framework and comparison of methods on a publicly available dataset. Biomed Phys Eng Express 2018; 4 (04) 045001
• 38 Qi H, Guo Z, Chen X, Shen Z, Wang JZ. Video-based human heart rate measurement using joint blind source separation. Biomed Signal Process Control 2017; 31: 309-20
• 39 Trumpp A, Lohr J, Wedekind D, Schmidt M, Burghardt M, Heller AR. , et al. Camera-based photoplethysmography in an intraoperative setting. Biomed Eng Online 2018; 17 (01) 33
• 40 Wedekind D, Trumpp A, Gaetjen F, Rasche S, Matschke K, Malberg H. , et al. Assessment of blind source separation techniques for video-based cardiac pulse extraction. J Biomed Opt 2017; 22 (03) 035002
• 41 Villarroel M, Jorge J, Pugh C, Tarassenko L. Non-Contact Vital Sign Monitoring in the Clinic. In: Proc ofthe 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017); 2017. p. 278-85
• 42 Hochhausen N, Barbosa Pereira C, Leonhardt S, Rossaint R, Czaplik M. Estimating Respiratory Rate in Post-Anesthesia Care Unit Patients Using Infrared Thermography: An Observational Study. Sensors 2018; 18 (05) 1618
• 43 Kamshilin AA, Volynsky MA, Khayrutdinova O, Nurkhametova D, Babayan L, Amelin AV. , et al. Novel capsaicin-induced parameters of microcirculation in migraine patients revealed by imaging photoplethysmography. J Headache Pain 2018; 19 (01) 43
• 44 Rubins U, Splgulis J, Miscuks A. Photoplethysmography imaging algorithm for continuous monitoring of regional anesthesia. In: Proceedings of the 14th ACM/IEEE Symposium on Embedded Systems for Real-Time Multimedia - ESTIMe- dia’16; 2016. p. 67-71
• 45 Jorge J, Villarroel M, Chaichulee S, Guazzi A, Davis S, Green G. , et al. Non-Contact Monitoring of Respiration in the Neonatal Intensive Care Unit. In: Proc 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017); 2016. p. 286-93
• 46 Blanik N, Heimann K, Pereira C, Paul M, Blazek V, Venema B. , et al. Remote vital parameter monitoring in neonatology - robust, unobtrusive heart rate detection in a realistic clinical scenario. Biomed Tech (Berl) 2016; 61 (06) 631-43
• 47 Chaichulee S, Villarroel M, Jorge J, Arteta C, Green G, McCormick K. , et al. Localised photoplethysmography imaging for heart rate estimation of pre-term infants in the clinic. In: Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics. International Society for Optics and Photonics; 2018. p. 105010R
• 48 van Gastel M, Balmaekers B, Verkruysse W, Bambang Oetomo S. Near-continuous non-contact cardiac pulse monitoring in a neonatal intensive care unit in near darkness. In: Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics. International Society for Optics and Photonics; 2018. p. 1050114
• 49 Wang W, den Brinker AC, de Haan G. Full video pulse extraction. Biomed Opt Express 2018; 9 (08) 3898
• 50 Chaichulee S, Villarroel M, Jorge J, Arteta C, Green G, McCormick K. , et al. Multi-Task Convolutional Neural Network for Patient Detection and Skin Segmentation in Continuous Non-Contact Vital Sign Monitoring. In: Proc of the 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017); 2017. p. 266-72
• 51 Wang W, Balmaekers B, de Haan G. Quality metric for camera-based pulse rate monitoring in fitness exercise. In: Proc of IEEE International Conference on Image Processing (ICIP); 2016. p. 2430-4
• 52 Wang W, den Brinker AC, Stuijk S, de Haan G. Color-Distortion Filtering for Remote Photoplethysmography. In: Proc of the 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017); 2017. p. 71-8
• 53 Wang W, den Brinker AC, Stuijk S, de Haan G. Robust heart rate from fitness videos. Physiol Meas 2017; 38 (06) 1023-44
• 54 Wang W, den Brinker AC, Stuijk S, de Haan G. Amplitude-selective filtering for remote-PPG. Biomed Opt Express 2017; 8 (03) 1965
• 55 Chauvin R, Hamel M, Briere S, Ferland F, Grondin F, Letourneau D. , et al. Contact-Free Respiration Rate Monitoring Using a Pan-Tilt Thermal Camera for Stationary Bike Telerehabilitation Sessions. IEEE Syst J 2016; 10 (03) 1046-55
• 56 Capraro G, Etebari C, Luchette K, Mercurio L, Merck D, Kirenko I. , et al. No Touch’ Vitals: A Pilot Study of Non-contact Vital Signs Acquisition in Exercising Volunteers. In: Proc of the IEEE Biomedical Circuits and Systems Conference (BioCAS); 2018. p. 1-4
• 57 Blocher T, Schneider J, Schinle M, Stork W. An online PPGI approach for camera based heart rate monitoring using beat-to-beat detection. In: Proc of the IEEE Sensors Applications Symposium (SAS): 2017. p. 1-6
• 58 Zhang Q, Wu Q, Zhou Y, Wu X, Ou Y, Zhou H. Webcam-based, non-contact, real-time measurement for the physiological parameters of drivers. Measurement 2017; 100: 311-21
• 59 Wu BF, Huang PW, Lin CH, Chung ML, Tsou TY, Wu YL. Motion Resistant Image-Photoplethysmography Based on Spectral Peak Tracking Algorithm. IEEE Access 2018; 6: 21621-34
• 60 Nowara EM, Marks TK, Mansour H, van Veeraragha A. SparsePPG: Towards Driver Monitoring Using Camera-Based Vital Signs Estimation in Near-Infrared. In: Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2018. p. 1385-94
• 61 Spicher N, Kukuk M, Maderwald S, Ladd ME. Initial evaluation of prospective cardiac triggering using photoplethysmography signals recorded with a video camera compared to pulse oximetry and electrocardiography at 7T MRI. Biomed Eng Online 2016; 15 (01) 126
• 62 Moco AV, Stuijk S, de Haan G. Skin inhomogeneity as a source of error in remote PPG-imaging. Biomed Opt Express 2016; 7 (11) 4718
• 63 Moco AV, Stuijk S, de Haan G. New insights into the origin of remote PPG signals in visible light and infrared. Sci Rep 2018; 8 (01) 8501
• 64 Addison PS, Jacquel D, Foo DMH, Borg UR. Video-based heart rate monitoring across a range of skin pigmentations during an acute hypoxic challenge. J Clin Monit Comput 2018; 32 (05) 871-80
• 65 Moco AV, Stuijk S, de Haan G. Motion robust PPG-imaging through color channel mapping. Biomed Opt Express 2016; 7 (05) 1737
• 66 Butle MJ, Crowe JA, Hayes-Gill BR, Rodmell PI. Motion limitations of non-contact photoplethysmography due to the optical and topological properties of skin. Physiol Meas 2016; 37 (05) N27-N37
• 67 Sidorov IS, Romashko RV, Koval VT, Giniatullin R, Kamshilin AA. Origin of Infrared Light Modulation in Reflectance-Mode Photoplethysmography. PLoS One 2016; 11 (10) e0165413
• 68 Trumpp A, Schell J, Malberg H, Zaunseder S. Vasomotor assessment by camera-based photoplethysmography. Curr Dir Biomed Eng 2016; 2 (01) 199-202
• 69 Zaytsev VV, Miridonov SV, Mamontov OV, Kamshilin AA. Contactless monitoring of the blood-flow changes in upper limbs. Biomed Opt Express 2018; 9 (11) 5387
• 70 Moco AV, Mondragon LZ, Wang W, Stuijk S, de Haan G. Camera-based assessment of arterial stiffness and wave reflection parameters from neck micro-motion. Physiol Meas 2017; 38 (08) 1576-98
• 71 McDuff DJ, Blackford EB, Estepp JR. Fusing Partial Camera Signals for Noncontact Pulse Rate Variability Measurement. IEEE Trans Biomed Eng 2018; 65 (08) 1725-39
• 72 Cheng J, Chen X, Xu L, Wang ZJ. Illumination Variation-Resistant Video-Based Heart Rate Measurement Using Joint Blind Source Separation and Ensemble Empirical Mode Decomposition. IEEE J Biomed Health Inform 2017; 21 (05) 1422-33
• 73 Zhang G, Shan C, Kirenko I, Long X, Aarts R. Hybrid Optical Unobtrusive Blood Pressure Measurements. Sensors 2017; 17 (07) 1541
• 74 Kwasniewska A, Ruminski J, Szankin M, Czuszynski K. Remote Estimation of Video-Based Vital Signs in Emotion Invocation Studies. Conf Proc IEEE Eng Med Biol Soc 2018; 4872-6
• 75 Massaroni C, Schena E, Silvestri S, Taffoni F, Merone M. Measurement system based on RBG camera signal for contactless breathing pattern and respiratory rate monitoring. In: Proc of the IEEE International Symposium on Medical Measurements and Applications (MeMeA); 2018. p. 1-6
• 76 Alghoul K, Alharthi S, Al Osman H, El Saddik A. Heart Rate Variability Extraction From Videos Signals: ICA vs. EVM Comparison. IEEE Access 2017; 5: 4711-9
• 77 He X, Goubran R, Knoefel F. IR night vision video-based estimation of heart and respiration rates. In: Proc of the IEEE Sensors Applications Symposium (SAS); 2017. p. 1-5
• 78 Blackford EB, Estepp JR. Using consumer-grade devices for multi-imager non-contact imaging photoplethysmography. In: Optical Diagnostics and Sensing XVII: Toward Point-of-Care Diagnostics 2017;10072:100720P
• 79 Al-Naji A, Chahl J. Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal. IEEE J Transl Eng Health Med 2017; 5: 1-10
• 80 Prochazka A, Charvatova H, Vysata O, Kopal J, Chambers J. Breathing Analysis Using Thermal and Depth Imaging Camera Video Records. Sensors 2017; 17 (06) 1408
• 81 Lewis GF, Davila MI, Porges SW. Novel Algorithms to Monitor Continuous Cardiac Activity with a Video Camera. In: Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2018. p. 1395-403
• 82 Gambi E, Ricciuti M, Spinsante S. Sensitivity of the Contactless Videoplethysmography-Based Heart Rate Detection to Different Measurement Conditions. In: Proc of the 26th European Signal Processing Conference (EUSIPCO) 2018; 2018. p. 767-71
• 83 Al-Naji A, Perera AG, Chahl J. Remote monitoring of cardiorespiratory signals from a hovering unmanned aerial vehicle. Biomed Eng Online 2017; 16 (01) 101
• 84 Szankin M, Kwasniewska A, Sirlapu T, Wang M, Ruminski J, Nicolas R. , et al. Long Distance Vital Signs Monitoring with Person Identification for Smart Home Solutions. Conf Proc IEEE Eng Med Biol Soc 2018: 1558-61
• 85 Blackford EB, Piasecki AM, ad Estepp JR. Measuring pulse rate variability using long-range, non-contact imaging photoplethysmography. Conf Proc IEEE Eng Med Biol Soc 2016; 2016: 3930-6
• 86 Blackford EB, Estepp JR, Piasecki AM, Bowers MA, Klosterman SL. Long-range non-contact imaging photoplethysmography: cardiac pulse wave sensing at a distance. In: Optical Diagnostics and Sensing XVII: Toward Point-of-Care Diagnostics 2016;9715: 971512
• 87 Jeong I, Finkelstein J. Introducing Contactless Blood Pressure Assessment Using a High Speed Video Camera. J Med Syst 2016; 40 (04) 77
• 88 Wei B, He X, Zhang C, Wu X. Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions. Biomed Eng Online 2017; 16 (01) 17
• 89 Huang RY, Dung LR. Measurement of heart rate variability using off-the-shelf smart phones. Biomed Eng Online 2016; 15 (01) 11
• 90 Nam Y, Kong Y, Reyes B, Reljin N, Chon KH. Monitoring of Heart and Breathing Rates Using Dual Cameras on a Smartphone. PLoS One 2016; 11 (03) e0151013
• 91 Poh MZ, Poh YC. Validation of a Standalone Smartphone Application for Measuring Heart Rate Using Imaging Photoplethysmography. Telemed J E Health 2017; 23 (08) 678-83
• 92 Barbosa Pereira C, Yu X, Czaplik M, Blazek V, Venema B, Leonhardt S. Estimation of breathing rate in thermal imaging videos: a pilot study on healthy human subjects. J Clin Monit Comput 2017; 31 (06) 1241-54
• 93 Cho Y, Julier SJ, Marquardt N, Bianchi-Berthouze N. Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging. Biomed Opt Express 2017; 8 (10) 4480-503
• 94 Cho Y, Bianchi-Berthouze N, Julier SJ. Deep Breath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. In: Proc of the Seventh International Conference on Affective Computing and Intelligent Interaction (ACII) 2017; 2018, 456-63
• 95 Wang W, Stuijk S, de Haan G. A Novel Algorithm for Remote Photoplethysmography: Spatial Subspace Rotation. IEEE Trans Biomed Eng 2016; 63 (09) 1974-84
• 96 Blackford EB, Estepp JR. A multispectral testbed for cardiovascular sensing using imaging photoplethysmography. In: Optical Diagnostics and Sensing XVII: Toward Point-of-Care Diagnostics 2017;10072:100720R
• 97 Iakovlev D, Dwyer V, Hu S, Silberschmidt V. Noncontact blood perfusion mapping in clinical applications. In: Biophotonics: Photonic Solutions for Better Health Care V 2016:988: 988712
• 98 Trumpp A, Bauer PL, Rasche S, Malberg H, Zaunseder S. The value of polarization in camera-based photoplethysmography. Biomed. Opt. Express, vol. 8, no. 6, p. 2822, Jun. 2017
• 99 Li MH, Yadollahi A, Taati B. Noncontact Vision-Based Cardiopulmonary Monitoring in Different Sleeping Positions. IEEE J Biomed Health Inform 2017; 21 (05) 1367-75
• 100 Amelard R, Clausi DA, Wong A. Spectral-spatial fusion model for robust blood pulse waveform extraction in photoplethysmographic imaging. Biomed Opt Express 2016; 7 (12) 4874
• 101 Amelard R, Clausi DA, Wong A. Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems. J Biomed Opt 2016; 21 (11) 116010
• 102 Wang W, Stuijk S, de Haan G. Living-Skin Classification via Remote-PPG. IEEE Trans Biomed Eng 2017; 64 (12) 2781-92
• 103 Amelard R, Hughson RL, Greaves DK, Clausi DA, Wong A. Assessing photoplethysmographic imaging performance beyond facial perfusion analysis. In: Optical Diagnostics and Sensing XVII: Toward Point-of-Care Diagnostics 2017;10072:100720Q
• 104 van Gastel M, Stuijk S, de Haan G. Robust respiration detection from remote photoplethysmography. Biomed Opt Express 2016; 7 (12) 4941
• 105 Lahiri BB, Bagavathiappan S, Jayakumar T, Philip J. Medical applications of infrared thermography: A review. Infrared Phys Technol 2012; 55 (04) 221-35
• 106 Hamedani K, Bahmani Z, Mohammadian A. Spatio-temporal filtering of thermal video sequences for heart rate estimation. Expert Syst Appl 2016; 54: 88-94
• 107 Basu A, Routray A, Mukherjee R, Shit S. Infrared imaging based hyperventilation monitoring through respiration rate estimation. Infrared Phys Technol 2016; 77: 382-90
• 108 Mutlu K, Rabell JE, del Olmo PM, Haesler S. IR thermography-based monitoring of respiration phase without image segmentation. J Neurosci Methods 2018; 301: 1-8
• 109 Barbosa Pereira C, Czaplik M, Blazek V, Leonhardt S, Teichmann D. Monitoring of Cardiorespiratory Signals Using Thermal Imaging: A Pilot Study on Healthy Human Subjects. Sensors 2018; 18 (05) 1541
• 110 Verkruysse W, Bartula M, Bresch E, Rocque M, Meftah M, Kirenko I. Calibration of Contactless Pulse Oximetry. Anesth Analg 2017; 124 (01) 136-45
• 111 van Gastel M, Stuijk S, de Haan G. New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring. Sci Rep 2016; 6: 38609
• 112 Vogels T, van Gastel M, Wang W, de Haan G. Fully-Automatic Camera-Based Pulse-Oximetry During Sleep. In: Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2018. p. 1462-70
• 113 Shao D, Liu C, Tsow F, Yang Y, Du Z, Iriya R. , et al. Noncontact Monitoring of Blood Oxygen Saturation Using Camera and Dual-Wavelength Imaging System. IEEE Trans Biomed Eng 2016; 63 (06) 1091-8
• 114 Wurtenberger F, Haist T, Reichert C, Faulhaber A, Bottcher T, Herkommer A. Optimum wavelengths in the near infrared for imaging photoplethysmography. IEEE Trans Biomed Eng 2019; 9294
• 115 Rapczynski M, Zhang C, Al-Hamadi A, Notni G. A Multi-Spectral Database for NIR Heart Rate Estimation. In: Proc of the 25^th IEEE International Conference on Image Processing (ICIP); 2018. p. 2022-6
• 116 Scebba G, Dragas J, Hu S, Karlen W. Improving ROI detection in photoplethysmographic imaging with thermal cameras. In: Proc of the 39^th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2017. p. 4285-8
• 117 Negishi T, Sun G, Liu H, Sato S, Matsui T, Kirimoto T. Stable Contactless Sensing of Vital Signs Using RGB-Thermal Image Fusion System with Facial Tracking for Infection Screening. In: Proc of the 40^th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2018; 2018, 4371-4
• 118 Gupta O, McDuff D, Raskar R. Real-Time Physiological Measurement and Visualization Using a Synchronized Multi-camera System. In: Proc of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2016. p. 312-9
• 119 Hu MH, Zhai GT, Li D, Fan YZ, Chen XH, Yang XK. Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement. J Biomed Opt 2017; 22 (03) 036006
• 120 Blazek V, Blanik N, Blazek CR, Paul M, Pereira C, Koeny M, Venema B, Leonhardt S. Active and Passive Optical Imaging Modality for Unobtrusive Cardiorespiratory Monitoring and Facial Expression Assessment. Anesth Analg 2017; 124 (01) 104-19
• 121 Chwyl B, Chung AG, Amelard R, Deglint J, Clausi DA, Wong A. SAPPHIRE: Stochastically acquired photoplethysmogram for heart rate inference in realistic environments. In: Proc of the IEEE International Conference on Image Processing (ICIP) 2016 2016. 1230-4
• 122 Lin KY, Chen DY, Tsai WJ. Image-Based Motion-Tolerant Remote Respiratory Rate Evaluation. IEEE Sens J 2016; 16 (09) 3263-71
• 123 Fei J, Pavlidis I. Virtual Thermistor. Conf Proc IEEE Eng Med Biol Soc 2007; 2007: 250-3
• 124 Wang W, den Brinker AC, Stuijk S, de Haan. Algorithmic Principles of Remote PPG. IEEE Trans Biomed Eng 2017; 64 (07) 1479-91
• 125 Demirezen H, Erdem CE. Remote Photoplethysmography Using Nonlinear Mode Decomposition. In: Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018; 2018: p. 1060-4
• 126 Liu X, Yang X, Jin J, Li J. Self-adaptive signal separation for non-contact heart rate estimation from facial video in realistic environments. Physiol Meas 2018; 39 (06) 06NT01
• 127 Hanfland S, Paul M. Video Format Dependency of PPGI Signals. In: Proc ofthe International Conference on Electrical Engineering; 2016. p. 1-6
• 128 Zhao C, Lin C, Chen W, Li Z. A Novel Framework for Remote Photoplethysmography Pulse Extraction on Compressed Videos. In: Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW): 2018. p. 1412-21
• 129 McDuff DJ, Blackford EB, Estepp JR. The Impact of Video Compression on Remote Cardiac Pulse Measurement Using Imaging Photoplethysmography. In: Proc of the 12^th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017); 2017. p. 63-70
• 130 He X, Goubran RA, Liu XP. Wrist pulse measurement and analysis using Eulerian video magnification. In: 2016 Proc of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI); 2016. p. 41-4
• 131 Fallet S, Schoenenberger Y, Martin L, Braun F, Moser V, Vesin JM. Imaging Photoplethysmography: a Real-time Signal Quality Index. Comput Cardiol 2017; 44: 1-4
• 132 Sugita N, Yoshizawa M, Abe M, Tanaka A, Homma N, Yambe T. Contactless Technique for Measuring Blood-Pressure Variability from One Region in Video Plethysmography. J Med Biol Eng 2019; 39 (01) 76-85
• 133 Kamshilin AA, Sidorov IS, Babayan L, Volynsky MA, Giniatullin R, Mamontov OV. Accurate measurement of the pulse wave delay with imaging photoplethysmography. Biomed Opt Express 2016; 7 (12) 5138
• 134 Amelard R, Hughson RL, Greaves DK, Pfisterer KJ, Leung J, Clausi DA. , et al. Non-contact hemodynamic imaging reveals the jugular venous pulse waveform. Sci Rep 2017; 7: 40150
• 135 van Gastel M, Liang H, Stuijk S, de Haan G. Simultaneous estimation of arterial and venous oxygen saturation using a camera. In: Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics, 2018, Vol. 10501. p. 105010W
• 136 Ghanadian H, Ghodratigohar M, Al Osman G. A Machine Learning Method to Improve Non-Contact Heart Rate Monitoring Using an RGB Camera. IEEE Access 2018; 6: 57085-94
• 137 Pursche T, Claus R, Tibken B, Moller R. Using neural networks to enhance the quality of ROIs for video based remote heart rate measurement from human faces. In: Proc of the IEEE International Conference on Consumer Electronics (ICCE); 2019. p. 1-5
• 138 Qiu Y, Liu Y, Arteaga-Falconi J, Dong H, El Saddik A. EVM-CNN: Real-Time Contactless Heart Rate Estimation from Facial Video. IEEE Trans Multimed 2019; 9210: 1-1
• 139 Dagdanpurev S, Sun G, Choimaa L, Abe S, Matsui T. Clinical Application of Multiple Vital Signs-Based Infection Screening System in a Mongolian Hospital: Optimization of Facial Temperature Measurement by Thermography at Various Ambient Temperature Conditions Using Linear Regression Analysis. In: 2018 Proc ofthe 40^th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2018 2018. 5313-6
• 140 Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M. , et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
• 141 Chen W, Picard RW. Eliminating Physiological Information from Facial Videos. In: Proc of the 12^th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) 2017;21(03):48-55
• 142 Wang W, Den Brinker AC, De Haan G. Single Element Remote-PPG. IEEE Trans Biomed Eng 2016. (Early Access)
• 143 Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP. , et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 2009; 62 (10) e1-e34
• 144 Greenspan H, van Ginneken B, Summers RM. Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Trans Med Imaging 2016; 35 (05) 1153-9
• 145 Shokri R, Shmatikov V. Privacy-preserving deep learning. In: 2015 Proc of the 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton); 2015, 909-10